Applications of Artificial Intelligence in Mining and Geotechnical Engineering 9780443187643

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Applications of Artificial Intelligence in Mining and Geotechnical Engineering

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Applications of Artificial Intelligence in Mining and Geotechnical Engineering Edited by

Dr. Hoang Nguyen Hanoi University of Mining and Geology, Hanoi, Viet Nam

Prof. Xuan-Nam Bui Hanoi University of Mining and Geology, Hanoi, Viet Nam

Prof. Erkan Topal Mining Engineering, WA School of Mines, Faculty of Science and Engineering, Curtin University, Bentley, WA, Australia

Assoc. Prof. Jian Zhou School of Resources and Safety Engineering, Central South University, Changsha, China

Prof. Yosoon Choi Pukyong National University, Busan, Republic of Korea

Prof. Wengang Zhang School of Civil Engineering, Chongqing University, Chongqing, China

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2024 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-443-18764-3 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

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Contents Contributors Editors’ biography Preface

1.

xvii xxiii xxvii

The role of artificial intelligence in smart mining Yosoon Choi and Hoang Nguyen 1. 2. 3. 4.

2.

Industry 4.0 and smart mining Implementation levels of a smart mining site Role of artificial intelligence in smart mining Future perspectives Acknowledgments References

1 2 4 5 5 5

Application of artificial neural networks and UAV-based air quality monitoring sensors for simulating dust emission in quarries Long Quoc Nguyen, Luyen K. Bui, Cuong Xuan Cao, Xuan-Nam Bui, Hoang Nguyen, Van-Duc Nguyen, Chang Woo Lee, and Dieu Tien Bui 1.

2.

3. 4.

5. 6.

Introduction 1.1 Motivations 1.2 Related works 1.3 Contributions Proposed UMS-AM system 2.1 UAV platform 2.2 Sensor networks Study site Data monitoring measurement and methodology 4.1 Air quality monitoring measurement 4.2 Multilayer perception neural network Results Conclusions Conflicts of Interest References

7 7 8 9 9 9 10 14 15 15 16 17 21 21 21

v

vi Contents

3.

Application of machine learning and metaheuristic algorithms for predicting dust emission (PM2.5) induced by drilling operations in open-pit mines Xuan-Nam Bui, Hoang Nguyen, Yosoon Choi, Erkan Topal, and Tuan-Ngoc Le 1. 2.

3. 4. 5.

4.

Introduction Methodology 2.1 Gradient boosting machine (GBM) 2.2 Differential evolution (DE) algorithm 2.3 Particle swarm optimization (PSO) 2.4 Integration of DE, PSO and GBM model 2.5 Performance metrics for evaluation Data acquisition and preparation Results and discussion Conclusion Acknowledgments References

23 25 25 27 28 30 33 33 36 40 41 41

Deep neural networks for the estimation of granite materials’ compressive strength using non-destructive indices Danial Jahed Armaghani, Athanasia D. Skentou, Mehdi Izadpanah, Maria Karoglou, Manoj Khandelwal, Gerasimos Konstantakatos, Anna Mamou, Markos Z. Tsoukalas, Basak Zengin, and Panagiotis G. Asteris 1. 2. 3.

4.

5. 6.

Introduction Granite materials through history—A short overview Materials and methods 3.1 Artificial neural networks 3.2 Experimental database 3.3 Performance indexes Results and discussion 4.1 Splitting of database datasets 4.2 Hyperparameters of ANN models used in this study 4.3 Assessment of the trained and developed ANN models 4.4 Prediction accuracy comparisons Limitations and future works Conclusions References

45 47 49 49 52 53 55 55 60 60 64 67 67 68

Contents vii

5.

Estimating the Cd2+ adsorption efficiency on nanotubular halloysites in weathered pegmatites using optimized artificial neural networks: Insights into predictive model development Mark A. Engle, Hoang-Bac Bui, and Hoa Anh Nguyen 1. 2. 3. 4.

5. 6. 7. 8.

6.

Introduction Materials description Artificial neural network Optimization algorithms used 4.1 Slime mold algorithm 4.2 Particle swarm optimization 4.3 Differential evolution Framework of optimized artificial neural networks Estimation of Cd2+ adsorption efficiency of halloysite Discussion Conclusion Acknowledgments References

75 77 80 81 81 82 83 84 84 85 92 93 93

Application of artificial intelligence in predicting slope stability in open-pit mines: A case study with a novel imperialist competitive algorithm-based radial basis function neural network Hoang Nguyen, Xuan-Nam Bui, Yosoon Choi, and Erkan Topal 1. 2.

3.

4. 5.

Introduction Methodology 2.1 Radial basis function neural network (RBFNN) 2.2 Imperialist competitive algorithm (ICA) 2.3 Proposing the ICA-RBFNN model 2.4 Model assessment metrics Application 3.1 Data preparation 3.2 Model development Results and discussion Conclusion Acknowledgments References

97 98 98 99 99 100 101 101 102 104 109 109 109

viii Contents

7.

Application of cubist algorithm, multi-layer perceptron neural network, and metaheuristic algorithms to estimate the ore production of truck-haulage systems in open-pit mines Sebeom Park, Yosoon Choi, Hoang Nguyen, Erkan Topal, and Xuan-Nam Bui 1. 2. 3.

4. 5.

8.

Introduction Dataset used Methodology 3.1 Selection of input variables using the cubist algorithm 3.2 Multi-layer perceptron neural network 3.3 Metaheuristic algorithm for optimizing the multi-layer perceptron Results and discussions Conclusion Acknowledgments References

113 114 117 117 118 118 120 127 128 128

Application of artificial intelligence in estimating mining capital expenditure using radial basis function neural network optimized by metaheuristic algorithms Erkan Topal, Nguyen Thi Kim Ngan, Xuan-Nam Bui, and Hoang Nguyen 1. 2.

3. 4. 5.

9.

Introduction Methodology 2.1 Radial basis function neural network (RBFNN) 2.2 Metaheuristic algorithms 2.3 Proposing the metaheuristics-based RBFNN models for estimating CAPEX 2.4 Performance metrics for evaluation Data preparation Results and discussions Conclusions Acknowledgments References

131 132 132 133 137 138 139 140 145 146 146

Application of deep learning techniques for forecasting iron ore prices: A comparative study of long short-term memory neural network and convolutional neural network Hoang Nguyen, Yoochan (Eugene) Kim, and Erkan Topal 1. 2.

Introduction Methodology 2.1 Long short-term memory neural network (LSTM) 2.2 Convolutional neural network (CNN)

149 151 151 153

Contents

3. 4. 5.

Dataset used Results and discussion Conclusion Acknowledgments References

ix 155 158 161 162 162

10. Optimization of large mining supply chains through mathematical programming Luan Mai and Zenn Saw 1.

2. 3. 4.

5.

6.

Overview 1.1 Mining supply chain 1.2 Optimization using mathematical programming 1.3 Solvers Modeling challenges Mining companies applying advanced analytics Optimization model 4.1 Indices and sets 4.2 Parameters 4.3 Decision variables 4.4 Objective function 4.5 Constraints Case study 5.1 Simplified operations of the equipment network 5.2 Results and discussions Conclusions References

165 165 167 169 170 172 173 173 174 174 174 174 178 178 179 182 182

11. Underground mine planning and scheduling optimization: Opportunities for embracing machine learning augmented capabilities Prosper Chimunhu, Erkan Topal, Ajak Duany Ajak, and Mohammad Waqar Ali Asad 1. 2.

3.

Introduction Applications of machine learning in mine planning and scheduling 2.1 Accuracy of schedule parametric inputs 2.2 Symbiotic resemblance of model to production operations (model framing effectiveness) 2.3 Suitability of the optimization objective function 2.4 Dynamic capability of models to adjust to changing operating environments Conclusions References

183 185 187 188 190 192 193 193

x Contents

12. Application of artificial intelligence in distinguishing genuine microseismic events from the noise signals in underground mines Roohollah Shirani Faradonbeh, Muhammad Ghiffari Ryoza, and Mohammadali Sepehri 1. 2. 3.

4.

Introduction Database and statistical analysis Methods 3.1 Meta-heuristic algorithm 3.2 Linear discriminant analysis 3.3 Model construction 3.4 Classification performance Summary and conclusions Appendix References

197 199 201 201 203 203 209 212 212 219

13. The implementation of AI-based modeling and optimization in mining backfill design Hakan Basarir, Ehsan Sadrossadat, Ali Karrech, Georg Erharter, and Han Bin 1. 2. 3.

4.

Introduction The use of AI in backfill design Case studies 3.1 Predictive modeling practice 3.2 Optimization practices Conclusions References

221 222 224 224 233 246 247

14. Application of artificial intelligence in predicting blast-induced ground vibration Clement Kweku Arthur, Ramesh Murlidhar Bhatawdekar, Victor Amoako Temeng, George Agyei, and Yao Yevenyo Ziggah 1. 2. 3.

4.

Introduction Case study Methodology 3.1 Particle swarm optimization 3.2 Backpropagation neural network 3.3 Support vector machine 3.4 Empirical techniques 3.5 Development of various models 3.6 Statistical evaluation of model performance Results and discussion 4.1 PSO results 4.2 BPNN and PSO-BPNN models formed 4.3 SVM and PSO-SVM models formed

251 252 254 254 255 255 256 257 258 259 259 260 261

Contents

5.

4.4 Empirical models formed 4.5 Comparison of all formed models for the prediction of blast-induced ground vibration Conclusion References

xi 261 262 266 266

15. Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines Biao He, Danial Jahed Armaghani, Sai Hin Lai, and Edy Tonnizam Mohamad 1. 2.

3.

4.

5.

Introduction Background of case study 2.1 Study site 2.2 Data collection Methodology 3.1 Extreme gradient boosting 3.2 Bayesian optimization 3.3 Optimized extreme gradient boosting model Results and discussion 4.1 Evaluation criteria 4.2 Performance of developed models 4.3 Importance analysis Conclusions Acknowledgments References

269 272 272 272 275 275 276 277 279 279 280 284 285 286 286

16. Application of artificial intelligence in predicting rock fragmentation: A review Autar K. Raina, Rishikesh Vajre, Anand Sangode, and K. Ram Chandar 1. 2. 3. 4.

5. 6.

Introduction Blasting and fragmentation Blastability in traditional literature—The empirical approach Use of AI in blastability 4.1 Artificial neural networks for predicting rock fragmentation 4.2 Genetic algorithms for predicting rock fragmentation 4.3 Machine learning for predicting rock fragmentation 4.4 Hybrid approaches for predicting rock fragmentation Challenges and future directions Conclusion Acknowledgments References

291 295 296 299 301 302 302 303 307 309 310 310

xii Contents

17. Underground stope dilution optimization applying machine learning Hyongdoo Jang and Erkan Topal 1. 2.

3.

Introduction Applications of machine learning in underground stope dilution optimization 2.1 Feature range and selection 2.2 Studies applied AI methods Conclusions References

315 317 318 321 322 323

18. Applying a novel hybrid ALO-BPNN model to predict overbreak and underbreak area in underground space Chuanqi Li, Daniel Dias, Jian Zhou, and Ming Tao 1. 2.

3. 4.

5.

Introduction Methodologies 2.1 Backpropagation neural network (BPNN) 2.2 Ant lion optimizer (ALO) Data preparation and performance evaluation Results and discussion 4.1 Developing a hybrid ALO-BPNN model for predicting overbreak and underbreak area 4.2 Comparation performance of OUA prediction 4.3 Sensitively analysis Conclusion and summary References

325 326 326 327 328 331 331 333 339 339 340

19. Fragmentation by blasting size prediction using SVR-GOA and SVR-KHA techniques Enming Li, Jian Zhou, Rahul Biswas, and Zahir Elharith MohammedElamein Ahmed 1. 2. 3.

4. 5.

Introduction Data analysis and pre-processing Method 3.1 Support vector regression 3.2 Grasshopper optimization algorithm (GOA) 3.3 Krill herd algorithm (KHA) Model development and discussion Conclusion References

343 344 351 351 351 353 354 357 358

Contents

xiii

20. Application of machine vision in two-dimensional feature characterization of rock engineering Jiayao Chen, Dingli Zhang, Qian Fang, Hongwei Huang, and Anthony G. Cohn 1. 2. 3. 4.

5.

Introduction Rock mass information acquisition method Traditional image algorithms Deep learning algorithms 4.1 Classification and detection of lithology of rock mass 4.2 Analysis of rock mass block and particle size 4.3 Analysis of rock fracture 4.4 Analysis of other rock mass parameters Conclusion References

361 362 365 367 368 369 371 373 375 375

21. Groundwater potential assessment in Dobrogea region of Romania using artificial intelligence and bivariate statistics Romulus Costache 1. 2. 3.

4.

5.

6.

Introduction Study area Data 3.1 Wells inventory 3.2 Groundwater predictors Methods 4.1 Multicollinearity assessment 4.2 Weights of evidence (WOE) 4.3 Support vector machine (SVM) 4.4 ROC curve for validation Results and discussion 5.1 Multicollinearity assessment 5.2 Weights of evidence 5.3 Groundwater potential 5.4 Results validation Conclusions References

379 381 381 381 382 385 385 385 385 386 387 387 388 390 391 391 395

22. Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review Chia Yu Huat, Danial Jahed Armaghani, Ehsan Momeni, and Sai Hin Lai 1. 2.

Introduction Background of soft computing

397 399

xiv Contents 2.1 2.2 2.3 2.4

3.

4. 5. 6.

Artificial neural network (ANN) Support vector machine (SVM) Decision tree (DT) Genetic programming and gene expression programming (GP & GEP) Application of AI for pile capacity prediction 3.1 Base artificial intelligence (AI) models 3.2 Hybrid AI models Discussion Future perspective Conclusion References

401 401 403 405 405 406 409 409 413 414 415

23. Landslide susceptibility in a hilly region of Romania using artificial intelligence and bivariate statistics Romulus Costache 1. 2. 3.

4.

5.

6.

Introduction Study area Data 3.1 Landslide inventory 3.2 Landslide predictors Methods 4.1 Frequency ratio (FR) 4.2 Multilayer perceptron 4.3 ROC curve for results validation Results and discussions 5.1 Frequency ratio (FR) analysis 5.2 Landslide susceptibility mapping 5.3 Results validation Conclusions References

419 420 421 421 421 425 425 425 425 426 426 429 432 432 434

24. Spatial prediction of bridge displacement using deep learning models: A case study at Co Luy bridge Thai Ha Vu, Ngoc Quang Vu, and Nguyen Van Thieu 1. 2.

3.

Introduction Study area and data used 2.1 Co Luy bridge 2.2 Data used Methods 3.1 Long short-term memory (LSTM) 3.2 Gated recurrent unit (GRU)

437 439 439 440 440 443 445

Contents xv

4. 5. 6.

Index

3.3 Proposed deep learning models 3.4 Setting parameters 3.5 Forecasting performance metrics Results and analysis Discussions Conclusions References

446 447 447 448 455 457 457 463

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Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

George Agyei (251), Department of Mining Engineering, Faculty of Mining and Minerals Technology, University of Mines and Technology, Tarkwa, Ghana Zahir Elharith MohammedElamein Ahmed (343), Department of Land and Infrastructure, Politecnico Di Torino, Torino, Italy Ajak Duany Ajak (183), Mining & Data Analytics, Dufico Consulting, Perth, WA, Australia Danial Jahed Armaghani (45, 269, 397), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, Sydney, NSW, Australia; Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Clement Kweku Arthur (251), Department of Mining Engineering, Faculty of Mining and Minerals Technology, University of Mines and Technology, Tarkwa, Ghana Mohammad Waqar Ali Asad (183), Department of Mining Engineering and Metallurgical Engineering, WA School of Mines, Curtin University, Bentley, Perth, WA, Australia Panagiotis G. Asteris (45), Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece Hakan Basarir (221), Department of Geoscience and Petroleum, Norwegian University of Science and Technology, Trondheim, Norway Ramesh Murlidhar Bhatawdekar (251), Department of Civil Engineering, Universiti Technologi Malaysia, Johor Bahru, Malaysia; Department of Mining Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India Han Bin (221), University of Science and Technology Beijing, Beijing, China Rahul Biswas (343), Department of Applied Mechanics, VNIT Nagpur, Nagpur, Maharashtra, India Dieu Tien Bui (7), GIS Group, Department of Business and IT, University of South-Eastern Norway, Bø i Telemark, Norway Hoang-Bac Bui (75), Department of Exploration Geology, Faculty of Geosciences and Geoengineering; HiTech-CEAE Research Team, Hanoi University of Mining and Geology, Hanoi, Vietnam

xvii

xviii Contributors

Luyen K. Bui (7), Faculty of Geomatics and Land Administration; Geodesy and Environment Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam Xuan-Nam Bui (7, 23, 97, 113, 131), Innovations for Sustainable and Responsible Mining (ISRM) Research Group; Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam Cuong Xuan Cao (7), Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi, Vietnam Jiayao Chen (361), Key Laboratory for Urban Underground Engineering of Ministry of Education, College of Civil Engineering, Beijing Jiaotong University, Beijing; Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai, China Prosper Chimunhu (183), Department of Mining Engineering and Metallurgical Engineering, WA School of Mines, Curtin University, Bentley, Perth, WA, Australia Yosoon Choi (1, 23, 97, 113), Department of Energy Resources Engineering, Pukyong National University, Busan, South Korea Anthony G. Cohn (361), School of Computing, University of Leeds, Leeds, United Kingdom; Department of Computer Science and Technology, Tongji University, Shanghai; School of Civil Engineering, Shandong University, Jinan; Luzhong Institute of Safety, Environmental Protection Engineering and Materials, and School of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, China Romulus Costache (379, 419), National Institute of Hydrology and Water Management, Bucharest, Romania Daniel Dias (325), Laboratory 3SR, CNRS UMR 5521, Grenoble Alpes University, Grenoble, France Mark A. Engle (75), Department of Earth, Environmental and Resource Sciences, The University of Texas at El Paso, El Paso, TX, United States Georg Erharter (221), Norwegian Geotechnical Institute, Oslo, Norway Qian Fang (361), Key Laboratory for Urban Underground Engineering of Ministry of Education, College of Civil Engineering, Beijing Jiaotong University, Beijing, China Muhammad Ghiffari Ryoza (197), Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA, Australia Biao He (269), Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia Hongwei Huang (361), Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai, China Chia Yu Huat (397), Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

Contributors

xix

Mehdi Izadpanah (45), Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran Hyongdoo Jang (315), Faculty of Science and Engineering, Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Bentley, Perth, WA, Australia Maria Karoglou (45), School of Chemical Engineering, National Technical University of Athens, Zografou Campus, Athens, Greece Ali Karrech (221), School of Engineering, Mechanical Engineering; School of Engineering, Civil, Environmental and Mining Engineering, The University of Western Australia, Perth, WA, Australia Manoj Khandelwal (45), Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia Yoochan (Eugene) Kim (149), Department of Mining Engineering and Metallurgical Engineering, WA School of Mines, Curtin University, Bentley, Perth, WA, Australia Gerasimos Konstantakatos (45), Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece Sai Hin Lai (269, 397), Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur; Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia Tuan-Ngoc Le (23), Vinacomin—Minerals Holding Corporation, Hanoi, Vietnam Chang Woo Lee (7), Department of Energy and Mineral Resources, College of Engineering, Dong-A University, Busan, Republic of Korea Chuanqi Li (325), Laboratory 3SR, CNRS UMR 5521, Grenoble Alpes University, Grenoble, France; School of Resources and Safety Engineering, Central South University, Changsha, China Enming Li (343), Universidad Politecnica de Madrid – ETSI Minas y Energia, Madrid, Spain; School of Resources and Safety Engineering, Central South University, Changsha, China Luan Mai (165), Visagio, Perth, WA, Australia Anna Mamou (45), Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece Edy Tonnizam Mohamad (269), Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Ehsan Momeni (397), Faculty of Engineering, Lorestan University, Khorramabad, Iran Nguyen Thi Kim Ngan (131), Faculty of Economics and Business Administration, Hanoi University of Mining and Geology, Hanoi, Vietnam Hoa Anh Nguyen (75), Faculty of Basic Sciences, Hanoi University of Mining and Geology, Hanoi, Vietnam

xx Contributors

Hoang Nguyen (1, 7, 23, 97, 113, 131, 149), Innovations for Sustainable and Responsible Mining (ISRM) Research Group; Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam Long Quoc Nguyen (7), Faculty of Geomatics and Land Administration; Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam Van-Duc Nguyen (7), Department of Energy and Mineral Resources, College of Engineering, Dong-A University, Busan, Republic of Korea Sebeom Park (113), Department of Energy Resources Engineering, Pukyong National University, Busan, South Korea Autar K. Raina (291), CSIR-Central Institute of Mining and Fuel Research, Nagpur Research Center (Mining Technology), Nagpur, India K. Ram Chandar (291), National Institute of Technology Karnataka Surathkal, Mangaluru, Karnataka, India Ehsan Sadrossadat (221), School of Engineering, Civil, Environmental and Mining Engineering, The University of Western Australia, Perth, WA, Australia Anand Sangode (291), CSIR-Central Institute of Mining and Fuel Research, Nagpur Research Center (Mining Technology), Nagpur, India Zenn Saw (165), Visagio, Perth, WA, Australia Mohammadali Sepehri (197), Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA, Australia Roohollah Shirani Faradonbeh (197), Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA, Australia Athanasia D. Skentou (45), Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece Ming Tao (325), School of Resources and Safety Engineering, Central South University, Changsha, China Victor Amoako Temeng (251), Department of Mining Engineering, Faculty of Mining and Minerals Technology, University of Mines and Technology, Tarkwa, Ghana Erkan Topal (23, 97, 113, 131, 149, 183, 315), Department of Mining Engineering and Metallurgical Engineering, WA School of Mines; Faculty of Science and Engineering, Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Bentley, Perth, WA, Australia Markos Z. Tsoukalas (45), Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece Rishikesh Vajre (291), CSIR-Central Institute of Mining and Fuel Research, Nagpur Research Center (Mining Technology), Nagpur, India Nguyen Van Thieu (437), Faculty of Computer Science, Phenikaa University, Hanoi, Vietnam

Contributors

xxi

Ngoc Quang Vu (437), Department of Planning and Urban Transport, University of Transport Technology, Hanoi, Vietnam Thai Ha Vu (437), Department of Geodesy, Hanoi University of Civil Engineering, Hanoi, Vietnam Basak Zengin (45), Kahramanmaras Istiklal University, Elbistan, Turkey Dingli Zhang (361), Key Laboratory for Urban Underground Engineering of Ministry of Education, College of Civil Engineering, Beijing Jiaotong University, Beijing, China Jian Zhou (325, 343), School of Resources and Safety Engineering, Central South University, Changsha, China Yao Yevenyo Ziggah (251), Department of Geomatic Engineering, Faculty of Geosciences and Environmental Studies, University of Mines and Technology, Tarkwa, Ghana

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Editors’ biography Hoang Nguyen is a highly accomplished lecturer and researcher at the Hanoi University of Mining and Geology in Vietnam. In 2020, he obtained his PhD degree from the Surface Mining Department at the Mining Faculty of the same institution. He is also awarded the postdoctoral fellowship of the Vingroup Innovation Foundation (VinIF) in 2022 based on his outstanding research results. With an extensive publication record, Dr. Hoang Nguyen has authored two books and published more than 100 papers in reputed journals. Additionally, he serves as an editor for several esteemed journals. His research interests encompass a wide range of cutting-edge mining technologies and artificial intelligence applications in this domain. His contributions to this field have earned him recognition, such as the Young Talent Award in Science and Technology of the Hanoi University of Mining and Geology in 2019. Furthermore, he has been acknowledged as one of the world’s top 2% of scientists in both 2021 and 2022, further highlighting his exceptional achievements in the scientific community. Affiliation: Faculty of Mining, Hanoi University of Mining and Geology, Hanoi, Vietnam Expertise: Cutting-edge Mining Technologies, Blasting, Slope stability, AIdriven Mining Optimization, Predictive Analytics for Mining, and AI-enhanced Mining Predictions Xuan-Nam Bui received BEng and MEng degrees in mining engineering from Hanoi University of Mining and Geology (HUMG), Vietnam, in 1996 and 2001, and the Dr-Ing degree in mining engineering from the Technische Universitaet Bergakademie Freiberg, Germany, in 2005. From 1996 to 2008, he was a lecturer at HUMG. He was an associate professor at the Surface Mining Department at HUMG in 2009. Since 2018, he has been appointed as a full professor there. He is the author and coauthor of 24 books, nearly 280 papers in international and national journals, and conference proceedings. His research interests include advanced mining engineering, friendly environmental and smart mining, and the use of AI in predicting the impacts of mining and engineering activities on the environment for sustainable development. He is the editor-in-chief of the Journal of Mining and Earth Sciences at HUMG. Prof. Xuan-Nam Bui is also a member of the Society of Mining Professors and some editorial boards of reputed international and national journals.

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xxiv Editors’ biography

Affiliation: Full Professor, Faculty of Mining, Hanoi University of Mining and Geology, Hanoi, Vietnam Expertise: Advanced mining engineering, friendly environmental and smart mining, uses of AI in predicting impacts of mining and engineering activities Erkan Topal is a mining engineer with a MSc degree in mineral economics and MSc and PhD degrees in mining engineering from the Colorado School of Mines in the United States. His main research interests are in mine planning and optimization, and mineral and energy economics. He is the world’s leading expert in the field of mine planning and optimization. Some of his internationally recognized research includes underground mine scheduling with stope boundary optimization, mine waste dump design with pit optimization, and the application of real options in engineering design and decision-making. He is on the editorial board of five scientific journals in the area of mining, including the editor-in-chief’s role for the International Journal of Mining, Reclamation and Environment, and has published more than 100 papers in reputed journals and refereed conference proceedings. Affiliation: Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Faculty of Science and Engineering, Curtin University, Perth, WA, Australia Expertise: Mine planning and optimization, mineral and energy economics Jian Zhou obtained his BSc (2008) and PhD (2015) degrees from Central South University (CSU), China, and was a visiting scholar with the Mine Design Laboratory at McGill University from 2013 to 2014. Currently, he is an associate professor in the School of Resources and Safety Engineering at CSU, China. His current research interests include geological and geotechnical hazards prediction and mitigation, applying predictive models in rock mechanics, and mining engineering. Dr. Zhou is the highly cited researcher in the field of Cross-Field (Clarivate), the highly cited Chinese researcher in the field of Mining Engineering (Elsevier), and received the Distinguished Young Scholars Fund of Hunan Province, China. He has published more than 180 papers in international journals on mining and geotechnical issues and received China’s 100 Most Influential International Academic Papers Award, Journal of Rock Mechanics and Geotechnical Engineering Best Paper Award, and Journal of Central South University Best Paper Award. His citation and H-index are 8800 and 53, respectively. Affiliation: School of Resources and Safety Engineering, Central South University, Changsha, China Expertise: Geological and geotechnical hazards prediction and mitigation, applying predictive models in rock mechanics and mining engineering Yosoon Choi is a full professor at the Department of Energy Resources Engineering at Pukyong National University, South Korea. He has been leading the Geo-ICT Laboratory at Pukyong National University since 2011. He has been

Editors’ biography

xxv

working in the areas of smart mining, renewables in mining, AICBM (AI, IoT, cloud, big data, mobile) convergence, unmanned aerial vehicle, mine planning and design, open-pit mining operation, mine safety, geographic information systems, 3D geo-modeling, geostatistics, hydrological analysis, energy analysis and simulation, and renewable energy systems. He has written more than 200 peer-reviewed academic papers and edited 8 special issues in scientific journals, such as “Applications of Unmanned Aerial Vehicle and Artificial Intelligence Technologies in Mining from Exploration to Reclamation,” “Recent Advances in Smart Mining Technology,” and “GeoAI: Integration of Artificial Intelligence, Machine Learning and Deep Learning with GIS.” Affiliation: Department of Energy Resources Engineering, Pukyong National University, Busan, South Korea Expertise: Smart mining, GIS, Modeling and Simulation, AICBM convergence Wengang Zhang is currently a full professor at the School of Civil Engineering at Chongqing University, China. His research interests focus on deep braced excavation, slope stability, as well as big data and machine learning in geotechnics and geoengineering. He is now a member of ISSMGE TC304 (Reliability), TC309 (Machine Learning), TC219 (System Performance of Geotechnical Structures), and TC222 (Digital Twin). He was selected as the 2021 and 2022 most cited Chinese researchers for his exceptional research performance in the field of Civil Engineering. He serves as an associate editor of the journal Geoscience Frontiers and is on the editorial board of Journal of Rock Mechanics and Geotechnical Engineering, Georisk, Underground Space, Natural Hazards Research, etc. He won the Georisk Most Cited Paper Award 2021, Underground Space Outstanding Paper Award 2021, and the Computers and Geotechnics Sloan Outstanding Paper Award 2019. Affiliation: School of Civil Engineering, Chongqing University, Chongqing, China Expertise: Deep braced excavation, slope stability, big data and machine learning in geotechnics and geoengineering

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Preface It is a privilege to introduce “Applications of Artificial Intelligence in Mining and Geotechnical Engineering,” a collection of papers that presents cuttingedge artificial intelligence (AI) advancements across both mining and geotechnical engineering domains. This contemporary research book offers key insights into developmental and practical aspects of AI for both academic and practicing resource engineers, together with data scientists worldwide. Focal points include the impact of AI techniques on open-pit and underground mining with respect to modeling and optimization in mining backfill design, problems related to blasting, slope stability, truck-haulage systems, mining supply chains, mine planning, mining capital cost, commodity prices, and environment-related applications. The positive effects of AI in geotechnical engineering are also scrutinized, with contemporary analysis of their use in rock mechanics, geotechnical engineering, tunneling, geohazards, soil mechanics, civil engineering, hydraulic engineering, as well as engineering geology research. As designed, this unique volume introduces the innovative techniques required to address the increasingly multifaceted issues encountered across both mining and geotechnical engineering fields. Accordingly, its contributing authors and editors offer it as a key resource for both new and established professionals seeking to either reach or ideally remain at the forefront of this rapidly evolving software and hardware arena. Key Features l

l

l

Guides readers through the process of gathering, processing, and analyzing datasets specifically tailored for mining, geotechnical, and engineering challenges Examines the evolution and practical implementation of artificial intelligence models in predicting, forecasting, and optimizing solutions for mining, geotechnical, and engineering problems Offers cutting-edge methodologies to address the most demanding and complex issues encountered in the fields of mining, geotechnical studies, and engineering. The book is edited by internationally reputed scientists: Dr. Hoang Nguyen and Prof. Xuan-Nam Bui (Hanoi University of Mining and Geology, Vietnam), Prof. Erkan Topal (Curtin University, Australia), Assoc. Prof. Jian Zhou (Central South University, China), Prof. Yosoon xxvii

xxviii Preface

Choi (Pukyong National University, Korea), and Prof. Wengang Zhang (Chongqing University, China). We hope that the book will be interesting for all readers Thank you very much. Hoang Nguyen Xuan-Nam Bui Erkan Topal Jian Zhou Yosoon Choi Wengang Zhang

Chapter 1

The role of artificial intelligence in smart mining Yosoon Choia and Hoang Nguyenb,c a

Department of Energy Resources Engineering, Pukyong National University, Busan, South Korea, Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam, cInnovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam b

1

Industry 4.0 and smart mining

The paradigm shift occurring because of Industry 4.0 has led to the development of a new technology called smart mining in the mining industry. Smart mining technology improves productivity and safety through the digitization, intelligence, and automation of mining sites and mineral processing factories so that minerals required by the market can be mined, processed economically and safely, and delivered promptly. The scope of application of smart mining technology includes all processes, from ordering products from consuming companies to mining minerals in mines (ore production), processing minerals in factories, and product shipment. Smart mining improves productivity and safety through monitoring, analysis, prediction, diagnosis, optimization, and automation by connecting assets, processes, and people at mining sites based on advanced technologies of Industry 4.0. To realize smart mining sites, cutting-edge information and communication technologies, such as the Internet of Things (IoT), big data, mobile devices, artificial intelligence (AI), virtual/ augmented/mixed reality (VR/AR/MR), and robotics, are being introduced to mining sites. Building a smart mining site has the following requirements, as shown in Fig. 1: (1) technology that links space/state information of a physical mine site, including 3D modeling, smart sensors, and IoT, to a virtual mine model (Physical to Virtual, P2V); (2) intelligence technologies such as AI, big data analysis, and cloud computing to perform analysis, prediction, diagnosis, and optimization in virtual mine models; (3) automation technology, such as drones, autonomous driving, and collaborative robots for control (Virtual to Physical, V2P); (4) technology, such as mobile and wearable devices and VR/AR/MR, that links Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00023-0 Copyright © 2024 Elsevier Inc. All rights reserved.

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Intelligence (Analysis–Prediction–Diagnosis–Optimization) AI, Big Data, Cloud

loT Sensors GIS/BIM/3D

HMI (Human Machine Interface)

Mobile, Wearable, XR(VR/AR/MR)

Drone Self-driving Robotics

V2P (Virtualã Physical)

P2V (PhysicalãVirtual)

Virtual Mine

Physical Mine FIG. 1 Elemental technologies for building smart mining sites.

physical mine sites and virtual mine models centered on people should be combined.

2 Implementation levels of a smart mining site The implementation level of a smart mining site can be divided into three stages. Level 1 involves constructing a digital spatial database of a mine site, inputting and visualizing attribute information, and performing preliminary simulations by changing the attribute information. 3D geology/mineral modeling technology and underground space surveying technology are used to construct a spatial database of a mine site (Fig. 2). Currently, unmanned aerial

FIG. 2 Smart mining digital twin space database construction using 3D geology/mineral modeling technology and underground space surveying technology.

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systems, such as drones, are used to build high-resolution spatial databases of mine sites. Level 2 involves the one-to-one matching of the physical mine in the real world with the mine model in the virtual world, in addition to real-time monitoring. As shown in Fig. 3, a mine safety system corresponding to Level 2 of smart mining is developed and supplied to the field. In addition, recently, a technology that can implement smart mining digital twins at a low cost has been developed at a small-scale mining site using a low-power Bluetooth beacon and a smartphone with short-range communication technology (Fig. 4).

FIG. 3 Example of an ICT-based mine safety management system corresponding to smart mining digital twin [1].

FIG. 4 Development case of underground mine navigation and production management system using low-power Bluetooth beacon and smartphone [2].

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Applications of artificial intelligence in mining and geotechnical engineering

Level 3 of smart mining site implementation uses big data collected from physical mines to perform analysis/prediction/simulation in virtual mines and then optimizes site operation methods based on the results to apply it to real objects in physical mines. To this end, technology is required to reflect the optimization results of virtual mines in actual physical mines. Therefore, as shown in Fig. 5, self-driving robots driven according to the optimization results can be used to perform exploration, transportation, and environmental/safety management of smart mine sites. In addition, as shown in Fig. 6, VR/AR/MR technology and wearable devices can be used to effectively deliver the optimal simulation results of the digital twin to workers in physical mines.

FIG. 5 Development case of a small self-driving robot for underground mine tunnel mapping [3].

FIG. 6 VR and AR technology application of smart mining digital twin using a wearable device [4,5].

3 Role of artificial intelligence in smart mining To implement Level 3, the top level in the smart mining site, the roles of modeling and simulation (M&S) and AI are essential. M&S optimizes the performance of objects by analyzing/diagnosing the state of physical objects using data collected from the real world and predicting via simulations the conditions

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under which it is more efficient to operate or the circumstances under which problems may occur. As it is essential to analyze big data collected from physical mines, if M&S is performed for virtual mines, the working conditions of the mines over time can be predicted, along with the key indicators related to productivity and profitability. These prediction results are used as important data for determining the optimal mine operation method. Recently, attempts have been made to combine the M&S technique, a system science approach, with the AI technique, a data science approach. M&S techniques based on physical theories or operating rules must secure detailed information and knowledge regarding the characteristics of the target object for model development. However, the verification of the reliability of the developed model is essential. AI techniques, such as databased machine learning, require a considerable amount of data on the target object, making it difficult to analyze causal relationships to identify the cause of the problem. Therefore, they cannot be applied to special situations or non-existent systems that have not been learned. Owing to the convergence of AI and M&S, research is actively conducted to address the limitations of each technique.

4

Future perspectives

Recently, smart mining technology has attracted increasing attention worldwide owing to its potential in realizing eco-friendly, highly efficient, low-cost, and accident-free mining sites. Zion Market Research [6] predicted that the smart mining market would expand from $8.6 billion in 2018 to $22.2 billion in 2025, owing to the digital transformation of the mineral industry, with an average annual growth rate of 14.5%. With the emerging necessity to create a new type of business through the convergence of advanced technologies in the mineral industry, the need for developing smart mining technologies is increasing. With the development of smart mining technology, large amounts of data are produced, collected, and shared in real time at mining sites. Accordingly, artificial intelligence technologies, such as machine learning, which can effectively analyze big data at mining sites, are attracting increasing attention. Therefore, the importance of artificial intelligence technologies will increase in the future mining industry.

Acknowledgments This work was supported by the KETEP grant funded by the Korea Government’s Ministry of Trade, Industry, and Energy (project no. 20227A10100020).

References [1] J. Baek, Y. Choi, Deep neural network for predicting ore production by truck-haulage systems in open-pit mines, Appl. Sci. 10 (5) (2020) 1657. [2] J. Baek, Y. Choi, C. Lee, J. Suh, S. Lee, BBUNS: Bluetooth beacon-based underground navigation system to support mine haulage operations, Fortschr. Mineral. 7 (11) (2017) 228.

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[3] H. Kim, Y. Choi, Comparison of three location estimation methods of an autonomous driving robot for underground mines, Appl. Sci. 10 (14) (2020) 4831. [4] H. Kim, Y. Choi, Performance comparison of user interface devices for controlling mining software in virtual reality environments, Appl. Sci. 9 (13) (2019) 2584. [5] J. Baek, Y. Choi, Smart glasses-based personnel proximity warning system for improving pedestrian safety in construction and mining sites, Int. J. Environ. Res. Public Health 17 (4) (2020) 1422. [6] Zion Market Research, Global Smart Mining Market Is Expected To Reach Around USD 22.19 Billion By 2025, 2019. https://www.zionmarketresearch.com/news/smart-mining-market. (Accessed 24 February 2023).

Chapter 2

Application of artificial neural networks and UAV-based air quality monitoring sensors for simulating dust emission in quarries Long Quoc Nguyena,b, Luyen K. Buia,c, Cuong Xuan Caoa, Xuan-Nam Buib,d, Hoang Nguyenb,d, Van-Duc Nguyenb,e, Chang Woo Leee, and Dieu Tien Buif a

Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi, Vietnam, bInnovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam, cGeodesy and Environment Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam, dDepartment of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam, eDepartment of Energy and Mineral Resources, College of Engineering, Dong-A University, Busan, Republic of Korea, fGIS Group, Department of Business and IT, University of South-Eastern Norway, Bø i Telemark, Norway

1

Introduction

1.1 Motivations As one of Vietnam’s most important economic activities, the mining industry has been developing at a high pace. Stone, limestone, and rock are the most common construction materials in high demand due to rapid urbanization. This results in a significant increase in the number of quarries in many regions. To meet the demand for construction, the production of quarries has been supported by the continuous application of advanced technologies. It is essential to continuously enhance the technology of all producing states to achieve all goals of mining production. Mine surveying and environmental management are among the essential activities that have received significant attention from mining managers and scientists. The mining industry has made a significant contribution to the Vietnam economy. However, this industry also inevitably leads to many environmental problems. In Vietnam, larger-scale open-pit mines are mainly located near Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00012-6 Copyright © 2024 Elsevier Inc. All rights reserved.

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populated areas. For example, Tan My and Thuong Tan are among the largest stone quarries in Vietnam, just several kilometers from Tan Uyen town, Binh Duong province. While blasting is an integral part of open-pit mining, it usually causes the emission of particulate materials, for example, dust pollution and gasses potentially hazardous to health [1]. Blasting usually results in airblasts, ground vibration, fly rock, toxic fumes, and particulate matter (PM). All blasting events in mining areas emit the primary residue of PM [1], for example, PM1.0, PM2.5, and PM10. To properly manage air quality in mining areas, it is crucial to establish an effective air quality monitoring system. To meet the high demand for construction materials, mine extension is inevitable. However, the expansion of a quarry is often considered with its potential environmental impacts. An air quality monitoring system can provide important data for the environmental management of quarries not only at present but also in the future when these quarries are expanded.

1.2 Related works Recently, the rapid development in the unmanned aerial vehicle (UAV) technology has brought many benefits to a wide range of military and civil fields, such as logistics and transportation [2,3], precise agriculture [4,5], forest management and biodiversity conservation [6], hazardous and environmental management [7], and urban management [8–10]. The preliminary successes of UAV applications have proved that this technology could be promising and likely to be employed in the broader field. While the world has witnessed many excellent examples of using UAV technology in the mining industry for topographical surveys, safety investigations, and other works [11], this technology is still relatively new to Vietnam [12]. For instance, UAV technology was used to conduct a topographic survey of slope areas on an open-pit mine [13]. Another UAV-based topographic survey of an ore stockpile was given by Cryderman et al. [14]. These authors used topographic data to estimate ore carrying capacity. Lee and Choi [15] have proved that fixed-wing and rotary-wing UAVs, the most popular ones, can be used effectively in both small-scale and large-scale open-pit mines as a topographic surveying tool [15]. For air quality monitoring in open-pit mine sites, results of the laboratory and field tests conducted by Alvarado et al. [1] demonstrated the feasibility of coupling an optoelectronic dust sensor with UAVs. In recent years, UAV-based systems have been considered for monitoring dust pollution, including particulate matter PM1.0, PM2.5, and PM10 [16–18]. DJI Matrice 600 Pro with six propellers may be the most popular used due to the ability to carry a payload of up to 6 kg [19,20]. However, the price of more than 5000 USD and relatively large weight (>10kg) when carrying is a hindrance when applying to open-pit mines. Thus, lighter weight UAV-based systems, that is, Inspire 2, should be investigated. In addition, predicting PM1.0, PM2.5, and PM10 play a vital role in the pollution assessment. The literature review shows that machine learning, that is, support vector machines [21], neural networks

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[22], and deep learning [22] are state-of-the-art methods used. Nevertheless, applications of machine learning for dust pollution prediction are still rare.

1.3 Contributions As mentioned above, the application of UAVs has been proven as an alternative tool for air quality monitoring. In this study, a low-cost UAV-based system was employed at Tan My and Thuong Tan quarries in Binh Duong province, one of southern Vietnam’s largest groups of stone quarries. This low-cost UAV system, named as UMS-AM, is designed to collect a variety of data that can be used for optimizing mining operations and control the atmospheric environment. Herein, we focus on monitoring the atmospheric environment at the quarries using the UMS-AM; how the multilayer perceptron neural network (MLP neural nets) could be used to predict three dust pollution, PM1.0, PM2.5, and PM10, and finally, generating 3D model for PM1.0, PM2.5, and PM10.

2

Proposed UMS-AM system

2.1 UAV platform UAVs are classified based on different but interrelated characteristics such as size and payload, wing types, flight endurance, flight range, altitude, and capabilities [23]. With the wing types, there are two main subtypes, namely, the rotary-wing and fixed-wing UAVs. The latter is suitable for applications with longer flight endurance, but large space is needed for take off and landing. Although the former uses batteries and has shorter flight times [6], it has been increasingly common because of its ability to take off and land vertically in a small space and to maintain position. Therefore, in this study, a rotary-wing UAV was considered a feasible platform for the system. Specifically, its characteristics are given in Table 1.

TABLE 1 Main parameters of the Inspire 2 used in this study. No.

Parameter

Inspire 2

1

Weight

4000 g

2

Battery

4280 mAh

3

Camera

Multi: CMOS, 100 20 MP

4

Max flight time

Approx. 27 min

5

Cruise speed

– P-mode/A-mode: 16.4 ft./s (5 m/s) – S-mode: 19.7 ft./s (6 m/s)

6

Radio link range

7 km

7

Payload

Approx. 1.9 kg

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Applications of artificial intelligence in mining and geotechnical engineering

Various UAVs are currently available for the mining industry; however, low price is still considered the main issue. Furthermore, the payload is also a critical issue for this study because several air quality sensors and accessories are mounted on the UAV. For this purpose, DJ Inspire 2 (Fig. 1) was considered to use.

FIG. 1 DJI Inspire 2. (https://www.dji.com)

2.2 Sensor networks Optical sensors (Inspire 2’s camera parameters) The DJI Inspire 2 is equipped with Zenmuse X4S, a powerful camera featuring a 20-megapixel 1-in. sensor. Dynamic range is increased from the Zenmuse X3 by one stop, with the signal-to-noise ratio and color sensitivity increased by 1.5 stops for next-level image quality. The Zenmuse X4S uses a DJI-designed compact lens with low dispersion and low distortion 24 mm equivalent prime lens. This 84° FOV high-resolution lens make the Zenmuse X4S powerful during aerial imaging as it is on the ground. Combined with CineCore 2.0 and the Inspire 2’s powerful image processing system, the camera can record 4 K/60H.264 and 4 K/30H.265 videos at a 100 Mbps bitrate and an oversample 5.2 K video into 4 K video in real-time, capturing fine image details. Furthermore, in Burst Mode, the Zenmuse X4S supports 14 fps shooting at 20 megapixels in both JPEG and DNG formats, hence the balance between agility and image quality (https://www.dji.com/zenmuse-x4s). In this study, together with the Zenmuse X4S, we used other sensors to measure various kinds of toxic gasses in the air, including carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and dust with PM2.5, PM10. In particular, the sensors from Alphasense, including CO-B4, NO-B4, NO2-B43F, and SO2-B4 were used to measure the toxic gas. Most sensors use individual sensor boards (ISBs) with four electrodes to convert the ADC signals with 12-bit. To measure the toxic gas, ISB uses the ADC chip to measure very small amounts of current and converts it into one part per million (ppm) of gas concentration. For example, CO-B4 has a 2.000 maximum ppm and converts the current in the range from 420 to 650 nA at 2 ppm. NO-B4 has its sensitivity from 500 to 850 nA at 2 ppm and 50 of maximum ppm. NO2-B43F converts from 200 to 650 nA at 2 ppm and has 50 of

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maximum ppm. SO2-B4 measures and converts 275–520 nA at 2 ppm and has 200 maximum ppm. The B4 series (four-electrode sensors) provides OEMs with reliable sensors for several high-volume applications, especially air quality networks requiring shallow parts per billion (ppb) detection levels. Intense signal levels combined with low zero current allow the resolution to : Sjbest,0 , h T j+1  h O k best,k n o
 n

o  Sjbest  Ojbest,0 , …, Ojbest,r h Sjbest ¼ min h Ojbest,0 , …, h Ojbest,r , (4) where r is the swarm’s total number of particles. The particles continue moving in the search space till the stopping condition is reached, and their position is changed at each iteration [14].

3.2 Backpropagation neural network The BPNN is a supervised learning approach consisting of a network of layers arranged in a feedforward manner. It basically consists of three distinct groups of layers, namely, the input layer, the hidden layer, and the output layer. These layers are connected by weights. The input layer receives data from the environment, which are then sent to the hidden layer(s) via connecting weights and biases. Here, processing units known as neurons transform the received data using non-linear activation functions. The processed output from the hidden layer is sent to the output layer via connecting weights. In the output layer, the loss function (error) between the estimated output and desired output is computed. The error is then backpropagated to adjust all the weights and biases in the network. The process is repeated in each iteration until the desired output is achieved.

3.3 Support vector machine SVM is a supervised learning approach introduced by Vapnik [15]. It is widely used for both classification and regression works. For a set of M training data having input variables xk and observed target response values tk, the goal of SVM for regression is to build a hyperplane that contains as many possible training data points where xk deviates from tk by a value not greater than εinsensitive loss function for each training point x. The SVM for regression is expressed in Eq. (5) as:

256 Applications of artificial intelligence in mining and geotechnical engineering

tk ¼ ωx + h

(5)

where h is the bias term. With a small norm, the hyperplane ω is chosen while concurrently reducing the sum of the distances between these training points and the hyperplane, as determined by the ε-insensitive loss function as described in Eq. (6).  0 if jtk  ðωxk + hÞj  ε (6) jtk  ðωxk + hÞjε ¼ jtk  ðωxk + hÞj  ε otherwise The user chooses the value of ε and the regularization parameter U which controls the compromise between finding a hyperplane with good regression efficiency. Eq. (7) expresses the quadratic programming problem related to SVM. M  X  1 χ k + χ ∗k min ω,h,χ,χ ∗ kωk2 + U 2 k¼1

tk  ðωxk + hÞ  ε + χ k with χ k  0

(7)

tk + ðωxk + hÞ  ε + χ ∗k with χ ∗k  0

3.4 Empirical techniques The empirical models developed and employed in this study are USBM, Langefor-Kihlstrom, Ambrasey-Hendron, and the Bureau of Indian Standard. Their mathematical expression is shown in Table 2.

TABLE 2 Empirical models and their mathematical expressions. Empirical model USBM Ambrasey-Hendron Bureau of Indian Standard

Equation

D α V ¼ h pffiffiffiffiffi Q

D α ffiffiffiffiffi V ¼h p 3 Q α Q V ¼h D⁄ pffiffiffiffiffi α Q V ¼h D⁄ 2

Langefor-Kihlstrom

3

3

4

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3.5 Development of various models In the development of the various models, the first stage was the input selection and data partitioning. Here, the selected features by the PSO feature selection algorithm were used as inputs to create the hybrid AI models, i.e., PSO-BPNN and PSO-SVM. All nine blasting parameters of burden, spacing, bench height, number of holes, hole diameter, stemming, powder factor, the maximum charge per delay, and distance from the monitoring station were used as inputs to the creation of the standalone AI models. For the empirical models, parameters of maximum charge per delay and distance from the blasting face to the monitoring station served as inputs. Furthermore, the total 53 blasting events obtained from the quarry were partitioned into 2 sets, namely, the training dataset and the testing dataset in the ratio of 77:23, respectively. This ratio was adopted as it has been scientifically proven by Gholamy et al. [16] to produce accurate results and avoid overfitting. The 41 training samples were used to build the various models whereas the remaining 12 testing samples were used to ascertain the predictive performance of the built models. The next stage employed was the normalization of the selected input parameters for the various AI models. Here, the input features having different value ranges were transformed into the interval range of [1, 1] using Eq. (8) [17]. Normalization was done to avoid the incidence of the inputs with larger ranges influencing the prediction outcome hd ¼ kmin +

ðkmax  kmin Þ  ðhi  hmin Þ ðhmax  hmin Þ

(8)

where hd is the normalized input value, hi is the individual input value to be normalized, hmax and hmin are the maximum and minimum values of the input vector whereas kmax and kmin are 1 and 1, respectively. Using the normalized inputs and outputs for both the training and testing dataset and the fine-tuned AI models’ hyperparameters, the AI models were created. For the BPNN models (i.e., PSO-BPNN and BPNN), the hyperparameters that require fine-tuning are the number of the hidden layer, the number of neurons in the hidden layer, the type of activation functions in both the hidden and output layer, and training algorithm. In this study, the BPNN model with one hidden layer was adopted as it has been universally proven to accurately model any given function. Furthermore, hyperbolic tangent sigmoid and linear activation functions were used in the hidden layer and output layer, respectively. In addition, the Bayesian regularization training algorithm was adopted in training the BPNN. For the number of neurons, a sequential trial and error process was undertaken to determine the best neuron numbers for the two BPNN models. For the SVM models, the hyperparameters that require fine-tuning are the type of kernel, the tradeoff U, and epsilon. Thus in this study, the type of kernel, the best regularization, and epsilon values were determined using a grid search implemented in the MATLB SVM package. Fig. 2 illustrates the flowchart of the development of the various AI models.

258 Applications of artificial intelligence in mining and geotechnical engineering

Start

Collection of Blasting Data

Input Feature Selection

PSO Algorithm Testing Data

Normalised Testing Data

Train Data

Normalised Train Data

Data Partitioning

Normalisation of Inputs of Train and Test Data

Fine Turning of Hyperparameters

Train AI Model

Validate Trained AI Model

No

Are the Testing Results Satisfactory?

Yes

Make Predictions Using AI Model

End

FIG. 2 Flowchart of the development of the AI models.

For the empirical models, the non-linear least square technique implemented in Microsoft Excel was used to determine the various non-linear site-specific parameters.

3.6 Statistical evaluation of model performance The performance of the various models was statistically evaluated using mean square error (MSE), root mean squared error (RMSE), correlation coefficient (R), and mean absolute error (MAE). The mathematical representations of these indicators are illustrated in Eqs. (9)–(12). The best model was then selected using the Bayesian information criterion (BIC) expressed in Eq. (13). Xa ðmt  pt Þ2 t¼1 (9) MSE ¼ q sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Xq 2 ð m  p Þ t t t¼1 (10) RMSE ¼ q Xq j m  pt j t¼1 t MAE ¼ (11) q

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Xq

 Þðpt  pÞ ðm t  m Xq 2  ð m  m Þ  ðp  pÞ2 t t¼1 t¼1 t

SSE BIC ¼ q  ln + g  lnðqÞ q

R ¼ Xq

t¼1

(12)

(13)

where q is the number of elements in the data, SSE is the sum of square error, g is the number of input features for the various models, mt is the actual measured output values, m is the mean value for the actual measured values, pt is the model’s estimated output, and p is the mean value of the model’s estimated output.

4

Results and discussion

4.1 PSO results Using the PSO algorithm, a pre-determined number of features were progressively selected and run to ascertain the best combination of features that resulted in the least loss function (MSE) value. The resulting combination of features and their respective MSE values are outlined in Table 3. The number of input features and their corresponding MSE are further illustrated in Fig. 3.

TABLE 3 Selected features and their corresponding loss function values. Number of input features

Features

MSE

1

HD

0.6954

2

B, HD

0.6062

3

HD, PF, Q

0.3473

4

NH, HD, Q, D

0.3511

5

B, S, HD, Q, D

0.2340

6

S, HD, ST, PF, Q, D

0.2978

7

S, BH, HD, ST, PF, Q, D

0.2612

8

B, S, NH, HD, ST, PF, Q, D

0.2625

9

B, S, BH, NH, HD, ST, PF, Q, D

0.2892

260 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 3 Pre-determined number of features and their corresponding MSE.

It is evidenced in Table 3 and Fig. 3 that five input features out of the nine obtained from the quarry were selected by the PSO as the best combination of features. The selected features are burden, spacing, hole diameter, the maximum charge per delay, and distance from the monitoring station. The rejected features are bench height, number of holes, stemming, and powder factor. These features were rejected due to their minor contribution to the generation of blastinduced ground vibration. Precisely, stemming has been found in the literature to affect air overpressure greatly rather than ground vibration [18]. .

4.2 BPNN and PSO-BPNN models formed By trying out several neurons in the hidden layer sequentially, it was found out one neuron produced the optimum PSO-BPNN whereas one neuron also produced the optimum PBNN. These models used the same Bayesian regularization in their training process to update their respective weights and bias. The structure as well as their training and testing results are shown in Tables 4 and 5, respectively.

TABLE 4 Optimum architecture of the PSO-BPNN and BPNN. Model

Number of inputs

Number of neuron

Output

Optimum architecture

PSO-BPNN

5

1

1

[5-1-1]

BPNN

9

1

1

[9-1-1]

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TABLE 5 Training and testing results for optimum PSO-BPNN and BPNN. MSE

RMSE

MAE

R

PSO-BPNN

0.5222

0.7226

0.5437

0.9143

BPNN

0.3407

0.5837

0.5056

0.9452

PSO-BPNN

0.7395

0.8600

0.6743

0.8043

BPNN

0.6425

0.8016

0.7160

0.5381

Model Training

Testing

4.3 SVM and PSO-SVM models formed By using the grid search, it was found that a linear kernel with a scale of 0.0010, epsilon value of 0.2498, and regularization parameter value of 0.0010 produced the optimum PSO-SVM model. Also, based on the same grid search, it was found that a linear kernel with a scale of 0.021544, epsilon value of 0.47347, and regularization parameter value of 0.001 produced the optimum SVM model. The training and testing results of these models are shown in Table 6.

TABLE 6 Training and testing results for optimum PSO-SVM and SVM. MSE

RMSE

MAE

R

PSO-SVM

0.5592

0.7478

0.5761

0.9079

SVM

0.3576

0.5980

0.5225

0.9429

PSO-SVM

0.6159

0.7848

0.6094

0.8879

SVM

0.6367

0.7979

0.7214

0.5505

Model Training

Testing

4.4 Empirical models formed Using the non-linear least square technique, the obtained equations of the various empirical models are shown in Table 7. Moreover, the training and testing results obtained for these empirical models are shown in Table 8.

262 Applications of artificial intelligence in mining and geotechnical engineering

TABLE 7 Developed empirical models. Empirical model

Equation

USBM

Ambrasey-Hendron Bureau of Indian Standard



D 1:208 V ¼ 143:62 pffiffiffiffiffi Q D 0:0524 ffiffiffiffiffiÞ V ¼ 5:2969 p 3 Q 0:8491 Q V ¼ 0:8713 D⁄ 2

3

pffiffiffiffiffi 2:2314 Q V ¼ 86:938 D⁄

Langefor-Kihlstrom

3

4

TABLE 8 Training and testing results for empirical models. Model

MSE

RMSE

MAE

R

Training USBM

3.6474

1.9098

1.5015

0.3454

Langefors-Kilhstrom

4.1869

2.0462

1.6778

0.5079

Ambrasey-Hendron

11.0664

3.3266

3.2079

0.0045

4.1369

2.0339

1.7376

0.5376

USBM

3.2659

1.8072

1.7442

0.8856

Langefors-Kilhstrom

5.5411

2.3539

2.2085

0.8856

Ambrasey-Hendron

11.9351

3.4547

3.3422

0.7040

5.6088

2.3683

2.0558

0.6363

Bureau of Indian Standard Testing

Bureau of Indian Standard

4.5 Comparison of all formed models for the prediction of blast-induced ground vibration Using the statistical performance indicators based on the testing data, the performance values for the various models are presented in Table 9. The graphical illustration of the various results presented in Table 9 is shown in Figs. 4–7.

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TABLE 9 Performance results of various models. Model

MSE

RMSE

MAE

R

PSO-BPNN

0.7395

0.8600

0.6743

0.8043

PSO-SVM

0.6159

0.7848

0.6094

0.8879

BPNN

0.6425

0.8016

0.7160

0.5381

SVM

0.6367

0.7979

0.7214

0.5505

USBM

3.2659

1.8072

1.7442

0.8856

Langefors-Kilhstrom

5.5411

2.3539

2.2085

0.8856

Ambrasey-Hendron

11.9351

3.4547

3.3422

0.7040

5.6088

2.3683

2.0558

0.6363

Bureau of Indian Standard

FIG. 4 MSE values for the various models.

Based on the error metrics, a model is said to be better when the obtained results are closest to zero. Thus, it can be observed from Table 9 and Figs. 4 and 5 that, the PSO-SVM model had the lowest MSE, RMSE, and MAE values compared to the other models in the prediction of blast-induced ground vibration. Furthermore, based on the MSE and RMSE metrics, the standalone SVM and BPNN models outperformed the PSO-BPNN model. However, based on the MAE metric illustrated in Fig. 6, the PSO-SVM and PSO-BPNN are better than all the standalone AI

264 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 5 RMSE values for various models.

FIG. 6 MAE values for various models.

models. Further analysis of the obtained results reveals that the AI models were far better at predicting ground vibration than the empirical models. This is because the error metrics of the AI models were less than 1 and closest to 0 than the empirical models which were in the ranges of more than 1–11. A closer look at Fig. 7 reveals a clear disparity in the obtained R values. It can be seen that apart from the PSO-SVM

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FIG. 7 R values for various models.

model being the best, all the other AI models were outperformed by the empirical models even though the empirical models had poor error metric values. This disparity can be attributed to the fact that though the prediction by the empirical models was poor, the obtained values were in the same magnitude and direction thus producing good R values [19]. Hence, to arrange the various models according to their order of performance, the BIC was used. The obtained BIC values for the various models are presented in Table 10. It is worth noting that, the model with the lowest BIC value is the best.

TABLE 10 BIC values. Model

BIC

PSO-SVM

6.6092

PSO-BPNN

8.8039

SVM

16.9458

BPNN

17.0558

USBM

19.1722

Langefors-Kilhstrom

25.5161

Bureau of Indian Standard

25.6618

Ambrasey-Hendron

34.7237

266 Applications of artificial intelligence in mining and geotechnical engineering

From Table 10, it can be seen that PSO-SVM is the best model for the prediction of blast-induced ground vibration. This was followed by PSO-BPNN. Then SVM and BPNN. Then finally the four empirical techniques. This shows that the AI models are superior in the prediction of blast-induced ground vibration than the empirical models. Furthermore, it can be gleaned that combining the feature selection approach to the standalone AI models, improved the performance of the AI models.

5 Conclusion In this chapter, the application of AI in the prediction of blast-induced ground vibration has been explored. In doing that, two AI techniques, namely, support vector machine and backpropagation neural network were adopted. Using a granite quarry in Penang, Malaysia as the case study, 53 blasting events with 9 input features of burden, spacing, bench height, number of holes, hole diameter, stemming, powder factor, the maximum charge per delay, distance from the monitoring station, and one output feature of PPV were obtained. Due to the effect of multi-collinearity and the curse of dimension, a metaheuristic feature selection approach based on a particle swarm optimization algorithm was employed to select five features out of the nine. The selected features are burden, spacing, hole diameter, the maximum charge per delay, and distance from the monitoring station. Combining the selected features by PSO and the standalone AI models resulted in the creation of hybrid AI models, namely, PSO-SVM and PSO-BPNN. These hybrid models were compared to the standalone BPNN and SVM, which used all nine input features, as well as to empirical models of USBM, Langefors-Kilhstrom, Bureau of Indian Standard, and Ambrasey-Hendron. The obtained results based on MSE, RMSE, MAE, R, and finally, BIC revealed that the PSO-SVM was the best model as it had the best MSE, RMSE, MAE, R, and BIC values of 0.6159, 0.7848, 0.6094, 0.8879, and 6.6092, respectively. Even though the other metric had a different placement for the best model after PSO-SVM, the BIC, which is the model selection criterion employed, revealed PSO-BPNN as the next best model. This was followed by SVM and then by BPNN and finally by the empirical models. It can thus be concluded that combining PSO with the standalone AI techniques improved their prediction performance. Furthermore, the AI techniques are superior in predicting blast-induced ground vibration compared to the empirical techniques.

References [1] M. Khandelwal, T.N. Singh, Prediction of blast-induced ground vibration using artificial neural network, Int. J. Rock Mech. Min. Sci. 46 (7) (2009) 1214–1222. [2] M. Monjezi, M. Ghafurikalajahi, A. Bahrami, Prediction of blast-induced ground vibration using artificial neural networks, Tunn. Undergr. Sp. Technol. 26 (1) (2011) 46–50.

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[3] V.A. Temeng, C.K. Arthur, Y.Y. Ziggah, Suitability assessment of different vector machine regression techniques for blast-induced ground vibration prediction in Ghana, Model. Earth Syst. Environ. 8 (1) (2022) 897–909. [4] B. He, S.H. Lai, A.S. Mohammed, M.M.S. Sabri, D.V. Ulrikh, Estimation of blast-induced peak particle velocity through the improved weighted random forest technique, Appl. Sci. 12 (10) (2022) 5019. [5] A. Srivastava, B.S. Choudhary, M. Sharma, A comparative study of machine learning methods for prediction of blast-induced ground vibration, J. Min. Environ. 12 (3) (2021) 667–677. [6] J. Zhou, Y. Qiu, M. Khandelwal, S. Zhu, X. Zhang, Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations, Int. J. Rock Mech. Min. Sci. 145 (2021) 104856. [7] P. Bayat, M. Monjezi, A. Mehrdanesh, M. Khandelwal, Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blastinduced ground vibrations, Eng. Comput. (2021) 1–10. [8] A.I. Lawal, S. Kwon, O.S. Hammed, M.A. Idris, Blast-induced ground vibration prediction in granite quarries: an application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN, Int. J. Min. Sci. Technol. 31 (2) (2021) 265–277. [9] X.N. Bui, H. Nguyen, Q.H. Tran, D.A. Nguyen, H.B. Bui, Predicting blast-induced ground vibration in quarries using adaptive fuzzy inference neural network and moth–flame optimization, Nat. Resour. Res. 30 (6) (2021) 4719–4734. [10] G.E. Erten, S.B. Keser, M. Yavuz, Blast-induced ground vibration prediction and uncertainty quantification in granite quarries using deep ensembles model, Res. Square (2022) 1–10. [11] S.M. Kalami, Feature Selection Using Metaheuristics and EAs, 2015. https://yarpiz.com/306/ ypml122-evolutionary-feature-selection. [12] X.F. Song, Y. Zhang, D.W. Gong, X.Z. Gao, A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data, IEEE Trans. Cybern. 52 (9) (2022) 9573–9586. [13] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of ICNN’95International Conference on Neural Networks, vol. 4, IEEE, 1995, November, pp. 1942–1948. [14] M. Hasanipanah, M. Noorian-Bidgoli, D.J. Armaghani, H. Khamesi, Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling, Eng. Comput. 32 (4) (2016) 705–715. [15] V. Vapnik, The Nature of Statistical Learning Theory, Springer Science & Business Media, 1995. [16] A. Gholamy, V. Kreinovich, O. Kosheleva, Why 70/30 or 80/20 relation between training and testing sets: a pedagogical explanation, Technical Report, University of Texas, El Paso, 2018. [17] V.A. Muller, F.H. Hemond, Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels, Talanta 117 (2013) 112–118. [18] M. Mpofu, S. Ngobese, B. Maphalala, D. Roberts, S. Khan, The influence of stemming practice on ground vibration and air blast, J. South Afr. Inst. Min. Metall. 121 (1) (2021) 1–10. [19] G. Small, R. Wong, The validity of forecasting, in: A Paper for Presentation at the Pacific Rim Real Estate Society International Conference, Christchurch, New Zealand, 2002, January, pp. 1–14.

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Chapter 15

Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines Biao Hea, Danial Jahed Armaghanib,c, Sai Hin Laia,d, and Edy Tonnizam Mohamadc a

Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia, bSchool of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, Sydney, NSW, Australia, cCentre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia, dDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia

1

Introduction

Blasting is a typical rock fragmentation method used in quarrying and mining activities. Blasting in quarry operations entails drilling multiple rows of blast holes approximately parallel to the bench’s free face. Around the blasting zone, these operations can cause several issues, such as ground vibration [1–5], airoverpressure (AOp) [3,6,7], fly-rock [8–10], back-break [11–13], and overbreak [14,15]. Among them, AOp is defined as a shock wave created by the explosion of explosive material into a surface. The components of AOp can be separated into air pressure pulse, gas release pulse, rock pressure pulse, and stemming release pulse [16,17]. These pulses are compressed in form and propagate through the air in the same way as ground blast waves do. Therefore, forecasting AOp is essential for accurate control blasts and risk assessments. The most common way to predict AOp is using empirical formulas that work based on a few effective parameters. Although the proposed empirical formulas can basically solve the problem of AOp, their prediction accuracy is not favorable since they take into account fewer influencing factors. Generally, only the maximum charge per delay and distance from the blast-face are considered the most important factors in the empirical models. Nevertheless, blast geometry Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00019-9 Copyright © 2024 Elsevier Inc. All rights reserved.

269

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Applications of artificial intelligence in mining and geotechnical engineering

and vegetation, according to Ref. [18], also influence AOp. Over-charging, weak strata, and atmospheric conditions can all have an impact on AOp [19]. As a result, empirical formulae frequently produce inaccurate predictions. Currently, machine learning (ML) and artificial intelligence (AI) techniques are being successfully used in many different research works [20–29]. It is also gaining traction in the fields of rock engineering and allied fields, notably rock blasting. Many studies have been carried out to predict blasting-induced AOp, and the relevant studies are shown in Table 1. For example, Nguyen et al. [34] designed a novel ensemble ML model to predict AOp using 180 data samples

TABLE 1 Studies related to blasting-induced AOp prediction. Studies

Models

Inputs

No. data

AminShokravi et al. [30]

PSO-LR

RMR, MC, DI

80

He et al. [31]

FDM-XGBoost, FDM-RF, DT

MC, DI, PF, St

62

Nguyen et al. [32]

Cubist, GBM, RF

PF, MC, B, St, S, NR, DI, H, RH

146

Amoako et al. [33]

BI-ENN

St, NH, MC, DI

171

Sawmliana et al. [6]

ANN

MC, DI, TC, D

120

Nguyen et al. [34]

GLMNETs–MLPNN

PF, MC, St, S, B, DI

180

Zhou et al. [35]

CHAID, ANN, KNN, SVM

RQD, PF, DI, MC, NH, B

62

Asmawisham et al. [36]

PSO-ANN

MC, DI

76

Hajihassani et al. [17]

PSO-ANN

HD, PF, MC, St, B, S, RQD, NH, DI

62

Armaghani et al. [37]

ANFIS

MC, PF, BS, St, DI

128

Note: RMR, rock mass rating; MC, maximum charge per delay; DI, distance from the blast-face to monitored points; PF, powder factor; St, stemming; B, burden; S, spacing; NR, number of rows per blast; H, bench height; RH, air humidity; NH, number of holes per blast; TC, total explosive charge fired in a round; D, depth of burial of the explosive charges; HD, hole depth; RQD, rock quality designation; ANN, artificial neural network; NLE, non-linear equations; PSO-LR, particle swarm optimization based linear regression; FDM, fuzzy Delphi method; XGBoost, extreme gradient boosting trees; RF, random forest; GBM, gradient boosting machine; GLMNETs, generalized linear modeling; MLPNN, multilayer perceptron; CHAID, Chi-square automatic interaction detector; KNN, k-nearest neighbors; SVM, support vector machine.

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collected from open-pit mines. The results revealed that the proposed ensemble ML model outperforms the traditional ML models such as support vector machine (SVM), random forest (RF), and empirical equations. Further, the sensitivity analysis concluded that burden and stemming are two predominant factors affecting the magnitude of AOp, and meanwhile, spacing, the maximum charge per delay, and distance from the blasting face to monitoring points should also be considered when conducting the AOp prediction task. Zhou et al. [35] hybridized the parametric and non-parametric models to estimate the blasting-induced AOp based on 62 blasting instances in a quarry blasting. A simple linear regression model—the parametric model—was used to select input variables, and thus six input variables were successfully selected from a total of nine parameters. Then, four non-parametric techniques, i.e., chisquare automatic interaction (CHAID), k-nearest neighbors (KNN), artificial neural network (ANN), and support vector machine (SVM), were employed to implement the AOp prediction task. The results showed that the combination of these two models (i.e., the parametric and non-parametric models) not only reduces the complexity of the system but also achieves better prediction accuracy. He et al. [31] combined a fuzzy Delphi method (FDM) and two tree-based machine learning models to predict AOp caused by mine blasting. First, four input parameters, i.e., the maximum charge per delay, powder factor, distance from the blast-face to monitored points, and stemming, were selected from nine variables, and then RF and XGBoost models were utilized to predict AOp. The results showed that the combination of expert opinions and machine learning techniques could design an interpretable model that is beneficial for AOp prediction. Hajihassani et al. [17] developed a hybrid model termed PSO-ANN to forecast blasting-induced AOp. To achieve this, a total of 62 blasting events were recorded to constitute the data samples. Nine variables were considered as input parameters to construct the PSO-ANN model. The results showed that the designed PSO-ANN yielded precise results compared to the traditional empirical formulas. It has been shown that ML models have good performance in predicting AOp. However, studies on ensemble ML models such as extreme gradient boosting trees (XGBoost) are less common in AOp prediction. Moreover, Nguyen et al. [32] concluded that the ensemble ML techniques (such as RF, GBM, and Cubist) are good candidates that can implement the task of AOp prediction. Therefore, in this paper, the ensemble ML model (XGBoost) was designed to estimate blasting-induced AOp. To achieve this, four granite quarry sites in Malaysia were taken as the engineering cases, which are used for data collection. Then, the collected data was used to train the XGBoost model, and simultaneously two optimization algorithms, i.e., Bayesian optimization (BO) and the random search (RS) algorithm were employed to optimize the hyperparameters of the XGBoost models. Finally, an importance analysis was conducted to identify the predominant factors for blastinginduced AOp prediction.

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2 Background of case study 2.1 Study site Four granite quarry sites in the Johor area, Malaysia, i.e., Kulai quarry, Bukit Indah quarry, Taman Bestari quarry, and Senai Jaya quarry, were investigated in the present paper. The goal of these quarries’ blasting operations is to generate aggregate with a monthly capacity of 160,000–380,000 t, depending on weather circumstances. Among them, the Kulai quarry had the lowest bench height of 10 m, whereas the Bukit Indah quarry had the highest bench height (28 m). In the quarries of Taman Bestari, Senai Jaya, Kulai, and Bukit Indah, a variety of rock mass weathering zones were discovered, ranging from moderately worn (MW) to fully weathered (CW). In addition, to identify the weathering zones, the Schmidt hammer test was implemented to estimate the rock mass strength. The test results revealed that the lowest and maximum uniaxial compressive strength (UCS) were 40.7 and 99.8, respectively. Noted that the uniaxial compression tests were carried out on a small number of block samples acquired from the quarries. Besides, the geological discontinuities, i.e., the rock quality designation (RQD) values, were quantified as a percentage of the drill core in lengths of 100 mm or more. The RQD findings had the lowest and highest values of 22.5 and 61.25, respectively.

2.2 Data collection A total of 166 blasting events were recorded at these 4 granite quarry sites, which constituted the dataset used in this study. Note that the data used in this paper is from Jahed Armaghani et al. [3]. According to the summary of Table 1, the parameters that were selected to estimate AOp in this study included burden to spacing (BS), powder factor (PF), the maximum charge per delay (MC), and distance from the blast-face (DI). Among them, BS, PF, and MC were computed based on the design parameters of each blasting instance, and DI was recorded by measuring the distance between the blasting point of each blasting operation and the blasting face. Additionally, the principal explosive substance is ANFO, and the initiation is done by dynamite. Fine gravel is used to stem the blast holes. The AOp values were recorded using a VibraZEB seismograph with transducers. Linear L-type microphones attached to the AOp channels of recording devices were used to monitor the AOp values. The VibraZEB measures AOp levels from 88 to 148 dB. The microphones’ operational frequency response ranges from 2 to 250 Hz, which is sufficient for correctly measuring AOp in the frequency range that is important for structures and human hearing. The summary of the dataset used in this work is tabulated in Table 2. It can be seen that the range of BS is from 0.41 to 0.91, the range of PF is from 0.24 to 0.98 kg/m3, the range of MC is from 69.79 to 309.09 kg, the range of DI is from 65 to 710 m, and the range of AOp is from 89.30 to 137.8 dB. To represent the relationship between the variables, matrix scatters plots were used to achieve this, as depicted in Fig. 1. Obviously, there is a significant negative linear correlation

TABLE 2 Typical characteristics of collected data in this work. Variables

Symbol

Unit

Type

Min

Mean

Median

Max

Std. dev.

Burden to spacing

–

BS

Input

0.41

0.753

0.77

0.91

0.102

3

Powder factor

kg/m

PF

Input

0.24

0.689

0.72

0.98

0.196

Maximum charge per delay

kg

MC

Input

69.79

202.445

206.89

309.09

64.34

Distance from the blast-face

m

DI

Input

65

329.048

330

710

142.529

Air-overpressure

dB

AOp

Output

89.30

113.161

114.15

137.8

12.122

FIG. 1 Matrix scatters plots of the datasets.

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between DI and AOp, i.e., as the distance from the blasting face increases, the AOp will continue to attenuate. However, for MC, PF, and BS, none of them showed a significant linear correlation with AOp. Similarly, for these four variables (i.e., BS, PF, MC, and DI), there is also no significant linear correlation between them. Note that in this section only a simple linear relationship between these four variables and AOp is focused on, and further in-depth exploration of the deeper connection between these variables and AOp will follow.

3

Methodology

3.1 Extreme gradient boosting Extreme gradient boosting (XGBoost), developed by [38], iteratively generates a new tree to fit the residuals of the previous tree, and the accuracy increases as the number of iterations increases [39]. Regularization terms are added to the objective function of XGBoost, making the model less prone to overfitting. XGBoost, on the other hand, expands the target function by a second-order Taylor expansion, therefore having a more accurate loss function [40]. The tree model used in XGBoost to build the regression tree fit is the Classification and Regression Trees (CART) model. The mathematical model of the XGBoost is as follows: ybi ¼

n X

f t ðxi Þ, f t  F and

(1)

t¼1

  F ¼ f ð x i Þ ¼ ω l ð xi Þ ,

(2)

where n denotes the number of CART models, F represents the hypothesis space of the CART model, ft is a function in the hypothesis space, ybi are the predicted targets, xi is the input variable, l(xi) denotes the leaf node of the sample xi, and ω signifies the leaf score. In the iterative process of XGBoost, a new tree corresponds to a new function, and the freshly created tree matches the residuals from the previous prediction. This process is shown as follows. 8 ð0Þ > y^i ¼ 0 > > > > < ð1Þ ð0Þ y^i ¼ y^i + f1 ðxi Þ (3) > > ⋮⋮⋮ > > > : ðtÞ ðt1Þ + ft ðxi Þ y^i ¼ y^i The objective function is shown as follows: Xobj ¼

n X i¼1

lðy, ybÞ +

K X k¼1

Ωðf k Þ,

(4)

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P where Xobj denotes the objective function, ni¼1 lðy, ybÞ denotes the loss between P predicted and actual values, Kk¼1 Ωðf k Þ denotes the regularization term, whose mathematical model is as follows: Ωðf k Þ ¼ γT +

T 1 X λ ω2 2 j¼1 j

(5)

Here T denotes the number of leaf nodes, γ denotes the penalty coefficient, and λ denotes the constraint of the number of leaf nodes. The model is iterated using additive training to further minimize the objective function [41]. Each iteration updates the objective function to: n   X ðtÞ ðt1Þ Xobj ¼ l yi , y^i + f t ð x i Þ + Ωð f t Þ (6) i¼1

To find the ft that minimizes the objective function, XGBoost utilizes a second-order Taylor expansion approximation at ft ¼ 0 to solve the objective function. Thus, the objective function is approximated as: n   X  1 2 ðtÞ t1 l yi , y^i (7) Xobj ¼ + gi ft ðxi Þ + hi ft ðxi Þ + Ωðft Þ 2 i¼1   ∂l yi , y^t1 i gi ¼ (8) ∂^ yt1 i   ∂2 l yi , y^t1 i (9) hi ¼ ∂^ yt1 i After that, the objective function can be finally characterized as follows: n  T X 1 1 X ðtÞ 2 Xobj ¼ gi ωl ðxi Þ + hi ωl ðxi Þ + γT + λ ω2 (10) 2 2 j¼1 j i¼1 In general, the technique of determining the minimum of a quadratic function is used to optimize the objective function [42]. The dominant hyperparameters in the XGBoost model that were tuned in the present research are elaborated as follows: (1) Number of estimators: The maximum number of gradient boosted trees; (2) Learning rate: Step size shrinkage used in each iteration; (3) Subsample: Subsample ratio of the training instances; and (4) Gamma: A minimum loss reduction is required to build a new partition on a tree’s leaf node.

3.2 Bayesian optimization Bayesian optimization (BO) is a very effective, advanced, and promising global optimization algorithm [43]. BO specializes in solving objective functions with

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unknown expressions and high computational complexity [42,44]. For the optimization problem shown in Eq. (11), BO can find the global optimal solution quickly. x+ ¼ arg max f ðxÞ, x∅

(11)

where x denotes the input vectors, ∅ denotes the search domain of vectors x, f represents the objective function, and x+ signifies the position vectors when the objective function f takes its maximum value. The traditional machine intelligence issue in sequential decision theory is efficiently solved by BO, which is to determine the next evaluation position based on the knowledge collected for an unknown objective function f, in this way, BO can reach the optimal solution in the fastest way [45]. The BO can obtain the optimal solution for complex objective functions with few evaluations, essentially, because the BO uses a proxy model to fit the actual objective function and actively selects the most “promising” evaluation points based on the fitting results. The BO uses the Bayes’ theorem in the optimization process, as shown in Eq. (12). pðf jD1:t Þ ¼

pðD1:t jf Þpðf Þ , pðD1:t Þ

(12)

where f denotes the unknown objective function, D1:t ¼ {(x1, y1), (x2, y2), …, (xt, yt)} denotes the observed datasets, p(D1:tj f) represents the likelihood distribution of y, p(f) represents the prior probability distribution, p(D1:t) represents the marginal likelihood distribution of the f, p(fj D1:t) signifies the posterior probability distribution. A probabilistic surrogate model and an acquisition function are the two major essential components of the BO [46]. The probabilistic surrogate model contains both a priori probability model and an observation model. The prior probability model is represented by p(f), and the observation model describes the generation mechanism of observed data, i.e., the likelihood distribution p(D1:tj f). Updating the probabilistic surrogate model indicates that a posterior probability distribution p(fj D1:t) containing more information about the data can be obtained according to Eq. (12). The acquisition function is built using the posterior probability distribution, and the acquisition function is maximized to find the most promising evaluation point. Simultaneously, a good acquisition function assures that the chosen evaluation point can reduce the overall loss.

3.3 Optimized extreme gradient boosting model In this section, the construction of the BO-XGBoost model was introduced in detail. Before constructing the models, the dataset including 166 samples is

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randomly split into 2 parts, i.e., 80% of the dataset (132 samples) for training the models and 20% of the dataset (34 samples) for testing the performance of the models. Moreover, to eliminate the negative effect of magnitude between the input variables and speed up the model’s training, the dataset was normalized using the following equation: x∗i ¼

xi  xmin , xmax  xmin

(13)

where xi denotes the data samples belonging to columns i, xmax and xmin denote the maximum and minimum values of xi, respectively, and x∗i signifies the normalized data samples. After completing the pre-processing of the data, the main work is to establish the ML models. The primary step for constructing the BO-XGBoost model is shown as follows. Step 1: Determine the objective function that needs to be optimized by BO. First, based on the procedures in Section 3.1, the single XGBoost model can be established. Then, since the BO algorithm is generally used to solve maximization problems, thus, the objective function is defined as the function of the coefficient of determination (i.e., R2), whose formula is shown as Eq. (14). In this way, the BO algorithm will optimize the XGBoost model to seek the maximum R2, therefore determining the optimal hyperparameters of the XGBoost model. During the process, a fivefold cross-validation method is used. Step 2: Determine the range of the hyperparameters. For the number of estimators, it is fixed at 1000. For the learning rate, its range is set to [0.01, 0.20]. For the subsample, its range is set to [0.2, 0.8]. For the gamma, its range is set to [0.0, 1.0]. Step 3: Optimize the hyperparameters. The next hyperparameter set is chosen by maximizing the acquisition function to assess the objective function in the optimization process, and the Gaussian process is utilized to develop a probabilistic model utilizing prior search results. At the same time, the iteration results of each set of candidate hyperparameters are recorded. The loop continues until the pre-defined number of iterations is reached. In this paper, the number of iterations of the BO algorithm is set to 100. The framework of the BO-XGBoost model is depicted in Fig. 2. In addition, to identify the performance of the BO-XGBoost model, we also used a method called the random search cross-validated method (RS) to optimize the hyperparameters of XGBoost. For the RS method, the selected distributions are sampled for a pre-determined number of parameter adjustments, and the number of iterations specifies the number of parameter settings that are attempted [47]. Like the BO algorithm, the RS method also uses fivefold cross-validation and the number of iterations is set to 100.

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FIG. 2 The framework of the BO-XGBoost model.

4

Results and discussion

4.1 Evaluation criteria In this section, the performance of the two mentioned XGBoost-based models (i.e., the BO-XGBoost model and RS-XGBoost model) were evaluated using four metrics, that is, the coefficient of determination (R2), the root mean squared error (RMSE), the mean absolute error (MAE), and the variance account for (VAF). For an excellent model, the larger the R2 and VAF the better, while the smaller the MAE and RMSE the better. The equations for computing these four metrics are shown as follows: n  2 X AOpmea  AOppre i¼1 R2 ¼ 1  X n  i¼1

AOpmea  AOpmea

2

,

(14)

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 1 var AOpmea  AOppre A  100%, VAF ¼ @1  varðAOpmea Þ 0

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n  2 1X RMSE ¼ AOpmea  AOppre , n i¼1 MAE ¼

n   1X AOp  AOp , mea pre n i¼1

(15)

(16)

(17)

where n is the number of data samples, AOpmea, AOppre, and AOpmea denote the measured AOp, predicted AOp using ML models, and the mean of measured AOp, respectively, and var() denotes the variance of the target object.

4.2 Performance of developed models For the sake of estimating blasting-induced AOp, the BO-XGBoost model and RS-XGBoost model were used. A total of 132 samples (80% of the dataset) were used to train the two XGBoost-based models. The iteration results of BO-XGBoost and RS-XGBoost are shown in Figs. 3 and 4, respectively. In Fig. 3, when the number of iterations is 36, the BO-XGBoost model obtains the largest R2 value of 0.8096. According to the statistical results, the number of iterations with R2 values above 0.70 in 100 iterations is 74, which indicates that the overall performance of the BO-XGBoost model is better. Although the R2 value fluctuates a lot (e.g., the 87th iteration) during the 100 iterations, it is still maintained at a high level overall. In Fig. 4, the RS-XGBoost model

FIG. 3 Iteration results of BO-XGBoost model.

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FIG. 4 Iteration results of RS-XGBoost model.

obtains the largest R2 value of 0.8084 when the number of iterations is 43. Intuitively, compared to the BO-XGBoost model, R2 values fluctuate less over 100 iterations. However, the statistical results show that the number of iterations with R2 values of the RS-XGBoost model above 0.70 in 100 iterations is 38, which is significantly smaller than that of the BO-XGBoost model. Although the optimal R2 value of the RS-XGBoost model is close to the optimal R2 value of the BO-XGBoost model, the analysis shows that the overall performance of the RS-XGBoost model is inferior. In other words, the BO algorithm has a greater probability of capturing the optimal parameters of the XGBoost model. The optimal hyperparameter sets of the two XGBoost-based models are shown in Table 3.

TABLE 3 The range and optimal hyperparameters for the XGBoost-based models. Optimal values

Hyperparameters

Lower limit

Upper limit

BO-XGBoost

RS-XGBoost

Learning rate

0.01

0.20

0.03

0.07

Subsample

0.20

0.80

0.55

0.73

Gamma

0.0

1.0

0.0003

0.0192

To accurately compare the performance differences between the BOXGBoost model and the RS-XGBoost model, the values of R2, RMSE, MAE, and VAF were computed and tabulated in Table 4. For the performance

TABLE 4 Results of the performance of the XGBoost-based models. Training set 2

Testing set

Models

R

RMSE

MAE

VAF (%)

R

BO-XGBoost

0.998

0.010

0.008

99.831

RS-XGBoost

0.952

0.054

0.044

95.209

2

RMSE

MAE

VAF (%)

0.956

0.057

0.046

95.667

0.951

0.061

0.051

95.165

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results of the training set, R2 of 0.998 and VAF of 99.831% of the BO-XGBoost model are significantly larger than that of the RS-XGBoost model with R2 of 0.952 and VAF of 95.209%, and RMSE of 0.010 and MAE of 0.008 of the BO-XGBoost model are significantly smaller than that of the RS-XGBoost model with RMSE of 0.054 and MAE of 0.044. For the performance results of the testing set, it can be seen that the R2 and VAF of the BO-XGBoost model are slightly larger than that of the RS-XGBoost. Similarly, for the RMSE and MAE, the results show that the prediction error of the BO-XGBoost model is slightly smaller than that of the RS-XGBoost model. In summary, the BO-XGBoost model performs significantly better than the RS-XGBoost model on the training set, while the BO-XGBoost model performs slightly better than the RS-XGBoost model on the test set. To have a better understanding of the BO-XGBoost and RS-XGBoost models’ performance, the predicted and measured AOp of the training and testing datasets are depicted in Figs. 5 and 6, respectively. The results revealed that the AOp results predicted by the BO-XGBoost model on the training set are closer to the measured AOp compared to the RS-XGBoost model, which can be inferred from the higher R2 and VAF value as well as the lower RMSE and MAE values of the BO-XGBoost model. For the testing set, the two models are closer in performance on it. In the light of the above discussion, it can be concluded that the BO-XGBoost model outperforms the RS-XGBoost model in predicting the blasting-induced AOp of this engineering case. Furthermore, in a published work that used the same dataset as the present paper [3], two ML models, i.e., ANFIS and ANN, were developed to estimate the blasting-induced AOp, and the performance of the models is shown in Table 5. In comparison, the BO-XGBoost model proposed in the present paper has a better ability to predict AOp. In addition, in that paper, only MC and DI were considered as the input parameters to predict AOp, not including the BS and PF, which is insufficient to provide a comprehensive analysis of the factors affecting AOp. Therefore, in

FIG. 5 Predicted vs measured AOp of BO-XGBoost model on training and testing sets.

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FIG. 6 Predicted vs measured AOp of RS-XGBoost model on training and testing sets.

this paper, we consider four factors that have an impact on AOp, and the ML model (i.e., BO-XGBoost) developed on this basis has a higher accuracy, which means that it is reasonable and beneficial to consider the impact of multiple factors on AOp.

TABLE 5 Comparison with the published works. Training set 2

Testing set

R

RMSE

MAE

VAF (%)

R

BO-XGBoost

0.998

0.010

0.008

99.831

ANFIS

0.971

0.043

–

ANN

0.828

0.104

–

Models

2

RMSE

MAE

VAF (%)

0.956

0.057

0.046

95.667

94.715

0.947

0.058

–

94.715

82.816

0.864

0.101

–

84.831

4.3 Importance analysis Regarding the potential risks of AOp caused by quarry blasting operations, it is critical to identify the key component impacting AOp. As a result, importance analysis was utilized in this section to determine the importance of predictor variables that are used to predict AOp. As previously stated, the BO-XGBoost model was developed using four variables: BS, PF, MC, and DI. Based on the “feature_importances” attribute of the XGBoost model, the importance of each input parameter can be obtained [48]. In particular, the XGBoost model determines the features to be picked as split nodes according to the structure fraction’s gain. As the frequency of the input variable used to generate the decision trees in the model increases, their influence on the output parameter becomes more obvious [40]. Based on this, the importance of the input variables

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can be obtained and illustrated in Fig. 7. Intuitively, the DI shows the greatest importance of 0.733 compared to the other three parameters, while BS, PF, and MC all show the least importance with AOp. This is consistent with the findings of Section 2.2, which demonstrate the importance of DI for the prediction of AOp in this engineering case.

FIG. 7 Importance of the input parameters.

5

Conclusions

This paper focused on the prediction of the blasting-induced AOp. A dataset containing 166 samples was collected from 4 granite quarry sites in Malaysia. The dataset included four input variables (i.e., BS, PF, MC, and DI) and one output (AOp). After that, the dataset was utilized to train the ML models after properly evaluating the relevant elements impacting the AOp (i.e., BO-XGBoost model and RS-XGBoost model). Subsequently, four metrics, i.e., R2, RMSE, MAE, and VAF, were utilized to evaluate the performance of the established ML models. The findings revealed that the BO-XGBoost model shows the best performance compared to the RS-XGBoost model as well as another two models (ANFIS and ANN) in a published work, as evidenced by the fact that the BO-XGBoost model has R2 of 0.998 and 0.956, RMSE of 0.010, 0.057, MAE of 0.008 and 0.046, and VAF of 99.831% and 95.667% on the training and testing sets, respectively. The findings of this paper proved that the BO-XGBoost model is a powerful and robust tool for AOp prediction. Further, the importance analysis of the input variables illustrated that DI showed the greatest importance with AOp in the present engineering case.

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Acknowledgments The authors would like to appreciate the Faculty of Engineering, Universiti Malaya, and the facilities provided which enabled the study to be carried out.

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Chapter 16

Application of artificial intelligence in predicting rock fragmentation: A review Autar K. Rainaa, Rishikesh Vajrea, Anand Sangodea, and K. Ram Chandarb a

CSIR-Central Institute of Mining and Fuel Research, Nagpur Research Center (Mining Technology), Nagpur, India, bNational Institute of Technology Karnataka Surathkal, Mangaluru, Karnataka, India

1

Introduction

In recent years, the blasting and fragmentation literature has emphasized the importance of sustainability and social responsibility in blasting operations. The environmental and social impacts of blasting, such as noise, dust, and community relations, have been increasingly recognized and addressed in research and practice. The development of new blasting techniques, such as pre-splitting, cushion blasting, and water-based blasting, has aimed to reduce the environmental and social impacts while maintaining the blasting efficiency and safety. The integration of sustainability criteria into the blasting design and evaluation has also been proposed as a way to promote sustainable development and stakeholder engagement. Rock fragmentation is a critical process in mining and civil engineering activities, which involves the breaking of rock mass into smaller fragments from the in-situ condition or block sizes through drilling and blasting. Fragmentation affects various aspects of mining and excavation operations, including excavation productivity, material handling efficiency, downstream processing, and quality of ore extracted. Blasting being part of the mine mill fragmentation system or MMFS [1] cannot be seen in isolation, as the performance of the system depends on fragment size with contrasting equations of performance characteristics of individual operations like drilling, blasting, loading, hauling, and crushing/grinding or the comminution process. It will not be inappropriate to mention that the mechanical nature of downstream operations and further breakage through such means is costlier than that obtained through blasting. Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00003-5 Copyright © 2024 Elsevier Inc. All rights reserved.

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The rock mass being blasted is comprised of individual blocks defined by joints and their spacings, which under the dynamic loading of explosion are further broken into smaller fragments for easy loading by excavators, hauling by the dumpers, and transportation to the respective destinations for further processing. It is therefore essential to have an understanding of the fragmentation process, measure the fragmentation, and predict the same with a greater degree of confidence. The whole exercise in this condition scales down to prediction and/ or measurement of fragmentation [2]. The process of measurement of fragmentation, deriving the cost relationships of unit operations and fragment size, forms the basis for optimization of fragmentation [3] for optimal performance of the MMFS. Accordingly, predicting rock fragmentation is essential for optimizing mining efficiency and reducing operational costs, as it allows engineers to design blasting parameters, such as burden, spacing, and delay time, to achieve the desired fragment sizes [4]. However, predicting rock fragmentation is challenging, as it involves complex interactions between rock mass properties, blasting parameters, and fragmentation outcomes, which are difficult to model using traditional empirical relationships [5]. Traditional methods of predicting rock fragmentation rely on empirical relationships between rock properties and blasting outcomes, which have limitations in accuracy and generality. For example, the Kuz-Ram model [6] is a widely used empirical model for predicting rock fragmentation, which relates the specific energy of the blasting operation to the mean fragment size. The model was later modified for the inclusion of rock characteristics and further discussed in detail [7,8]. However, this model has been shown to have limitations in accuracy and applicability, as it does not consider the effect of other blasting parameters, such as the geometry of the blast design on fragmentation outcomes [8]. The fragment size after said empirical method determines the mean fragment size and its uniformity in terms of explosive load and the blast design variables. Therefore, another aspect of the prediction is to define the distribution which best fits the blasted fragment sizes. As such, empirical models, such as the Rosin-Rammler distribution, a form of Weibull distribution, have been used for predicting rock fragmentation [9], but they suffer from similar limitations in accuracy and generality. More recently, the Swebrec function [10] and distribution-free methods [11] have been claimed to be better representations of blast fragmentation. However, the constraints in such prediction have motivated researchers to advance the subject through the application of intelligent methods. AI techniques have been successfully applied in various domains, such as image recognition, natural language processing, and speech recognition, and have shown potential for improving the accuracy and efficiency of such predictions. Artificial intelligence (AI) techniques have also emerged as promising tools for predicting rock fragmentation, by capturing complex relationships between rock properties, blasting variables, and blast outcomes. AI techniques,

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such as machine learning, artificial neural networks, genetic algorithms, fuzzy logic, and hybrid methods, can learn from data and extract patterns and relationships that are difficult to model using traditional methods. Several outcomes and processes in blasting have been modeled using different techniques of AI and some of these are summarized in Table 1. It may be pointed out that most of such works pertain to the prediction of ground vibration, air overpressure, and flyrock distance. Several other citations report different methods for modeling the effects of the blasting and can be found elsewhere. However, it is imperative that some of the outcomes of the blasting phenomenon are significantly represented in the AI domain now and efforts are still on to improvise over such predictions.

TABLE 1 Different methods used to model blast outcome by various authors using AI techniques.

S. No.

Reference

Blast outcome modeled

1.

Trivedi [12]

BP

ANN

2.

Monjezi et al. [13,14]

GV

ANN

3.

Hajihassani et al. [15]

GV

Particle swarm optimization

4.

Bakhshandeh Amnieh et al. [16]

GV

ANN

5.

Saadat et al. [17]

GV

ANN

6.

Jahed Armaghani et al. [18]

GV

ANN, AN-Fuzzy inference system

7.

Singh and Singh [19]

GV

ANN, Backpropagation network

8.

Garai et al. [20]

GV

RFA

9.

Taheri et al. [21]

GV

ANN

10.

Bakhshandeh Amnieh et al. [22]

GV

ANN

Method used

Continued

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TABLE 1 Different methods used to model blast outcome by various authors using AI techniques—cont’d Blast outcome modeled

Method used

S. No.

Reference

11.

Armaghani et al. [23]

GV, Rf

EANN, Particle swarm optimization

12.

Li et al. [24]

GV

Biogeography-ANN (BBO), Particle swarm optimization

13.

Rana et al. [25]

GV

ANN

14.

Hajihassani et al. [26]

GV

Artificial neural network optimized by imperialist competitive algorithm

15.

Azimi et al. [27]

16.

Bhatawdekar et al. [28]

GV

Review of soft computing methods in GV prediction

17.

Dindarloo [29]

GV

Genetic programming

18.

Hajihassani et al. [15]

GV, AoP

Warm optimization-based artificial neural network

19.

Jahed Armaghani et al. [30]

AoP

Hybrid AI

20.

Tonnizam Mohamad et al. [31]

AoP

Neuro-genetic

21.

Jahed Armaghani et al. [32]

AoP

ANN, and imperialist competitive algorithm (ICA)-ANN

22.

Armaghani et al. [33]

AoP

Neuro-fuzzy

23.

Bhatawdekar et al. [34]

Rf

More than 20 methods used by various authors as compiled and reviewed in the paper

24.

Fang et al. [35]

k50

Firefly optimization algorithm and boosted generalized additive model

25.

Shi et al. [36]

k50

Support vector machines

26.

Bahrami et al. [37]

k50

ANN

Hybrid genetic algorithm-optimized artificial neural network

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TABLE 1 Different methods used to model blast outcome by various authors using AI techniques—cont’d Blast outcome modeled

S. No.

Reference

Method used

27.

Mehrdanesh et al. [38]

k50

ANN, classification, regression tree, and support vector machines

28.

Esmaeili et al. [39]

k50

(ANN), an adaptive neuro-fuzzy inference system (ANFIS)

29.

Shi et al. [40]

k50

Backpropagation Neural Network (BPNN) improved by genetic algorithm (GA-BP)

30.

Bhatawdekar et al. [34]

BB

ANN, fuzzy interface system, and support vector machine

31.

Ghasemi [41]

BB

Particle swarm optimization

32.

Esmaeili et al. [39]

BB

ANN and ANFIS

GV, ground vibration; AoP, air overpressure; k50, mean fragment size of blasted muck; BB, backbreak; ANN, artificial neural network; BP, blast performance.

However, it may be pointed out that the basic premise of blasting is the rock mass properties and the explosive properties followed by the blast design variables. The efforts to improvise the rock mass evaluation through AI techniques are, nonetheless, limited as will be clear in this chapter. Similarly, the prediction of fragmentation from blasting has also not been covered in such detail as warranted by its importance in MMFS economics. Keeping in view the above facts, this review provides a summary of the recent advances in the application of AI techniques for predicting rock fragmentation, including machine learning, artificial neural networks, genetic algorithms, fuzzy logic, and hybrid methods. The paper discusses the advantages and limitations of AI techniques and the challenges and opportunities for their further development and application in rock fragmentation prediction.

2

Blasting and fragmentation

Blasting and fragmentation are critical components of mining and construction activities, as they directly influence the efficiency, productivity, and safety of these operations. Fragmentation is known to affect the downstream costs and the overall economics of the MMFS [42,43]. The literature on blasting and

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fragmentation is extensive and diverse, covering a wide range of topics, such as blast design, rock fracture mechanics, explosives, energy distribution, fragmentation analysis, and prediction of blast outcomes. The early studies on blasting and fragmentation were mainly based on empirical and semi-empirical models, which used simple equations and assumptions to predict the blast outcomes. These models relied on the experience and intuition of the blasters and were limited in their accuracy and applicability. However, they provided a starting point for the development of more advanced models and methods. In the 1980s and 1990s, with the advent of computers and numerical simulations, the blasting and fragmentation research shifted towards more quantitative and analytical approaches. The use of finite element, discrete element, and boundary element methods allowed for the modeling of the rock behavior and the blast wave propagation in a more realistic and detailed manner. These methods also enabled the optimization of the blasting parameters, such as the hole diameter, spacing, and depth, to achieve the desired fragmentation outcomes. In the 2000s and 2010s, the blasting and fragmentation research further evolved towards the integration of advanced technologies, such as sensors, drones, and artificial intelligence. The use of sensors and drones allowed for the collection of real-time and high-resolution data on the blasting outcomes, such as the fragment size distribution, the flyrock distance, and the ground vibration. The use of artificial intelligence, such as machine learning algorithms and optimization techniques, allowed for the prediction and optimization of the blasting outcomes based on the input variables, such as the rock properties, the blasting parameters, and the environmental factors.

3 Blastability in traditional literature—The empirical approach There are hundreds of publications in the published domain and particularly various symposia on “Rock Fragmentation by Blasting” commonly referred to as FragBlast wherein different measurement methods, predictive regimes, MMFS optimization case studies, and complicacies in prediction and measurement of blast fragmentation are documented. A simple search with the keywords “rock fragmentation” in Google Scholar yields 2330 results as of the date, which speaks a lot about the amount of literature available on the subject. It is hence technically not feasible to review the same except for what has been said in the previous or following sections keeping in view the scope of this chapter. The empirical approach to blastability assessment has been widely used in the literature and industry, particularly in small-scale operations and in developing countries where advanced technologies and resources are limited. The empirical approach involves the use of simple tests and visual inspection to estimate the hardness, strength, and other properties of the rock, and to assess its

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blastability. These tests include the scratch test, the hammer test, the point load test, and the slake durability test. The empirical approach is based on the assumption that experienced blasters can use their judgment and knowledge of the rock properties and the blasting parameters to select the appropriate drilling and blasting methods. There are umpteen number of methods that have been used to assess the blastability of the rock mass using traditional and advanced statistical techniques. Some important works in this regard are summed up in Table 2 and explained in Fig. 1.

TABLE 2 Various approaches used by authors in predicting blastability. S. No.

Reference

Focus area

1.

Qu et al. [44]

Correlation analyses of blastability

2.

Bameri et al. [45]

Uncertainty consideration

3.

Bhatawdekar et al. [46]

Blastability in Tropically Weathered Limestones

4.

Segarra et al. [47]

Discontinuity mapping with photogrammetry

5.

Dey and Sen [48]

Review of blastability

6.

Ignatenko et al. [49]

Pre-project assessment

7.

Huang [50]

Investigations

8.

Nourian and Moomivand [51]

New method for uniformity index of Fragment Size

9.

Scott [52]

Blast design

10.

Christaras and Chatziangelou [53]

Blastability quality system

11.

Mitrovic et al. [54]

Explosive energy distribution

12.

Lu [55]

In-situ and blasted block-size distributions

13.

Segaetsho and Zvarivadza [56]

Improvement of wall control

14.

Tazhibaev et al. [57]

Blast resistance of rocks

15.

Navarro et al. [58]

Ore Grade Assessment from Drill Monitoring

16.

Rakishev [59]

New characteristic

17.

Bhatawdekar et al. [60]

Review

18.

Rustan and Kumar [61]

Laboratory scale single-hole blastability test Continued

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TABLE 2 Various approaches used by authors in predicting blastability—cont’d S. No.

Reference

Focus area

19.

Segaetsho and Zvarivadza [62]

Wall control at a hard-rock mine

20.

Kosolapov [63]

Modern Methods and Tools

21.

Lilly [64]

Empirical classification

22.

Widzyk-Capehart and Lilly [65]

A review of general considerations

23.

Chatziangelou and Christaras [66]

Rock mass quality

24.

Rached et al. [67]

Strength Properties of Rock

Blastability Factors affecting blastability Geological factors

Geotechnical factors

Blast design parameters

Rock type

Rock strength

Bench height

Groundwater Conditions

Joints persistence

Burden

Slope angle

Number of joints

Spacing

Geological discontinuities

RQD

Stemming length

Fracture or weak zones FIG. 1 Measures used in the traditional approach to the blastability assessment.

Several studies have evaluated the effectiveness and limitations of the empirical approach to blastability assessment. A complete description of such methods can be seen in Salmi and Sellers [68]. In conclusion, the empirical approach to blastability assessment has been widely used in the literature and industry and can be effective for preliminary

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assessment and design. However, the empirical approach has limitations and significant subjectivity and needs to be complemented with more advanced methods, such as numerical simulations, geophysical methods, and the application of artificial intelligence (AI) methods, to improve the accuracy and reliability of blastability. Further research and development are needed to integrate the empirical approach with these advanced methods and to optimize the blasting outcomes.

4

Use of AI in blastability

The use of artificial intelligence (AI) in blastability assessment has emerged as a promising approach to improve the accuracy, efficiency, and safety of the blasting process. AI techniques, such as machine learning, neural networks, and genetic algorithms, can analyze large and complex datasets of rock properties, drilling and blasting parameters, and blasting outcomes, and can identify the optimal drilling and blasting methods for each rock type and blast site. AI can also simulate the blasting process and predict the fragmentation size distribution, the muck-pile shape, and the flyrock and vibration levels, and can optimize the blast design to minimize the environmental and social impacts. Several studies have demonstrated the potential use of AI in blastability assessment. For example, Zhou et al. [69] presented a multi-factor index system for rock mass blastability grading in metal mines, which consists of six indicators including density, p-wave velocity, wave impedance, uniaxial compressive strength, rock elasticity, and uniaxial tensile strength. The improved rock engineering system (RES)-multi-dimensional cloud rock mass blastability classification model is established based on the system engineering theory and the cloud-inference expert semi-quantitative (CESQ) method. The system is designed to support engineering case analysis and is consistent with the results of the sample blasting experiment. The model can effectively obtain expert experience and decrease the subjectivity of the traditional RES methods. The five 6-D cloud models for the rock mass blastability grade are generated via the multi-dimensional cloud theory. The evaluation system built in this paper has great potential for rock mass blasting classification and mine blasting engineering design. Zhang et al. [70] used the support vector machine (SVM) algorithm to optimize the drilling and blasting parameters, such as the hole diameter, the hole depth, the burden, and the spacing and achieved a significant reduction in the blasting cost and a considerable increase in the fragmentation size. The authors concluded that the SVM algorithm could improve the efficiency and profitability of the blasting process. A summary of such techniques considered for blastability by various authors is given in Table 2 and explained in Fig. 2.

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1. ARTIFICIAL NEURAL NETWORKS 2. GENETIC ALGORITHM 3. MACHINE LEARNING 4. HYBRID APPROACHES

DATA AQUISITON AND ANALYSIS

AI IN BLASTABILITY

OPTIMIZATION TECHNIQUES

INTELLIGENT DECISION MAKING SYSTEM

BLAST MODELLING AND PREDICTION

BLAST DESIGN OPTIMIZATION

PREDICTION OF BLASTABILITY

FIG. 2 Use of AI in blastability and related modeling.

The use of AI in blastability assessment also has implications for safety and sustainability. AI can predict the flyrock and vibration levels and can optimize the blast design to minimize the risk of accidents and damage to the neighboring communities and environment. AI can also reduce the waste and energy consumption associated with the blasting process, by optimizing the drilling and blasting parameters and reducing the need for secondary blasting and rehandling. In conclusion, the use of AI in blastability assessment has the potential to revolutionize the blasting process and improve the efficiency, accuracy, and safety of the process. Further research and development are needed to optimize AI algorithms and integrate them with traditional methods and expert judgment. The implementation of AI in blastability assessment also requires a cultural and

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organizational shift, and a strong commitment to data collection, sharing, and collaboration among the mining industry, academia, and the government.

4.1 Artificial neural networks for predicting rock fragmentation Artificial neural networks (ANNs) are a type of machine learning algorithm that simulate the structure and function of biological neurons to learn complex patterns and relationships in data. ANNs consist of interconnected nodes, called neurons, which are organized into layers. Each neuron receives inputs from other neurons or external sources, applies a non-linear function to the inputs, and produces an output that is transmitted to other neurons or external destinations. ANNs have been widely used for predicting rock fragmentation, due to their ability to learn complex and non-linear relationships between input variables and output variables. ANNs can be trained using a variety of learning algorithms, such as backpropagation, genetic algorithms, and particle swarm optimization. Several studies have reported the successful application of ANNs for predicting rock fragmentation. For example, Kulatilake et al. [71] used a singlehidden layer backpropagation neural network to predict mean particle size resulting from blast fragmentation. The neural network model was trained using a portion of the blast data, which was clustered into two groups based on intact rock stiffness. The in-situ block size was used to represent rock mass structure, and the modulus of elasticity was used to represent intact rock properties in the developed models. The developed neural network models were compared with measured mean particle size and predictions based on one of the most applied fragmentation prediction models in the blasting literature. The prediction capability of the trained neural network models was found to be strong and better than the most applied fragmentation prediction model. Sayadi et al. [72] utilized artificial neural networks (ANNs) to predict both fragmentation and backbreak simultaneously in Tehran Cement Company limestone mines in Iran. The authors compared the performance of backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) models and found that the BPNN model was the most accurate in predicting both fragmentation and backbreak. The study also identified the burden and stemming as the most effective parameters on the outputs, whereas specific charge was the least effective parameter for both outputs. The optimal architecture for BPNN was 6-10-2, and for RBFNN, it was 6-36-2 with a spread factor of 0.79. However, it is important to note that the study was limited to a specific location and dataset, and further research is needed to validate the use of neural networks in predicting blasting outcomes in other locations and with different parameters. Amoako et al. [73] assessed the potential of ANN and SVR techniques in rock fragmentation prediction, yielding satisfactory results and outperforming the Kuznetsov model. They demonstrated the possibility of analyzing a varied number of hidden layers for a neural network using Keras Python library’s Bayesian optimization feature, making the analysis less time-consuming. The study aims to improve model performance via data augmentation and build

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additional rock fragmentation models using other machine learning techniques. They plan to develop robust machine learning-based fragmentation software that predicts the entire fragment size distribution. One advantage of ANNs is their ability to capture complex and non-linear relationships between input variables and output variables, which may be difficult to model using traditional methods. ANNs can also handle highdimensional datasets and can generalize well to new data. However, ANNs require a large amount of labeled data for training and may suffer from overfitting if the model is too complex or the training data is insufficient.

4.2 Genetic algorithms for predicting rock fragmentation Genetic algorithms (GAs) are a type of optimization algorithm that simulate the process of natural selection to find the optimal solution to a problem. GAs use a population of candidate solutions, called individuals, which are represented as strings of binary or real-valued numbers. GAs apply genetic operators, such as mutation and crossover, to the individuals to create new generations of individuals. The fitness of each individual is evaluated based on a fitness function, which measures how well the individual solves the problem. GAs iteratively improve the population until a satisfactory solution is found. GAs have been applied to predict rock fragmentation, by optimizing the blasting parameters to achieve the desired fragmentation outcomes. For example, Monjezi et al. [14] used a hybrid model of an artificial neural network (ANN) and genetic algorithm (GA) to optimize blast parameters in open pit blasting operations. GA produced 32 blast patterns with simultaneous satisfaction of minimizing flyrock and backbreak, with the best pattern having flyrock and backbreak values of 27.96 and 0.71 m, respectively. Shi et al. [40] utilized a genetic algorithm (GA) in their study as a way to improve the accuracy of predicting mean particle size (K50) using a backpropagation neural network (BPNN). The authors found that the BPNN model improved by the genetic algorithm (GA-BP) achieved a relative error of only 3.09%. One advantage of GAs is their ability to explore a large search space and find the optimal solution, even in the presence of noise and uncertainty. GAs can also handle non-linear and non-convex optimization problems, which are common in rock fragmentation prediction. However, GAs require a well-defined fitness function and may converge to a local minimum instead of the global minimum, depending on the initialization and the operators used.

4.3 Machine learning for predicting rock fragmentation Machine learning is a subfield of AI that focuses on the development of algorithms that can learn from data and make predictions or decisions based on that data. Machine learning algorithms can be categorized into two main types: supervised learning and unsupervised learning.

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Supervised learning involves the use of labeled data to train a model to make predictions or decisions. In the context of rock fragmentation prediction, supervised learning algorithms can be used to train a model to predict fragmentation outcomes based on input variables, such as rock properties and blasting parameters. Various supervised learning algorithms have been applied to rock fragmentation prediction, such as decision trees, random forests, support vector machines (SVM), and artificial neural networks. Decision trees are a type of supervised learning algorithm that use a tree-like structure to model decisions and their possible consequences. Random forests are ensemble-learning methods that use multiple decision trees to improve prediction accuracy and reduce overfitting. Support vector machines are a type of supervised learning algorithm that use a hyperplane to separate data into different classes. Artificial neural networks are a type of supervised learning algorithm that simulate the structure and function of biological neurons to learn complex patterns and relationships in data. Several studies have reported the successful application of machine learning algorithms for predicting rock fragmentation. For example, Yu et al. [74] used a random forest algorithm to predict fragmentation outcomes based on rock properties and blasting parameters, achieving an accuracy of 83%. Xie et al. [75] combined a machine learning algorithm called gradient boosting machine (GBM) with the firefly algorithm—FFA, to predict the size of rock distribution in mine blasting to optimize blasting parameters. According to the authors, adjusting blasting parameters can optimize the effective use of explosive energy while minimizing the generation of oversized rocks and excessive fragmentation, thereby reducing the adverse impacts on the surrounding environment. One advantage of machine learning algorithms is their ability to capture complex relationships between input variables and output variables, even in the presence of noise and uncertainty. Machine learning algorithms can also handle large and high-dimensional datasets, which are common in rock fragmentation prediction. However, machine learning algorithms require a large amount of labeled data for training, which may be difficult to obtain in some cases. Moreover, machine learning algorithms may suffer from overfitting, where the model performs well on the training data but poorly on new, unseen data.

4.4 Hybrid approaches for predicting rock fragmentation Hybrid approaches combine multiple machine learning algorithms or optimization techniques to improve prediction accuracy and reduce overfitting. Hybrid approaches can also leverage the strengths of different algorithms to handle different types of input variables or data distributions. Several studies have reported the successful application of hybrid approaches for predicting rock fragmentation. For example, Armaghani [76] used a hybrid approach of imperialism competitive algorithm (ICA) and artificial neural network (ANN) to solve the shortcomings of ANN itself for the

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prediction of rock fragmentation outcomes. The author claimed that the results of the evaluation demonstrated that the ICA-ANN model surpassed the ANN model in terms of both training and testing, as evidenced by the R2 values of 0.949 and 0.813 for ICA-ANN and 0.941 and 0.819 for ANN models, respectively. The developed ICA-ANN hybrid model achieved VAF values close to 100 (94.573 and 94.082 for train and test, respectively). Zhou et al. [77] explored the effectiveness of using an adaptive neuro-fuzzy inference system (ANFIS) for predicting the particle size distribution of a muck-pile after blasting in the mining industry. The researchers optimized the premise and consequent parameters of ANFIS using both the firefly algorithm (FFA) and the genetic algorithm (GA). They compared the accuracy of the ANFIS-FFA and ANFIS-GA models with ANFIS, support vector regression (SVR), and artificial neural network (ANN) models, and evaluated their efficiency in predicting fragmentation on different time scales. Results from 88 blasting events of two quarry mines in Iran indicated that both ANFIS-FFA and ANFIS-GA models performed well, but the ANFIS-GA model had the lowest root mean square error (RMSE) and the highest R2 values, with R2 and RMSE values of 0.989 and 0.974, respectively. One advantage of hybrid approaches is their ability to combine the strengths of different algorithms or techniques to achieve better performance than individual algorithms or techniques. Hybrid approaches can also handle complex and high-dimensional datasets, by partitioning the data into subsets that are best handled by different algorithms or techniques. However, hybrid approaches may be more complex and computationally intensive than individual algorithms or techniques and may require more resources for training and evaluation. There are also some other unique approaches for predicting the blastability and rock fragmentation used by various authors as described in Table 3. TABLE 3 Unique approaches of analysis for predicting blastability and rock fragmentation. Citation

Methods

Description

Zhou and Li [78]

Unascertained measurement (UM Model)

A method for dealing with incomplete or uncertain data by assigning values to upper and lower bounds

Information entropy theory

A method for measuring the amount of uncertainty in a system or dataset

Matter-element analysis

A method for evaluating the quality of a system or object based on multiple criteria or attributes

Blastability evaluation system

A system for integrating the above methods and parameters to assess the blastability of a rock mass

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TABLE 3 Unique approaches of analysis for predicting blastability and rock fragmentation—cont’d Citation

Methods

Description

Ren et al. [79]

Set Pair Analysis (SPA)

A novel uncertainty analysis method used to handle the uncertainty and complexity of rock mass blastability classification

SPA-based distance measure

Used to calculate the similarity degree between the target rock mass and the benchmark rock masses

SPA-based decisionmaking algorithm

Used to classify the rock mass blastability into four categories based on the similarity degree

Monte Carlo simulation

A measure of the ease of blasting a rock mass, based on rock mass properties such as hardness, density, and jointing.

Sensitivity analysis

A method for analyzing how changes in one or more input parameters affect the output of a model. This study is used to identify the most important rock mass parameters for blastability assessment.

Discrete Fracture Network (DFN)

A non-parametric model is used to represent the complex geometry of natural fractures in rock mass.

Stochastic algorithm

A method used to simulate fracture network patterns based on statistical properties of the fractures.

Intersection algorithm

A method used to calculate the intersections between fractures in the DFN.

Bameri et al. [45]

Go´mez et al. [80]

Table 3 presents a range of methods and parameters used to predict rock fragmentation and blastability, all of which involve various forms of artificial intelligence. The diversity of methods indicates the complexity of predicting blastability and fragmentation and the need for diverse approaches to address the range of uncertainties and complexities inherent in this problem. The first method presented in Table 3 is the unascertained measurement (UM) model Zhou and Li [78], which uses information entropy theory and matter-element analysis to deal with incomplete or uncertain data by assigning values to upper and lower bounds. The blastability evaluation system integrates the UM model with other parameters to assess the blastability of a rock mass. The use of the UM model is notable as it allows for the incorporation of uncertainty into the blastability assessment process, an essential feature for any model used to predict rock fragmentation and blastability.

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Another method by Ren et al. [79], the set pair analysis (SPA), is used to handle the uncertainty and complexity of rock mass blastability classification. The SPA-based distance measure is used to calculate the similarity degree between the target rock mass and benchmark rock masses, while the SPA-based decision-making algorithm is used to classify rock mass blastability into four categories based on the similarity degree. The use of the SPA is interesting as it provides a novel uncertainty analysis method for rock mass blastability classification. The Monte Carlo simulation and sensitivity analysis are also used by Bameri et al. [45] to assess the blastability of a rock mass by identifying the most important rock mass parameters. The use of these methods is particularly notable as they provide a measure of the ease of blasting a rock mass based on rock mass properties such as hardness, density, and jointing. These methods also allow for a more accurate assessment of the blastability of a rock mass by identifying the most important rock mass parameters and their impact on blastability. The Discrete Fracture Network (DFN) by Go´mez et al. [80] is a nonparametric model used to represent the complex geometry of natural fractures in a rock mass. The DFN model is particularly useful for studying the response of rock masses to different loading conditions, such as blasting, seismic events, or mining operations. The stochastic algorithm is used in the DFN model to simulate fracture network patterns based on the statistical properties of the fractures. The algorithm is based on the assumption that fractures in a rock mass are randomly distributed and follow a specific statistical distribution. The stochastic algorithm generates a set of fractures that are statistically similar to the fractures observed in the field. This approach is useful for generating synthetic fracture networks that can be used to study the behavior of rock masses under different loading conditions. The intersection algorithm is a key component of the DFN model, as it allows the fracture network to be represented as a set of interconnected fracture segments. The intersection algorithm takes into account the orientation and geometry of the fractures and calculates the intersections between them. This information is then used to generate a three-dimensional representation of the fracture network, which can be used to simulate the behavior of the rock mass under different loading conditions. Overall, the methods and parameters used in these studies show the importance of incorporating uncertainty and complexity into the prediction of rock fragmentation and blastability. These methods highlight the challenges of predicting blastability and fragmentation but also demonstrate the potential of using artificial intelligence to address these challenges. By using a range of methods and parameters, researchers can gain a more comprehensive understanding of the blastability and fragmentation of a rock mass, and therefore improve the safety and efficiency of blasting operations in mining and other industries.

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Challenges and future directions

Despite the promising results of machine learning algorithms and optimization techniques for predicting rock fragmentation, several challenges and future directions need to be addressed to improve their applicability and effectiveness. Firstly, there is a need for more data to train and evaluate the algorithms, especially for rare or extreme fragmentation outcomes. The quality and diversity of the data also need to be ensured, to avoid biases and errors in the prediction models. Secondly, the interpretation and visualization of the prediction models need to be improved, to facilitate the understanding and communication of the results to the stakeholders. The models should also be robust and transparent, to avoid unexpected or undesirable consequences in practice. Thirdly, the integration of the prediction models with the blasting planning and execution systems needs to be ensured, to enable real-time feedback and adjustment of the blasting parameters based on the predicted fragmentation outcomes. The optimization of the blasting parameters should also consider the environmental and social impacts, such as noise, dust, and vibration. Several basic challenges with respect to the data used in generating AI prediction models are given in Table 4.

TABLE 4 Main challenges in RMC in terms of data used in the AI models. Main challenges

Precise challenges

Data collection

Data quality

It can be affected by various factors such as drilling and blasting techniques, weather conditions, and rock characteristics. It is essential to ensure that the data collected is representative and free of errors.

Collecting input data

Collecting data on rock strength may require drilling and core sampling, while collecting data on moisture content may require specialized sensors and probes.

Collecting output data

Collecting data on output parameters can be challenging, especially if the mining operation is still in the planning or early stages. For example, measuring fragmentation size may require specialized equipment, such as a digital camera, or manual measurements using a ruler or tape measure.

Data diversity

The geological and engineering parameters that affect blastability can vary widely between mines and even within the same mine, making it difficult to obtain a comprehensive dataset.

Data labeling

Labeling rock mass characteristics can be difficult, as the boundaries between different rock types may be unclear or subject to interpretation.

Problems

Continued

TABLE 4 Main challenges in RMC in terms of data used in the AI models—cont’d Main challenges

Data validity and repeatability

Data logging

Precise challenges

Problems

Handling missing data

Incomplete or missing data can affect the accuracy and reliability of models. Addressing missing data requires careful consideration and may involve imputation techniques, such as mean or median imputation, or advanced statistical methods, such as multiple imputation.

Sampling bias

Bias in the sampling process can lead to nonrepresentative data and affect the accuracy of the models. For example, if samples are taken from a specific area of the mine, the model may not accurately predict blastability in other areas.

Measurement errors

Errors in the measurement of input and output parameters can affect the validity of data and result in inaccurate predictions. For example, errors in measuring rock strength can lead to incorrect predictions of blastability.

Data quality

Poor data quality, such as missing data, outliers, or errors, can affect the validity of data and lead to inaccurate predictions. Data quality control procedures, such as outlier detection and data imputation, are necessary to ensure the accuracy and reliability of the data.

Model overfitting

Overfitting occurs when the AI model fits the training data too closely, resulting in poor performance on new data. Overfitting can be a challenge in blastability prediction models, where the range of input parameters and blastability outcomes can be highly variable.

Model validation

Validating the AI model requires testing its performance on new data that were not used to train the model. Model validation can be challenging in the context of blastability prediction, as collecting new data can be time-consuming and expensive.

Real-time data logging

It is essential to collect data in real-time to capture these changes accurately. Real-time data logging can be challenging, as it requires specialized equipment and personnel and can be expensive.

Data volume

Collecting and managing large volumes of data can be challenging, as it requires adequate storage and computing resources.

Data integration

Integrating data from different sources can be challenging, as data may be in different formats and require pre-processing to be usable.

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Table 4 shows that the AI prediction models rely heavily on the quality and quantity of input data, and may not perform well in cases where data is limited or of poor quality. Additionally, these models may not capture all the factors that affect blastability, such as human factors or environmental conditions. Therefore, while AI prediction models of blastability can be a useful tool for logical analysis, they should be used in conjunction with other analytical methods and expert knowledge to ensure accurate and reliable results. Despite the fact that a lot of work has been carried out on blastability, the crisp and universally acceptable is not still available. It needs to be seen whether it can be related to a single factor or has to be a composite of several independent rock mass properties. Lastly, the application of machine learning algorithms should be explored in detail for blast fragmentation prediction and process optimization. The algorithms and techniques should also be adapted to different types of rocks and geological settings, to ensure their generalizability and transferability.

6

Conclusion

The chapter briefly introduces and summarizes the conventional methods of predicting rock blastability and fragmentation. The trends in the application of AI in blast outcome prediction are outlined and a survey of literature is presented where AI has been used for fragmentation and blastability prediction. Salient details of such works have been presented. It is observed that AI does not find extensive use in fragmentation and blastability prediction but several publications though limited in nature have tried to predict these blast outcomes in terms of ANNs, SVMs, decision trees, GAs, and hybrid approach methods. Some non-conventional methods like unascertained measurement, information entropy theory, matter-element analysis, blastability evaluation system, set pair analysis (SPA), SPA-based distance measure, SPA-based decision-making algorithm, Monte Carlo simulation, sensitivity analysis, discrete fracture network (DFN), stochastic algorithm, and intersection algorithm have also been tried for the purpose. In the acquisition process, the collection of high-quality, diverse, and representative data is crucial for the success of AI prediction models in blastability. Careful planning, data quality control, and validation are critical to ensuring the accuracy and reliability of the AI model. Machine learning algorithms and optimization techniques have the potential to revolutionize the blasting industry by enabling more precise and optimal blasting outcomes, reducing waste and environmental impacts, and increasing safety and productivity. It may not be out of place to mention that further research and development is needed to address the challenges and limitations of these algorithms and techniques, and to enhance their integration with the blasting planning and execution systems. Collaboration among researchers, practitioners, and stakeholders

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is essential to ensure the practicality and acceptability of these methods in the blasting industry. In conclusion, the application of artificial intelligence in predicting rock fragmentation is a rapidly evolving field with great potential for innovation and impact. It offers a new paradigm for optimizing blasting outcomes and improving the sustainability and efficiency of rock engineering.

Acknowledgments The authors are thankful to the Director CSIR-CIMFR for his permission to publish this book chapter. The grant from the Ministry of Mines (GoI), F. No. Met4-14/18/2022 is gratefully acknowledged.

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Chapter 17

Underground stope dilution optimization applying machine learning Hyongdoo Jang and Erkan Topal Faculty of Science and Engineering, Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Bentley, Perth, WA, Australia

1

Introduction

Modern mining is more efficient than ever with various cutting-edge mining technologies. However, there are still a lot of problems that need to be resolved in practical mining activities. In mine sites, mining engineers frequently face situations where they must make decisions without complete and accurate information. Since even a minor mistake could directly harm the miners’ well-being as well as the mine’s profitability irreparably, proper decision-supporting systems are essential. When limiting the scope to underground mining, unplanned dilution and ore loss (uneven break: a pair of inevitable and unpredictable phenomena) are considered the most challenging problem. The importance of planned dilution and ore loss cannot be overlooked that should be soundly determined in the stope planning stage (Fig. 1B). In constant with the planned uneven break (planned dilution and ore loss) which are the controllable losses, the level of unplanned uneven break is difficult to predict but can be measured after stope production (Fig. 1D)—which leads to tremendous loss by surging over broken barren into the ore stream. Despite the tremendous efforts to control the uneven break, many underground stoping mines are suffering from the excessive level of uneven break due to its complex causing mechanism. Although several empirical methods have been employed in mines to predict unplanned dilution, the performance of these methods is currently unsatisfactory and unable to predict ore loss. Currently, many underground mines operating stoping methods rely on the stability graph method [2,3] which is not an overbreak management method but an empirical stope design approach that evaluates the stability of stope in underground by plotting a stability number (N) against a hydraulic radius of a stope Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00006-0 Copyright © 2024 Elsevier Inc. All rights reserved.

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FIG. 1 Demonstration of planned and unplanned dilution and ore loss in underground stoping mine [1]. (A) Determined ore vein, (B) Stope design, (C) Stope blasting design, (D) Stope reconciliation after extraction.

wall. Mines are obliged to use the stability graph method to predict potential uneven break and determine their target over and underbreak percentages in the stope design stage. However, the performance of uneven break prediction through the stability graph method is usually unreliable. Jang et al. [4] investigated three underground stoping mines in Western Australia and reported that the correlation coefficient (R) for the target unplanned dilution determined based on the stability graph method and the measured unplanned dilution resulted in 0.31. In general, the uneven break causative factors can be categorized into four groups: geological factors, stope design factors, blasting factors, and operational errors. Numerous researchers have worked on identifying geological factors that contribute to unplanned dilution and ore loss. However, due to the inherent complexity of the anisotropic and heterogeneous features of the rock mass, defining these factors and their impact weights is a challenging task. Blast design factors are also the key factors to uneven break phenomenon, but the consequences of it are often unpredictable due to the nature of the complex rock breakage process under extreme explosion energies. This unpredictability is partly due to the dynamic explosion process, which generates shock waves and gas pressure within a matter of milliseconds following the exothermic chemical reaction of the explosion. Alongside the two sets of uneven break causative factors, improper stope design, including the incorrect shape, size, and sequence of excavation, can lead to a significant uneven break. Stope design should be carried out with great care considering the geological condition and geotechnical analyses of the subjected stope area. Human errors are common issues in mining operations which can exacerbate the effects of other unfavorable geological conditions on unplanned dilution and ore loss. Although modern autonomous drilling machines ensure better operational performances, drilling with perfect precision remains a significant challenge. This is mainly due to the complex geological features of the natural rock mass that can hinder drilling

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activities, including precise collar location, straight-line drilling without deviations, and reaching exact depths. There are several low-dimensional parametric studies have been carried out to reveal the causing mechanism of overbreak and underbreak (so-called uneven break). Germain and Hadjigeorgiou [5] conducted a study to investigate the effects of ground conditions and stope blasting design factors, specifically rock mass quality (Q) and powder factor (PF), on overbreak. The study found that the correlation coefficients of PF and Q against stope performance were
0.083 and 0.282, respectively. These results suggest that the mechanism causing overbreak is highly complex. The distribution of underground stress resulting from mining activities is a well-known cause of uneven break. Stewart et al. [6] analyzed stope reconciliation at the Kundana Gold mine in Western Australia and reported that over 50% of the observed overbreak occurred in the stope wall where the induced stress exceeded the damage criterion. While some studies have investigated the effect of stope design parameters on uneven break, few have explored the mutual interactions or correlations among different parameters. Henning and Mitri [7] investigated the overbreak phenomenon in mines that utilized the blasthole mining method to excavate tabular-shaped orebodies. Their study found that overbreak was more likely to occur in large, square-like stopes than in vertical or horizontal, long squared stopes. Additionally, overbreak was more likely to occur in secondary stopes with one or more backfilled walls. Overall, these parametric studies have contributed to our understanding of how uneven break occurs with regard to individual parameters. However, the underlying mutual interactions and correlations among different parameters have not been fully revealed.

2 Applications of machine learning in underground stope dilution optimization Optimizing stope dilution is a critical aspect of mining operations, as it can have a significant impact on a mine’s profitability and efficiency. However, the process is challenging because it involves considering a multitude of known and unknown factors, including geological and geotechnical conditions, mining methods, and stope production design sequences, along with their mutual interactions. To address these challenges, machine learning can be used in underground stope dilution optimization to predict potential over and underbreak, support decision-making processes, and potentially establish an automated stope optimization system. By utilizing machine learning, miners can gain a better understanding of the complex factors that affect stope dilution and develop more effective strategies to optimize their mining processes. This promising approach could significantly improve mining operations, helping to enhance efficiency and reduce costs.

318 Applications of artificial intelligence in mining and geotechnical engineering

2.1 Feature range and selection Table 1 summarizes a range of studies that have used different AI methodologies and feature sets to optimize stope dilution. One critical aspect of these studies is feature selection, as the chosen method only establishes models within the given feature dimension. Therefore, it is important to consider the full range of potential contributing factors when developing a model to optimize stope dilution. However, the approach of incorporating the full range of uneven break causative factors may lead to increased model complexity, potentially affecting model significance. Additionally, comprehensive data collection may be challenging due to data access restrictions and limited data sources. These challenges may be addressed with improved computational capabilities and data management programs. To date, very few studies have been successful in developing a full range of uneven break causative factors. Focusing on the underground production stoping mines, Jang et al. [1,4,17] adopted 10 uneven break causative factors that encompassed key factors from all four uneven break causing factor categories—geological, stope design, blasting, and operational errors. A general uneven break prediction model from the study resulted in a correlation coefficient (R) of 0.719 and showed a high increment of the model significance by developing a mine-specific model that resulted in an R of 0.994. Qi et al. [13] attempted to compare different AI methods to predict hanging wall stability, applying various stope design and geotechnical parameters. The study showed the high level of performance of AI methods to predict hanging wall stability. Recently, Erdogan Erten et al. [9] studied to predict stope stability applying an ANN-based method and compared the results with k-nearest neighbor (kNN), naive Bayes (NB), support vector machine (SVM), decision tree (DT), and the stability graph method. The study also showed excellent applicability of AI methods to predict stope stability. However, the approach of both studies may be difficult to apply in real mining practice as they have not considered blasting design and possible operational error factors that could exacerbate uneven break in a stope. In some instances, mines may face exceptional scenarios where the occurrence of uneven break is attributed to one or two specific factors. In such cases, the complexity of the problem can be reduced and verified with one- or twodimensional regression AI modeling methodologies. For example, if a mine operates in adverse blocky rock conditions for the hanging wall, the geological condition of the hanging wall can be considered the primary contributing factor for any potential failure from that direction. Similarly, directional overbreak may be observed during the development drift blasting in certain underground block caving mines. In such cases, it may be worthwhile to investigate the impact of excessive directional stresses on the consistently directional overbreak phenomenon. When developing a prediction model for uneven break in low-dimensional cases, it is important to thoroughly investigate the effects of other causative factors to validate the proposed model.

TABLE 1 Studies of AI and machine learning applications to uneven break in tunnel and stope production blast. Author

Method

Subject

Features

Results

Bazarbay and Adoko [8]

FIS

Stope— dilution

N0 , HR

FIS 84% of accuracy

Erdogan Erten et al. [9]

ANN, kNN, NB, SVM, DT, SGM

Stope— stability

Stope surface, strike length, exposed height, ore width, HR, N0 , depth of excavation

The accuracy values of the stability status of either stable or unstable. ANN: 91.63%, SVM: 81.86%, DT: 77.21%, NB: 76.74%, kNN: 73.95%, and SGM: 69.77%

Zhao and Niu [10]

ANN

Stope— ELOS

N0 , HR, average borehole deviation, PF

R2 of 0.976

Jang et al. [11]

ANN

Tunnel

Face mapping information, UCS, discontinuities frequencies, condition, and attack angle to tunnel contour

ANN model—R of 0.879–0.766 in testing. Defines overbreak resistance factor

Koopialipoor et al. [12]

ANN, GA-ANN

Tunnel

ND, SD, B, S, PF, AL, RMR

ANN: 0.639, GA-ANN: 0.881 (values in R2 on testing)

Qi et al. [13]

LR, ANN, DT, GBM, SVM, FA

Stope

Stope dip, strike width, stope heights, RQD, Jn, Ja, Jr., SGM factor A, B, and C, stope design, stress category, undercut area

GBM: 0.855, SVM: 0.816, and LR: 0.801 (values in RUC on testing)

Mottahedi et al. [14]

ANN, FL, ANFIS, SVM

Tunnel

Blasting area,, PB, SC (PF), SD, CB, TA, RMR

ANN: 0.93, FL: 0.96, ANFIS: 0.97, SVM: 0.87 (values in R2 on testing)

Jang et al. [1]

CWA and PM to ANN

Stope

AQ, K, AsR, SbR, Pt, Pf, Bdia, Blen, BTBL, AHW

AQ results the highest contribution to stope uneven break with 20.48%

Bahri et al. [15]

FIS

OSD

VST, seam thickness, dip and depth, RMR, HR

R2 of 0.970 Continued

TABLE 1 Studies of AI and machine learning applications to uneven break in tunnel and stope production blast—cont’d Author

Method

Subject

Features

Results

Mohammadi et al. [16]

Fuzzy set theory

Tunnel

PF, SD, NC, CB, LS, JO, RMR

R2 of 0.960

Jang et al. [17]

Neurofuzzy

Stope

AQ, K, AsR, SbR, Pt, Pf, Bdia, Blen, BTBL, AHW

PFCR and GSCR from the fuzzy system

Jang et al. [4]

MLRA, MNRA, ANN

Stope

AQ, K, AsR, SbR, Pt, Pf, Bdia, Blen, BTBL, AHW

MLRA: R of 0.412–0.584

Shaorui et al. [18]

WNN

Tunnel

TL, S, DO

10%–30% difference between the prediction and the real volume of overbreak blocks

Jang and Topal [19]

MLRA, MNRA, ANN

Tunnel

Sc, RQD, Js, Ja, Jw, RMR

MLRA: R2 of 0.694

MLRA: R of 0.425–0.607 ANN: R of 0.719–0.994 in testing

MNRA: R2 of 0.708 ANN: R2 of 0.948 in testing

Methods and statistic factors: R, Correlation coefficient; MLRA, multiple linear regression analysis; MNRA, multiple non-linear regression analysis; ANN, artificial neural network; WNN, wavelet neural network; FL, fuzzy logic; FIS, fuzzy inference system; ANF, adaptive neuro-fuzzy inference system; SVM, support vector machine; kNN, knearest neighbor; NB, Naive Bayes; DT, decision tree; SGM, stability graph method; LR, logistic regression; GBM, gradient boosting machine; FA, firefly algorithm; CWA, connection weight algorithm; PM, profiling method; AUC, area under the ROC (the receiver operating characteristic curve) curve; RF, random forest; PSO, particle swarm optimization. Parameters: AQ, Adjusted Q; K, horizontal to vertical stress ratio; AsR, stope aspect ratio; SbR, space and burden ratio; Pt, planned tons of stope; Pf, powder factor; Bdia, blasthole diameter; Blen, average length of blasthole; BTBL, breakthrough of a stope; AHW, angle difference between the blasthole and stope wall; PFCR, powder factor control rate; GSCR, ground support control rate; Sc, unconfined strength of rock; RQD, rock quality designation; Js, spacing of joints; Ja, state of joints (join t set alteration number); Jo, spatial orientation of joints; Jw, ground water condition; Jn, joint set number; Jr, joint set roughness; TL, trace length; S, spacing of structural planes; PB, ratio of amount of charge in contour holes to the contour holes burden value (kg/m); DO, discontinuous orientation; SD, specific drilling; NC, ratio of number of contour holes to the total number of holes; CB, ratio of the amount of charge in contour holes to the burden in contour; LS, ratio of the length of holes to the stemming; JO, joint orientation favorability; ND, number of delays; B, burden; AL, advance length; HR, hydraulic radius; N0 , stability number from SGM; ELOS, equivalent linear overbreak slough; OSD, out of seam dilution in coal mine; VST, variation in seam thickness.

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2.2 Studies applied AI methods Artificial neural network (ANN) is the most commonly used AI method in underground dilution optimization studies. However, when expanding the scope to include uneven break in both underground stope and tunnel, Jang and Topal’s [19] study was the first to attempt predicting overbreak using AI. Their study only considered uncontrollable geological parameters, such as Sc, RQD, Js, Ja, Jw, and RMR, to predict the depth of the uneven break around a tunnel contour. The results found excellent applicability of ANN to overbreak prediction study by achieving an R2 (coefficient of determination) of 0.948. Since then, many researchers have used ANN to optimize tunnel overbreak studies. Shaorui et al. [18] used wavelet neural network (WNN) models, which is a type of ANN based on wavelet analysis, to predict overbreak in a tunnel in China. The study also only used geological factors, such as trace length, spacing of structural planes, and discontinuous orientation, to predict overbreak. The results showed a prediction accuracy of 70–90% between the projected outcomes and actual observations. Mottahedi et al. [14] also utilized ANN with other AI methods such as FL, ANFIS, and SVM to predict percentage of overbreak in tunnel using 267 datasets that included 5 selected blast design and geological factors (as shown in Table 1). Koopialipoor et al. [12] also conducted similar study that applied ANN to predict the area of overbreak by analyzing 406 datasets of blasting parameters with RMR and tunnel advance length. ANN models from both studies showed significance performances of R2 of 0.960–0.881 in testing stage. A pragmatic overbreak management system has been introduced by Jang et al. [11]. The overbreak resistance factor (ORF) from the study was developed based on five tunnel overbreak predicting ANN models and their sensitivity analysis. The ORF provides ranges of possible overbreak and underbreak based on the geological conditions of the tunnel face. The use of artificial neural networks (ANNs) in predicting stope dilution has been studied by Jang et al. [4]. The study collected 1067 datasets from 3 underground stoping mines in Western Australia that included the full range of uneven break causative factors. Another study by Qi et al. [13] also utilized ANN and other AI methods such as logistic regression (LR), decision tree (DT), gradient boosting machine (GBM), and support vector machine (SVM) to predict hanging wall stability. The hyperparameters were tuned using the firefly algorithm (FA) and geological parameters were used as inputs. The proposed models were evaluated using the AUC values which ranged from 0.783 for ANN to 0.882 for GBM. Recently, ANN has been utilized to predict stope stability prediction Zhao and Niu [10] attempted to predict equivalent linear overbreak slough (ELOS) applying 120 datasets of the modified stability number (N0 ), hydraulic radius (HR), average deviation of the borehole, and powder factor as input variable. The proposed ANN model from the study resulted R2 of 0.976 that confirmed applicability of the ANN to dilution management in underground stoping mine. Erdogan Erten et al. [9] aimed to develop ANN model to predict stope stability

322 Applications of artificial intelligence in mining and geotechnical engineering

applying stope surface, strike length, exposed height, ore width, HR, N0 , and depth of excavation as input parameters. The model was developed by training 215 stope cases that achieved 91.63% of accuracy with the proposed hybrid grid search-based ANN method which outperformed than other AI methods. There are few studies applying fuzzy logic and its delivered methods to predict unplanned dilution. Fuzzy expert system (FES) has been utilized by Jang et al. [17] to develop uneven break consultation system that determine the level of powder factor and ground support based on predicted overbreak from ANN model and geological condition of the stope. The membership functions of the FES model were developed by surveying results of 15 underground mining experts. Bazarbay and Adoko [8] also utilized FES (also noted as FIS: fuzzy inference system). The proposed FES model has been developed using two membership functions—HR, N0 —to determine the level of dilution that showed classification accuracy of 84%.

3 Conclusions Effective dilution control is essential to ensure miners’ safety and protect the profitability of the mine. The current empirical methods such as stability graph method with ELOS, employed in mines to predict the uneven break, are unsatisfactory that often leading excessive levels of uneven break as the inherent complexity of the mutual interactions and correlations among different parameters. ANNs can effectively predict uneven break and its causes as verified by many studies. Care must be in place when constructing the structure of the model and the feature selection as they are critical aspects that affect the performance of ANN model. In this regard, using another AI to determine hyperparameters can enhance the accuracy of the ANN. As shown from few previous studies, fuzzy inference system (FIS) can model uncertainty and imprecision in uneven break prediction. Membership functions and if-then rules are critical components of a fuzzy inference system that must be established by mining experts. The application of AI to optimize uneven break has promising future perspectives. For example, machine learning algorithms, such as random forests and support vector machines, could be employed to complement ANNs and FIS. Additionally, deep learning models, such as convolutional neural networks (CNN), can be used to enhance the accuracy of uneven break prediction. These models could also be used to predict other mining-related phenomena, such as rock mass stability and fragmentation as well. In conclusion, the development of advanced decision-support systems based on AI is essential to effectively manage uneven break in underground mining. ANN and fuzzy inference systems have shown promise in this regard, but further research is needed to fully understand the mutual interactions and correlations among different parameters that contribute to uneven break.

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References [1] H. Jang, E. Topal, Y. Kawamura, Illumination of parameter contributions on uneven break phenomenon in underground stoping mines, Int. J. Min. Sci. Technol. 26 (6) (2016) 1095–1100. [2] K.E. Mathews, E. Hoek, D.C. Wyllie, S. Stewart, Prediction of stable excavation spans for mining at depths below 1000 m in hard rock, 1981. CANMET DSS Serial No: 0sQ8000081, Ottawa. [3] Y. Potvin, Empirical Open Stope Design in Canada, University of British Columbia, Vancouver, Canada, 1988. [4] H. Jang, E. Topal, Y. Kawamura, Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses, J. South. Afr. Inst. Min. Metall. 115 (2015). [5] P. Germain, J. Hadjigeorgiou, Influence of stope geometry and blasting patterns on recorded overbreak, Int. J. Rock Mech. Min. Sci. 34 (3) (1997) 115.e111–115.e112, https://doi.org/ 10.1016/S1365-1609(97)00219-0. [6] P. Stewart, J. Slade, R. Trueman, The effect of stress damage on dilution in narrow vein mines, in: 9th AusIMM Underground Operators Conference 2005, 2005. [7] J.G. Henning, H.S. Mitri, Numerical modelling of ore dilution in blasthole stoping, Int. J. Rock Mech. Min. Sci. 44 (5) (2007) 692–703. [8] B. Bazarbay, A.C. Adoko, Development of a knowledge-based system for assessing unplanned dilution in open stopes, IOP Conf. Ser. Earth Environ. Sci. 861 (6) (2021) 062086, https://doi. org/10.1088/1755-1315/861/6/062086. [9] G. Erdogan Erten, S. Bozkurt Keser, M. Yavuz, Grid search optimised artificial neural network for open stope stability prediction, Int. J. Min. Reclam. Environ. 35 (8) (2021) 600–617. [10] X. Zhao, J.A. Niu, Method of predicting ore dilution based on a neural network and its application, Sustainability 12 (4) (2020) 1550. https://www.mdpi.com/2071-1050/12/4/1550. [11] H. Jang, Y. Kawamura, U. Shinji, An empirical approach of overbreak resistance factor for tunnel blasting, Tunn. Undergr. Sp. Technol. 92 (2019) 103060. [12] M. Koopialipoor, D. Jahed Armaghani, M. Haghighi, E.N. Ghaleini, A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels, Bull. Eng. Geol. Environ. 78 (2) (2019) 981–990, https://doi.org/10.1007/s10064-017-1116-2. [13] C. Qi, A. Fourie, G. Ma, X. Tang, X. Du, Comparative study of hybrid artificial intelligence approaches for predicting hanging wall stability, J. Comput. Civ. Eng. 32 (2) (2018) 04017086. [14] A. Mottahedi, F. Sereshki, M. Ataei, Development of overbreak prediction models in drill and blast tunneling using soft computing methods, Eng. Comput. 34 (1) (2018) 45–58, https://doi. org/10.1007/s00366-017-0520-3. [15] N.A. Bahri, F.M.A. Ebrahimi, S.G. Reza, A fuzzy logic model to predict the out-of-seam dilution in longwall mining, Int. J. Min. Sci. Technol. 25 (1) (2015) 91–98. [16] M. Mohammadi, M.F. Hossaini, B. Mirzapour, N. Hajiantilaki, Use of fuzzy set theory for minimizing overbreak in underground blasting operations—a case study of Alborz Tunnel, Iran, Int. J. Min. Sci. Technol. 25 (3) (2015) 439–445, https://doi.org/10.1016/j. ijmst.2015.03.018. [17] H. Jang, E. Topal, Y. Kawamura, Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system, Appl. Soft Comput. 32 (2015) 1–12. [18] S. Shaorui, L. Jiaming, W. Jihong, Predictions of overbreak blocks in tunnels based on the wavelet neural network method and the geological statistics theory, Math. Probl. Eng. 2013 (2013) 706491, https://doi.org/10.1155/2013/706491. [19] H. Jang, E. Topal, Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network, Tunn. Undergr. Sp. Technol. 38 (2013) 161–169.

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Chapter 18

Applying a novel hybrid ALO-BPNN model to predict overbreak and underbreak area in underground space Chuanqi Lia,b, Daniel Diasa, Jian Zhoub, and Ming Taob a

Laboratory 3SR, CNRS UMR 5521, Grenoble Alpes University, Grenoble, France, bSchool of Resources and Safety Engineering, Central South University, Changsha, China

1

Introduction

Rock excavation is a necessary engineering project in underground mine construction such as laneway and chamber excavation. Unlike urban tunnel construction using advanced tunnel boring machines (TBM) technology, almost all underground mines, and tunnels in remote areas have used drilling and blasting technology as the core excavation approaches. Nevertheless, due to the particularity and uncontrollability of explosives, it is difficult to reach the space size and shape required by the design scheme in the actual excavation construction on site [1]. Therefore, the adverse phenomenon of overbreak and underbreak have been common. The overbreak is defined as a surplus drilled section of the workforce and the underbreak is defined as the remainder of the blast operation [2]. The adverse consequences caused by overbreak and underbreak are different. Overbreak means that the original safe space is missing part of the rock and increases the instability, putting workers and equipment at risk. The quality of the ore in the mine is diluted if the missing rock contains high-grade ore [3]. And the consequences of underbreak are more to cause producing operational ore losses [4]. Therefore, it is necessary to explore an accurate prediction and effective control method of overbreak and underbreak to reduce the loss of mineral resources and reduce operational safety risks in underground space.

Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00021-7 Copyright © 2024 Elsevier Inc. All rights reserved.

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Applications of artificial intelligence in mining and geotechnical engineering

After continuous exploration and experiments, the influencing factors of overbreak and underbreak can be roughly divided into three categories, that is, blasting parameters, geometric characteristics of explosion pattern, and properties of rock mass [5–7]. Furthermore, there is still no suitable empirical or deterministic analysis method to predict overbreak and underbreak. Therefore, many researchers have focused on using artificial intelligence (AI) to predict overbreak and underbreak by considering some of the influencing factors [2,3,8–11]. However, no work has taken all three types of influencing factors into consideration in overbreak and underbreak prediction. In this study, a total of 12 parameters from three categories of influencing factors were collected to predict overbreak and underbreak, including two parameters of geometric characteristics of explosion pattern (depth (H) and the degree of binding of the layered surface (D)), six parameters of blasting (i.e., the total number of holes (N), spacing of perimeter holes (PS), spacing of auxiliary holes (AS), the thickness of glossy blast layer (TH), total charge (TC), charge concentration (CC), charge structure (CS) and the maximum single-hole charge (MC)), two parameters of properties of rock mass (i.e., uniaxial compressive strength (UCS) and rock mass rating (RMR)). In addition, the areas of overbreak and underbreak are used to quantitatively describe the deviation between the actual size and the initial size of excavation, namely, overbreak and underbreak area (OUA). Furthermore, a traditional backpropagation neural network (BPNN) model was optimized by using a meta-heuristic algorithm called the ant lion optimizer (ALO) to predict OUA.

2 Methodologies 2.1 Backpropagation neural network (BPNN) The backpropagation neural network (BPNN) has been a famous improved model of artificial neural network (ANN) for solving various engineering problems induced by blasting [12–15]. The most important characteristic of BPNN is backpropagation algorithm can be used to estimate the weights and biases between input, hidden, and output layers more accurately. Assume that there are m input variables (xi¼1,…,m) and n output variables (yi¼1,…,n), then the BPNN can be expressed by the following mathematical formula: ! m X  1 wj,i xi + bj hj ¼ g gðI Þ ¼ 1 + eI i¼1

I¼

m X i¼1

wj,i xi + bj

yj ¼

n X

, wo,j hj + bo

j¼1

where the meanings of the variables in Eq. (1) are shown in Table 1.

(1)

Applying a novel hybrid ALO-BPNN model Chapter

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TABLE 1 Variables description of BPNN. Variables

Description

hj

The jth neuron in the hidden layer

wj,i

The weights between the ith neuron (input layer) and the jth neuron (hidden layer)

wo,j

The weights between the jth neuron (hidden layer) and the oth neuron (output layer)

bj

Biases in the hidden layer

bo

Biases in the output layer

g(I)

Activation function

Moon

(a)

Ant Trap Antlion

Trap (b)

Antlion

FIG. 1 (A) Cone-shaped traps and (B) hunting behavior of antlions.

2.2 Ant lion optimizer (ALO) Mirjalili [16] developed a meta-heuristic algorithm inspired by the predation behavior of antlions to solve the optimization problem, that is, the ant lion optimizer (ALO). As shown in Fig. 1A, a conical pit is dug in the sand by antlion larvae along a circular path. The antlion then hides at the base of the cone and waits for insects (preferably ants) to fall into the trap. The unique smooth wall design makes it easy for ants to fall to the bottom of the trap. To prevent the ants from escaping the trap, antlions cleverly throw sand to the edge of the cave to encourage prey to the bottom (see Fig. 1B). Once the hunting is over, the antlion repairs the trap. Guided by this philosophy, the ALO can be described by following steps and corresponding the mathematical relationships: I. Trapping of ants: The ants can be caught in a trap built by antlions when searching for food, and then antlions get the signal for hunting. These behaviors can be expressed using the following formulas: pti ¼ XtAntlionz + pt , pt pt ¼ , v

sti ¼ XtAntlionz + st : st st ¼ v

(2)

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Applications of artificial intelligence in mining and geotechnical engineering

II. Catching ants and re-constructing trap: Besieged ants are attacked by antlions. To get more ants, antlions must repair traps to better prevent ants from escaping. Furthermore, antlions are committed to updating their prey in search of better prey. The above behaviors can be described using the following formulas: XtAntlionz ¼ XtAntz XtAnt ¼

    if jfitj XtAntz > jfitj XtAntlionz ,

RtAnt + RtElitismAntlion , 2

(3) (4)

where the meanings of the variables are in Eqs. (2)–(4) are shown in Table 2.

TABLE 2 Variables description of the ALO algorithm. Variables

Description

pti

The minimum of all variables at tth iteration for ith ant

p

t

sti t

The minimum of all variables at the tth iteration The maximum of all variables at tth iteration for ith ant

s

The maximum of all variables at the tth iteration

v

A ratio related to the iteration

XtAntz

The position of ant at tth iteration

XtAntlionz

The position of antlion at tth iteration

RtAnt

The random position of antlion

RtElitismAntlion

The random position of elitism antlion

t

Current iteration

3 Data preparation and performance evaluation In this study, 95 excavation operations have been recorded by Shi et al. [17] in the Pan Long Shan tunnel, Taian City, China. After each blasting, the actual excavation area of the working face was measured and the overbreak and underbreak area (OUA) was calculated manually. Meanwhile, a total of 12 parameters are considered as impact factors related to OUA, including the uniaxial compressive strength (UCS), rock mass rating (RMR), depth

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(H), the degree of binding of the layered surface (D), the total number of holes (N), spacing of Perimeter holes (PS), spacing of auxiliary holes (AS), the thickness of glossy blast layer (TH), the total charge (TC), charge concentration (CC), charge structure (CS), and the maximum single-hole charge (MC). Therefore, these impact factors are used as input variables to predict the unique output variable OUA. The detailed information on input and output variables are shown in Table 3, and the results of the correlation analysis between input variables and output variables are shown in Fig. 2. To obtain an excellent prediction model, the dataset was randomly divided into a train set (80%) and a test set (20%). It should be noted that the same training set and test set were used for training and evaluating prediction models in the training and testing phases, respectively. In addition, all data should be normalized to 1 to 1 before generating the train models, which is an effective approach for reducing the effect of parameter difference on the model performance [18–21]. In this study, four widely used statistical indices are usually used to evaluate the predictive performance of all models, that is, the determination coefficient (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the variance accounted for (VAF), the application of these indices can be found in the literature [22–28]. The definitions of these indices are written as follows: hXn 

  i2  OUA OUA  OUA  OUA m,i m p,i p i¼1 R2 ¼ X n   Xn  ,  OUA  OUA OUA  OUA m,i m p,i p i¼1 i¼1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n  2 1X OUAm,i  OUAp,i , RMSE ¼ n i¼1 MAE ¼

n   1X OUAm,i  OUAp,i , n i¼1

  var OUAm,i  OUAp,i VAF ¼ 1   100, varðOUAm,i Þ

(5)

(6)

(7)



(8)

where n represents the number of samples in the training or testing phase, OUAm, i and OUAm are measured values and mean measured values of OUA, respectively, OUAp, i and OUAp are predicted values and mean predicted values of OUA, respectively.

TABLE 3 Details of the input and output variables. Variables

Units

Minimum

UCS

MPa

31.70

53.60

43.54

45.50

7.40

RMR

/

40.00

50.00

45.37

50.00

5.01

H

m

62.00

148.00

92.97

94.00

18.04

D

/

0.30

1.00

0.70

0.70

0.24

N

/

43.00

113.00

71.08

68.00

22.68

PS

cm

48.60

68.30

60.32

60.50

5.69

AS

cm

68.80

136.60

106.69

120.90

23.84

TH

cm

65.10

93.20

76.32

72.50

8.24

TC

kg

37.50

189.30

110.10

45.00

70.12

CC

kg/m

0.15

0.30

0.22

0.23

0.05

CS

/

0.17

1.00

0.77

1.00

0.36

MC

kg

2.40

3.90

3.22

3.00

0.45

1.50

36.00

4.29

3.50

3.86

OUA

2

m

Maximum

Mean

Median

Std. dev.

Applying a novel hybrid ALO-BPNN model Chapter

PS

0.054

0.735

−0.308

−0.162

−0.068

−0.171

−0.373

−0.035

−0.582

0.284

0.281

−0.232

−0.488

−0.338

0.079

−0.958

0.515

0.846

0.953

−0.607

−0.895

−0.433

0.318

0.331

0.431

−0.698

−0.948

−0.491

0.099

0.271

0.421

TH

0.876

0.847

0.329

0.325

−0.396

TC

0.704

−0.025

−0.457

65 85

CC

0.048 0.427

0.598

−0.418

CS

0.589

0.065

MC

−0.163

OUA 35 45

UCS

60 100 140

50 80 110

70 100 130

H

N

AS

50

150

TC

35 50

0.3 0.8 50 65

−0.938

0.15 0.30

TH

0.001

0.573

2.5 3.5

CC

−0.172

−0.172

AS

MC

−0.027

60 120

−0.132

0.068

−0.09

0.162 −0.006

50 90

0.3

D

0.298

0.071 −0.039

70 120

0.082

−0.37

50 150

RMR

40 46

−0.352

0.165

0.7

OUA

−0.684

3.5

−0.038

CS

−0.169

0.511

0.255

TC

RMR

0.058

0.156

AS

−0.322

MC 2.5

N

−0.811

CC 0.15 0.25

H

−0.147

N PS

TH 65 75 85

UCS

0.064

0.919

H D

PS 50 60

0.2 0.8

D 0.3 0.6 0.9

331

5 25

RMR 40 44 48

UCS

18

0.2 0.6 1.0

5 20 35

CS

OUA

Uniaxial compressive strength- UCS, MPa

Total number of holes- N

The thickness of Glossy blast layer- TH, cm

Rock mass rating- RMR

Spacing of Perimeter holes- PS, cm

Charge concentration- CC, kg/m

Depth- H, m

Spacing of Auxiliary holes- AS, cm

Charge structure- CS

The degree of binding of the layered surface- D

Total charge- TC, kg

Maximum single-hole charge- MC, kg

Overbreak and Underbreak area- OUA, m 2

FIG. 2 Correlation analysis of input and output variables.

4

Results and discussion

A novel hybrid model has been developed in this investigation. Fig. 3 shows the framework of the proposed ALO-BPNN model for estimating OUA. In what follows, the development of this model and performance comparison results with other models are presented and discussed in detail.

4.1 Developing a hybrid ALO-BPNN model for predicting overbreak and underbreak area The number of hidden layers, the number of neurons in hidden layers, and the weights and biases between layers are the main optimization objectives of the BPNN model. To increase the prediction accuracy and optimization rate of the hybrid model, the traditional BPNN model was trained in advance to achieve a certain accuracy. Two hidden layers were considered in this study, and the corresponding number of neurons in the hidden layers is controlled within 4–10. R2 and RMSE were used to evaluate the prediction performance of considered BPNN models in the training and testing phases together. In other words, the BPNN model with the maximum R2 and the lowest RMSE values can be optimized by the ALO algorithm to improve the prediction performance. The results of the traditional BPNN models are shown in Table 4. As can be seen

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Applications of artificial intelligence in mining and geotechnical engineering

area

overbreak

underbreak Excavation

Uniaxial compressive strength- UCS, MPa

Total number of holes- N

Rock mass rating- RMR

Spacing of Perimeter holes- PS, cm

Depth- H, m

Spacing of Auxiliary holes- AS, cm

The degree of binding of the layered surface- D

Total charge- TC, kg

The thickness of Glossy blast layer- TH, cm

Charge structure- CS

Charge concentration- CC, kg/m

Maximum single-hole charge- MC, kg

Training dataset (80%)

Testing dataset (20%) weights and biases

BPNN model

Initialize the antlions population Calculating fitness value of each antlion

Optimized

ALO- BPNN model

weights and biases

Updating position of current antlion Calculating fitness value of all antlions

Target satified ? (RMSE)

No Updating antlions

Yes

ALO algorithm

Final ALO- BPNN model Overbreak and Underbreak area- OUA, m2

FIG. 3 The framework of the hybrid ALO-BPNN model for predicting OUA.

TABLE 4 Performance evaluation of BPNN models. Neurons of hidden layers

R2

RMSE

Models no.

Hidden layer 1

Hidden layer 2

Training

Testing

Training

Testing

1

4

/

0.9847

0.8520

0.5156

0.8098

2

6

/

0.9918

0.8740

0.3763

0.7470

3

8

/

0.9940

0.9353

0.3231

0.5355

4

10

/

0.9952

0.8838

0.2888

0.7176

5

6

2

0.9827

0.9229

0.5479

0.5846

6

8

2

0.9621

0.8097

0.8105

0.9181

7

8

4

0.9244

0.8329

1.1451

0.8604

8

8

6

0.9933

0.8028

0.3416

0.9346

Line in bold represents the better solution.

in this table, model number 4, that is, a single hidden layer with 10 neurons has shown the highest value of R2 and the lowest value of RMSE in the training phase. However, the No. 4 model failed to continue its good performance in the testing phase. The best model is No. 3 with the highest value of R2 (0.9353) and the lowest value of RMSE (0.5355) in the testing phase.

Applying a novel hybrid ALO-BPNN model Chapter

18

333

Therefore, model number 3 was optimized by the ALO algorithm to improve prediction performance in this study. After determining the basic structure of BPNN, the selection of weights and biases has a great influence on the performance of the model [29–31]. In this study, the ALO algorithm was used to select the optimized values of weights and biases by copying the strong seduction and hunting ability of antlions. There are two main parameters that should be adjusted before running the algorithm, that is, the population size of antlions and several iterations. To this purpose, the population size of antlions was considered to be equal to 25, 50, 75, 100, and 150, respectively, for 400 iterations, in this study. Finally, the best population size for hybrid model construction can be obtained with the lowest value of RMSE. The comparison results of the prediction performance of BPNN-ALO models with different population sizes are shown in Fig. 4A. As can be seen in this figure, the model with a population size of 75 has the lowest RMSE for 400 iterations. In addition, the calculation time is also taken into account in model evaluation. Models that take less time get more favor when they have very similar predictive performance in the same dataset. As shown in Fig. 4B, the iteration time increases with the increase in population size. The model with a population size of 75 obtained the best prediction performance with a computing time of 876 s. Therefore, 75 antlions and 400 iterations were used to optimize BPNN for OUA prediction. Population 25 Population 75 Population 150

3.5

RMSE

3.0

Population 50 Population 100

2.0

ALO- BPNN

1.5

1400

0.23

0.5

1200

0.22

1000

0.21

800

0.20

600

0.19

1.0 0.0

0.5 0.0

1600 time

0.24

1.0

2.5

0.25

0

100

0

100

200

Iteration

200

300

300

400

400

0.18 0.17

Time (s)

(b)

4.0

RMSE

(a)

RMSE 25

50

75

100

125

400 200 150

Population size

FIG. 4 The results of optimization for ALO-BPNN models: (A) Population size of antlions and (B) computing time.

4.2 Comparation performance of OUA prediction To obtain the best prediction model for predicting OUA in this study, two BPNN models (a traditional BPNN model and corresponding optimized ALO-BPNN model) and two other models (generalized regression neural network (GRNN) and extreme learning machine (ELM)) were constructed in the same training phase. The selection method of hyperparameters for GRNN and ELM can be referred in Refs. [32, 33]. After turning the hyperparameters into different models, each model was run in terms of the same train set to obtain the best prediction model. The results of the prediction performance of all models in the training phase were presented in Table 5. As can be seen in this table, four models have achieved excellent predictive indices by resulting in high values of

TABLE 5 Performance indices of all models in the training phase. Performance Model

R2

Score

RMSE

Score

MAE

Score

VAF (%)

Score

BPNN

0.9940

2

0.3231

1

0.2286

1

99.4000

1

ALO-BPNN

0.9972

4

0.2193

4

0.1232

4

99.7236

4

GRNN

0.9967

3

0.2399

3

0.1728

3

99.6971

3

ELM

0.9939

1

0.3260

2

0.1800

2

99.4164

2

Applying a novel hybrid ALO-BPNN model Chapter

335

18

(b)

40

Predicted overbreak area (cm2)

(a)

Predicted overbreak area (cm2)

R2 (close to 1) and VAF (close to 100%) and low values of RMSE and MAE (close to 0). Nevertheless, the ALO-BPNN model has the best performance indices than other models with the highest values of R2 (0.9972) and VAF (99.7236%), and the lowest values of RMSE (0.2193) and MAE (0.1232). After that are GRNN, BPNN, and ELM. Fig. 5 displays the regression diagrams of four models to explore the relationships between measured and predicted OUA in all models. The measured and predicted values of OUA have been set as the horizontal axis and the vertical axis, respectively. The black diagonal lines in each diagram indicate lines where the predicted and measured values are in perfect agreement, and the lines with 10% represent the 10% error between the measured and the predicted values. In other words, if the predicted and measured values are the same, their relationship can be expressed as y ¼ x. As can be observed in this figure, the ALO-BPNN model not only has the closest relationship between the predicted and the measured values of OUA but also has the lowest value of RMSE. Therefore, the ALO-BPNN is the best prediction model in the training phase, after that also are GRNN, BPNN, and ELM. However, the division of the train set and test set is random and uneven, thus the model performance in the training phase is not equal to the final prediction effect. Therefore, it is necessary to verify the predictive performance of trained models by using the same test set. The performance indices of four models in the testing phase have been shown in Table 6. As can be seen in this table, the 10%

35

y=0.9652x+0.1672

30

RMSE=0.3231

10%

25 20 15 10 5 0

BPNN 0

5

10

15

20

25

30

35

40

10%

35

y=0.9954x+0.0321

30

RMSE=0.2193

20 15 10 5

ALO-BPNN

0

40

0

10%

35

y=0.9783x+0.1640

30

RMSE=0.2399

10%

25 20 15 10

GRNN

5 0

0

5

10

15

20

25

30

35

Measured overbreak area (cm2)

40

Predicted overbreak area (cm2)

Predicted overbreak area (cm2)

(d)

40

5

10

15

20

25

30

35

40

Measured overbreak area (cm2)

Measured overbreak area (cm2)

(c)

10%

25

40

10%

35

y=0.9645x+0.0718

30

RMSE=0.3260

10%

25 20 15 10 5 0

ELM 0

5

10

15

20

25

30

35

40

Measured overbreak area (cm2)

FIG. 5 Regression diagrams of the models in the training phase: (A) BPNN model, (B) ALOBPNN model, (C) GRNN model, and (D) ELM model.

TABLE 6 Performance indices of all models in the testing phase. Performance Model

R2

Score

RMSE

Score

MAE

Score

VAF (%)

Score

BPNN

0.9353

3

0.5355

3

0.4659

2

93.7112

2

ALO-BPNN

0.9410

4

0.5111

4

0.4314

4

94.5769

4

GRNN

0.9311

2

0.5525

2

0.4435

3

93.6809

3

ELM

0.9152

1

0.6129

1

0.5041

1

91.6021

1

Applying a novel hybrid ALO-BPNN model Chapter

337

18

y=0.8723x+0.6332 RMSE=0.5355

10%

BPNN 0

1

2

3

4

5

6

7

8

9

10 9 8 7 6 5 4 3 2 1 0

10

(d)

10%

y=0.8599x+0.7550

10%

RMSE=0.5525

GRNN 0

1

2

3

4

5

6

7

8

9

Measured overbreak area (cm2)

Predicted overbreak area (cm2)

(b) 10%

10

10 9 8 7 6 5 4 3 2 1 0

10%

y=0.8799x+0.6560 RMSE=0.5111

10%

ALO-BPNN 0

1

2

3

4

5

6

7

8

9

10

Measured overbreak area (cm2) Predicted overbreak area (cm2)

(c)

10 9 8 7 6 5 4 3 2 1 0

Measured overbreak area (cm2) Predicted overbreak area (cm2)

(a)

Predicted overbreak area (cm2)

performance indices of all models are degraded in the testing phase. The values of R2, RMSE, MAE, and VAF of the ALO-BPNN model have been continuously evaluated as optimal performance indices among the other models, and the predictive performance of BPNN has surpassed that of the GRNN model by resulting in higher values of R2 and VAF, lower values of RMSE and MAE in the testing phase. The regression diagrams of the four models have been shown in Fig. 6. As mentioned earlier, each model performed worse in the testing phase than in the training phase. A few points clustered on the black diagonal line, and most of the points were close to the 10% line, and only a few of the points have large errors. Nonetheless, the ALO-BPNN model has lower values of RMSE than other models and its predicted points in the diagram have been clustered closer to the black diagonal line (100% agreement) and the lines with 10% error. The worst model is ELM with the highest values of RMSE (0.6129), the GRNN model and the BPNN model have similar performance in terms of RMSE and regression functions. The score results of the four models in terms of various performance indices in the training set and test set, respectively, have been shown in Fig. 7. As can be seen in this picture, the ALO-BPNN model is the best in both the training and testing phases. The prediction performance of the ELM model and GRNN

10 9 8 7 6 5 4 3 2 1 0

10%

y=0.8244x+0.5941

10%

RMSE=0.6129

ELM 0

1

2

3

4

5

6

7

8

9

10

Measured overbreak area (cm2)

FIG. 6 Regression diagrams of the models in the testing phase: (A) BPNN model, (B) ALO-BPNN model, (C) GRNN model, and (D) ELM model.

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Applications of artificial intelligence in mining and geotechnical engineering

(a)

7

ELM

(b)

4

ELM

VAF (%)

12

GRNN

RMSE

16

ALO-BP

R BPNN

10

MAE

16

ALO-BP

2

5 0

2

4

6

GRNN

10 8

BPNN

20 18 16 14 12 10 8 Score

10 12 14 16 18 20 Score

6

4

2

0

FIG. 7 The performance score results of the four models: (A) Training and (B) testing.

model in the testing phase are inferior to that of the training phase, while that of BPNN is just the opposite. To better evaluate the performance of all models in the testing phase, the Taylor diagram is constructed in Fig. 8. A typical Taylor diagram can be divided into three parts, that is, correlation coefficient, standard deviation, and RMSE. As can be seen in this picture, the black arcs and dots represent the correlation coefficient, the blue arcs and dots represent the standard deviation, and the green arcs and dots represent RMSE. The RMSE and correlation coefficient of the test data defaults to 0 and 1, respectively. Then, the prediction performance is determined by the correlation coefficient, standard deviation, and RMSE, which will be compared with those of the measured data in the test set. It can be observed that the ALO-BPNN is the best model with the closest position to the test. After the ALO-BPNN model, the better performance in terms of the position relative to the test can be found in the points of the BPNN, GRNN, and ELM models, respectively. test

0

−0.2

0.2

−0.4

Correlation Coefficient

ELM

0.4

GRNN

−0.6

BPNN

0.6

ALO-BPNN −0.8

0.8

−0.9

0.9

3.2

2.4

1.6

0.79

0

0.79

0.63 1.6

0.99

0.32

0.95

1.6

1.3

2.2

1.9

2.5

3.2

2.8

3.5

4.1

−1

SE RM

−0.99

3.8

4.4

5.1

0.95 4.7

−0.95

1 2.4

Standard Deviation

FIG. 8 Comparison of the performance of multiple models in the Taylor diagram.

3.2

Applying a novel hybrid ALO-BPNN model Chapter

339

18

4.3 Sensitively analysis There are 12 variables as input parameters to predict OUA in this study, while the sensitivity of each variable to OUA is unknown and needs further study. Therefore, the PAWN method proposed by Pianosi and Wagener [34,35] is used to obtain the importance score (i.e., sensitivity index) of each variable. Fig. 9 illustrates the estimated importance scores of all input variables to the predicted OUA by using the ALO-BPNN model. As can be observed in this picture, the variables with the highest and the lowest scores are the thickness of the glossy blast layer (TH) of 0.4022 and the maximum single-hole charge (MC) of 0.1231, respectively. The total charge (TC), spacing of auxiliary holes (AS), and spacing of perimeter holes (PS) have shown the same sensitivity to OUA prediction in terms of the same importance score (0.3940). The sensitivity of other variables is ranked as charge structure (CS), the total number of holes (N), the degree of binding of the layered surface (D), charge concentration (CC), the uniaxial compressive strength (UCS), rock mass rating (RMR), and depth (H) with the importance scores of 0.3809, 0.3648, 0.2708, 0.2387, 0.2113, 0.1950, and 0.1375, respectively. 0.3940 0.3940 0.4022 0.3940 0.2708 0.2113

0.2387

0.1950

ge

re ole

cha r

ctu

n

stru

le-h

ge

atio

sin g um

xim Ma

ent r onc ec

Ch ar

cha r arg Ch

To

bla

fG

los

sy

tal

st l a

hol ary nes so

Th

e th ick

Spa cin g

ge

yer

es

es hol

xili Au

of

Per of

Spa cin g

num

ber

of

ime ter

hol

fac sur tal

Th

ed

egr

ee

of

To

f th e la go din bin

iax Un

es

e

0.1231

yer ed

ss r ma

stre n

Ro ck

pre ssi ve

De pth

atin g

0.1375

ial

com

0.3809

0.3648

gth

Importance score

0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

FIG. 9 Sensitive analysis of input variables on OUA prediction.

5

Conclusion and summary

Effective control and prediction of OUA have always been an urgent demand for underground space excavation projects, since OUA not only affects the progress of underground excavation project but also causes a certain potential safety hazard to underground space. In this study, a novel hybrid

340

Applications of artificial intelligence in mining and geotechnical engineering

ALO-BPNN model is proposed to predict OUA with the other three AI models (i.e., BPNN, GRNN, and ELM). The results show that the ALO-BPNN model has a better performance for OUA prediction than other models by resulting in more satisfactory performance indices (higher values of R2 and VAF, lower values of RMSE and MAE). In addition, the thickness of the glossy blast layer (TH) was considered the most important input variable based on the results of sensitive analysis. The effectiveness and applicability of artificial intelligence methods have been demonstrated in this study for predicting and controlling the OUA.

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[33] M. Shariati, M.S. Mafipour, B. Ghahremani, F. Azarhomayun, M. Ahmadi, N.T. Trung, A. Shariati, A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement, Eng. Comput. (2020) 1–23. [34] F. Pianosi, T. Wagener, A simple and efficient method for global sensitivity analysis based on cumulative distribution functions, Environ. Model Softw. 67 (2015) 1–11. [35] F. Pianosi, T. Wagener, Distribution-based sensitivity analysis from a generic input-output sample, Environ. Model Softw. 108 (2018) 197–207.

Chapter 19

Fragmentation by blasting size prediction using SVR-GOA and SVR-KHA techniques Enming Lia,b, Jian Zhoub, Rahul Biswasc, and Zahir Elharith MohammedElamein Ahmedd Universidad Politecnica de Madrid – ETSI Minas y Energia, Madrid, Spain, bSchool of Resources and Safety Engineering, Central South University, Changsha, China, cDepartment of Applied Mechanics, VNIT Nagpur, Nagpur, Maharashtra, India, dDepartment of Land and Infrastructure, Politecnico Di Torino, Torino, Italy a

1

Introduction

Blasting is a common technique to tackle issues of mining engineering and civil engineering [1–5]. Its main function is to demolish and displace rock, concrete or soil. In mining operations, the primary aim of blasting is fragmentation and displacement of rock mass. It is reported that nearly 20%–30% explosive energy is effective for the expected fragmentation and replacement [6], but the remaining energy is turned to be negative effects such as backbreak, flyrock or undesirable fragmentation [7]. To overcome these problems, scientific and reasonable blasting design is crucial. However, these negative phenomena sometimes are inevitable because of the complexity of blasting mechanism. Regarding this, a lot of empirical models and methods are proposed [8–10]. For instance, Kuznetsov [11] developed an approach to predict mean fragment size (MFS). In this approach, rock mass and explosive properties play significant roles in controlling blasting effect. Bergmann et al. [12] proposed BRW model where the velocity of detonation and the peak pressure in the gauge hole with filled water were proposed and considered as influential factors to blasting mean fragment. Considering rock mass factors and blasting design parameters, Chung and Katsabanis [9] developed 80% passing size and mean fragment size prediction equations. However, only several influential factors can be considered in a certain function and overlook the influence of other factors. In the past few years, the powerful ability of integrating various influenced factors of artificial intelligence (AI)-based approaches have gained promising achievements in geotechnical issues [13–16]. For the Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00014-X Copyright © 2024 Elsevier Inc. All rights reserved.

343

344 Applications of artificial intelligence in mining and geotechnical engineering

application of advanced artificial intelligence technologies in the prediction of blasting fragment, some works are inspiring [17,18]. For instance, Shi et al. [19] utilized the support vector machine (SVM), artificial neural network (ANN) and multivariate regression analysis (MVRA) to predict rock fragment distribution. Gao et al. [20] used Gaussian process regression (GPR) with different kernel functions to establish rock fragmentation prediction models. Proposed GPR-squared exponential model outperformed than other AI-based prediction approaches. Hasanipanah et al. [21] employed particle swarm optimizationadaptive network-based FIS (PSO-ANFIS) method to assess rock fragmentation and compared the prediction capability and obtained desirable prediction performance. Despite all this, some novel techniques are still not investigated to tackle blasting fragment issues. In this study, 76 groups of blasting fragmentation parameters obtained from Sharma and Rai [22] are analyzed and used for developing blasting fragment prediction models. Here, v-support vector regression (SVR) is utilized as the benchmark prediction tool combined with two kind of nature-inspired optimization algorithms, i.e., grasshopper optimization algorithm (GOA) and krill herd algorithm (KHA). To measure the optimization process, mean square error (MSE) was utilized. Two classical mathematical indices, i.e., R2 (coefficient of determination) and mean absolute error (MAE), are used to assess the prediction performance. Finally, developed prediction models of blasting fragment were compared, and the best model was selected and recommended to predict blasting fragment.

2 Data analysis and pre-processing To develop intelligent blasting fragmentation size models, 76 groups of data were analyzed and utilized in this study [22]. The main reason of employing this dataset is due to the fruitful influenced factors. Total 19 factors related to blasting fragmentation were contained in this dataset. According to the data sources, these data can be categorized into three groups. The first group is from blast designs which include blast hole diameter (D) (m), average bench height (H) (m), average sub-grade drilling (J) (m), average spacing (S) (m), average burden (B) (m), average stemming (ST) (m), average length (L) (m), average width (Wd) (m), S/B ratio (S.B), ST/B ratio (ST.B), stiffness ratio (H.B), J/B ratio (J.B), B/D ratio (B.D), length/width ratio (L.Wd) and number of holes (NH). And the second group belongs to explosive parameters which involve total explosive amount (Qe) (t), linear explosive density (De) (kg/m) and powder factor (PF) (kg/m3). The last group can be categorized into rock mass parameter, i.e., uniaxial compressive strength (UCS) (MPa). The general data distribution of each influenced factor and blasting fragmentation has been shown in Fig. 1 based on boxplots. From Fig. 1, a few outliers can be observed from parameters ST, Wd, PF, NH, UCS and MFS. To further demonstrate the data distribution of these parameters, histograms with ten bins have been shown in Fig. 2.

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Data distribution of influenced factors and blasting fragmentation 40 30 20

0.3 0.25

D

H 120 100 80 60 40

10 5 ST

5

0

B.D

PF

0.5

T.B

150 100 50 L.W

35 30 25 20 15

1.5 De

S.B

1

2 Qe

1

B 1

2

2.5

50

S 1.2

30

100

10

J

Wd

J.B

8

10

35

0.2 H.B

2 1 0

L

10

12

150 100 50

0.4

4 3 2

3 2 1 0

NH 0.6 0.4 0.2

UCS

MFS

FIG. 1 Data distribution of all blasting parameters based on the boxplot.

FIG. 2 Blasting fragmentation data distribution by histogram plots with ten bins.

Ununiform distribution of blasting fragmentation parameters can be observed based on the histogram with 10 bins according to Fig. 2, where less blasting fragmentation and larger blasting fragmentation occupy less part of the whole dataset. Above data distribution indicated the complexity of data distribution. Some basic mathematical statistics for significant influenced parameters and blasting fragmentation were calculated as shown in Table 1. And then the correlation between different influenced factors and blasting fragmentation were calculated based on the function “corr” with the type of “Spearmen”, “Pearson” and “Kendall” in the environment of “Matlab 2020b”. Detailed information about correlation between different influenced factors and blasting fragmentation can be seen in Fig. 3.

TABLE 1 Basic statistical results of blasting fragment parameters. Max

Min

Mean

Median

Std.

Kurtosis

Skewness

D

0.62


1.16

0.26

0.27

0.18

58.03


7.18

H

45.50


1.08

23.34

21.00

10.56


0.32

0.42

J

3.50


0.68

1.35

1.50

1.03


0.78


0.12

S

12.80


1.40

9.89

10.00

2.48

8.77


2.40

B

10.00


0.99

8.41

9.00

1.98

12.96


3.36

ST

12.20


0.37

6.45

6.00

2.44

0.96

0.59

L

133.00


0.83

71.25

72.00

29.51


0.05

0.09

Wd

175.00

1.95

71.60

68.20

28.06

4.78

1.29

S.B

1.29


0.86

1.10

1.15

0.33

22.23


4.56

ST.B

1.22


0.66

0.72

0.70

0.26

10.30


1.73

H.B

4.55


1.33

2.59

2.50

1.06

1.31


0.41

J.B

0.39


0.63

0.14

0.19

0.16

8.85


2.36

B.D

35.86


0.74

30.55

32.15

6.64

16.42


4.01

L.W

2.00


0.68

1.03

1.02

0.49

0.95


0.45

NH

145.00

1.31

56.76

56.00

26.36

3.09

1.11

Qe

282,088.00


0.15

82,520.44

56,387.00

68,274.39

1.13

1.39

De

99.22


1.02

68.06

67.14

19.87

2.82


0.96

PF

2.53

0.22

1.73

1.68

0.31

7.22


0.73

UCS

36.00

0.66

21.47

22.00

6.39

2.61


0.63

MFS

2.34

0.10

0.38

0.34

0.27

39.69

5.75

FIG. 3 Correlation analysis between different influenced factors and blasting fragmentation based on “Spearman,” “Pearson,” and “Kendall” approaches. (Continued)

FIG. 3, CONT’D

FIG. 3, CONT’D

350 Applications of artificial intelligence in mining and geotechnical engineering

To determine the input scenarios, the influential factors which have correlation larger than 0.3 with MFS were regarded as significant influential factors and with correlation less than 0.1 were considered as less-correlated influential factors. From the correlation analysis by “Spearman” method, it can be found that Wd, S.B, ST.B, NH, Qe and UCS are significant influential factors, while D, S and B are less-correlated influential factors. From the correlation results of “Pearson” method, it can be found that Wd, S.B, ST.B, NH and UCS are significant influential factors and D, S, L, De and PF are less-correlated influential factors. When utilizing the “Kendall” method to analyze the correlation, it can be observed that significant influential factors are Wd, S.B, ST.B, NH and UCS, where the correlation between Wd and MFS is 0.2954 which is about to 0.3, and thus, Wd was accounted to be significant. Less-correlated influential factors include D, H, S, B, L, B.D and L.W. Finally, the significant influential factors and less-correlated influential factors are summarized in Table 2.

TABLE 2 Union results based on three correlation methods. Less-correlated factors

Correlation method

Significant factors

Spearman

Wd, S.B, ST.B, NH, Qe and UCS

D, S and B

Pearson

Wd, S.B, ST.B, NH and UCS

D, S, L, De and PF

Kendall

Wd, S.B, ST.B, NH and UCS

D, H, S, B, L, B.D, L.W

Union of three correlation methods

Wd, S.B, ST.B, NH, Qe and UCS

D, H, S, B, L, De, PF, B.D, L.W

Finally, according to the three correlation methods, the significant influential factors can be determined including Wd, SB, ST.B, NH, Qe and UCS. The less-correlated influential factors involve D, H, S, B, L, De, PF, B.D and L.W. And therefore, the two input scenarios can be determined in which the first input scenario involves all influential factors. As for the second input scenario, those less-correlated influential factors are removed from the original influential factors and thus the second types of inputs are J, ST, Wd, S.B, ST.B, H.B, J.B, NH, Qe and UCS. Differential ranges of data distribution of different parameters can be observed which stimulates the implementation of normalization to the original data. Therefore, the original data was all normalized into the range of [0,1] so as to reduce the influence of magnitude. Principal component analysis (PCA) was also employed to weaken the influence of overlapped information to prediction.

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After PCA, the dimension of input Scenario 1 was reduced from 19 to 7, and the dimension of input Scenario 2 was reduced from 10 to 6. After pre-processing the original datasets, they are divided into two parts where 80% of the original datasets are used to establish the training networks [23,24]. The remaining 20% is not used in the model development but is employed to test the prediction performance of the network. With the increase of inputs, the prediction model would also get more complicated. However, according to the prediction results, it is expected to more effective prediction scenarios.

3

Method

3.1 Support vector regression The support vector regression was derived from the support vector machine based on statistical learning theory [25]. At first, the support vector machine was used for tackling classification issues, and by introducing the ε-insensitive loss function, it can be used for addressing regression issues. Its main advantages include versatility in kernel function selection, higher tolerance for high-dimension spaces and effectiveness for small-scale data. The main principle of SVR for solving non-linear problems is that by applying the kernel function to map the original input data to higher dimension so as to make the total deviation between input data and hyperplane minimal. In this study, v-support vector regression was employed as the benchmark prediction tool [26,27].

3.2 Grasshopper optimization algorithm (GOA) The GOA was proposed by Saremi et al. [28] which gets inspiration from the swarm behaviors of grasshoppers. A grasshopper is a kind of widely distributed insect and is considered as a pest because of its detriment to crops and plants. From birth to death, the life cycle of grasshoppers involves three stages: egg, nymph and adult. The swarm behaviors of grasshoppers are usually embodied in the nymph and adult stages. Two kinds of different swarm characteristics can be observed in the larval phase and adulthood phase where larval grasshoppers move slowly and minorly and adulthood grasshoppers have longer movement range and faster movement. Therefore, larval grasshoppers tend to obtain food source in their movement path. While adulthood grasshoppers can migrate a long distance to explore more food source. Aforementioned swarming behaviors can be mathematically modeled as follows: Pi ¼ Si + Gi + W i

(1)

where Pi denotes current position of the ith grasshopper, Si represents the swarm interaction, Gi defines the gravity force on the ith grasshopper and Wi is the wind advection. It can be found that the position of a grasshopper is influenced by

352 Applications of artificial intelligence in mining and geotechnical engineering

three factors in the mathematical model of GOA. Gi and Wi can be defined by the function as follows: Gi ¼
g^ eg (2) W i ¼ u^ ew

(3)

where g and u represent constants and ^eg and ^ew denotes unit vectors. In the nature, the larval grasshopper moves slowly and limitedly. Therefore, the gravity force and wind direction will have a significant influence on seeking for food. By mimicking this phenomenon, Gi and Wi control the searching direction of GOA. Another significant parameter Si can be defined as follows: Si ¼

N X

  s d ij d^ij

(4)

j¼1, j6¼i

where N represents the population number, dij defines the space distance between the ith and jth grasshopper and ^d ij represents a unit vector which indicates the space direction from the ith to the jth grasshopper. As for s, it controls the attractive force between different grasshoppers. The equations of dij, ^dij and s are as follows:   d ij ¼ Pi
Pj  (5) Pi
P j d^ij ¼ dij
y

sðyÞ ¼ FeAL
e
y

(6) (7)

where AL and F denote the scale and intensity of the attractive force, respectively. A rudimentary animation demonstration of the life cycle and swarming behavior of grasshoppers has been demonstrated in Fig. 4.

FIG. 4 A general demonstration of the swarming behavior of grasshoppers.

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For making the GOA optimization function to be suitable and clear for solving a defined problem, the Si, Gi and Wi can be rewritten according to the previous refinement and the ultimate form of the GOA function is shown as follows: !   N X  UBD
LBD  D k (8) Pi ¼ k + BD S Pj
Pi 2 j¼1, j6¼i In which UBD and LBD determine the searching upper bound and lower bound, respectively. D defines the dimension of proposed problem. BD represents the current best solution, and k is a coefficient which creates a balance mechanism between exploration and exploitation and k can be calculated according to the following function: k ¼ kmax
J

kmax
kmin J max

(9)

In which kmax and kmin is defined as 1 and 0.00001 according the original paper and J is the current iteration number; Jmax represents the maximum iteration number.

3.3 Krill herd algorithm (KHA) As a type of newly-developed nature-inspired optimization algorithm, krill herd algorithm (KHA) simulated the herding behavior of krill [29]. It has been successfully applied in all kinds of optimization issues and proved to be helpful. The general working flow of KHA involves initializing parameters, approaching optimization target and evaluating optimization results. In the stage of initialization, the velocity of each krill individual was set, and this stage is influenced by local effect, target swarm effect and repulsive swarm effect. This process can be described by the following equations: V new ¼ ai V max + bn V old i i i

(10)

ai ¼ alocal + atarget i i

(11)

where

represents the maximum induced motion of the ith krill individual, where Vmax i Vold i represents the previous induced motion of the ith krill individual, bn is the inertia weight of the motion induced. alocal and atarget denote the local and target i i effects, respectively. To determine the range of surrounding members of each krill individual, the sensing distance was adopted [29]. It can be evaluated by the following function: SDi ¼

n
1   1 X xi
xj  5n i¼0

(12)

354 Applications of artificial intelligence in mining and geotechnical engineering

where n is the number of krill individuals and xi and xj denote the dimensional location of the ith and jth krill individual, respectively. In the next step, to enrich the population diversity, a random diffusion process is employed by tuning the maximal diffusion speed and random diffusion direction index as follows: DFi ¼ DFmax ∂

(13)

where DFmax signifies the maximal diffusion speed and ∂ represents the random diffusion direction index with the value of [
1, 1]. To approximate the best solution, the position of each krill individual would be updated with the change of time according to the following equation: xi ðt + αtÞ
xi ðtÞ ¼ αt

dxi dt

(14)

i where t and αt describe the time parameters. And dx dt can be disassembled into the accumulation of three behaviors in a dimensional space as follows:

dxi ¼ N i + Fi + D i dt

(15)

where Ni, Fi and Di represent the motion induced by other krill individuals, swarming movement and random diffusion, respectively. During the process of position update, the thought of genetic behaviors is referenced to ensure better solution which involves crossover and mutation. For crossover, the ith krill individual updates its position according to the position other individuals. For mutation, the random mutation might happen to produce new krill position.

4 Model development and discussion To develop the MFS prediction model, two input scenarios and two regression approaches were adopted. To get the most optimal model and measure the model stability, two significant parameters which can control the model performance should be set carefully, namely swarm size and iteration. Generally, bigger swarm size or iteration would cost more optimization time, while less swarm size or iteration would induce over-fitting or under-fitting. Finally, the iteration equal to 500 and swarm size equal to 30, 40, 50 and 60 were set and compared. Mean squared error (MAE) and coefficient of determination (R2) are performed to assess the model goodness. The detailed prediction performance of GOA-SVR and KHA-SVR can be seen in Tables 3 and 4. Minimum, maximum, mean and standard deviation values are used for measuring the comprehensive performance of MFS prediction models. It can be found that when utilizing GOA-SVR and input Scenario 1 as prediction approach, MAE value is about 0.027 and 0.048 for the training set and testing set, respectively,

Fragmentation by blasting size prediction Chapter

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TABLE 3 GOA-SVR prediction performance. Training set 2

Testing set 2

Input scenario

Swarm size

R

MAE

R

MAE

Scenario 1

30

83.34

0.026

83.98

0.048

40

82.61

0.027

84.50

0.048

50

82.39

0.027

84.60

0.047

60

83.42

0.026

83.73

0.048

Minimum

82.39

0.026

83.73

0.047

Maximum

83.42

0.027

84.60

0.048

Mean

82.94

0.027

84.20

0.048

0.45

0.000

0.36

0.000

30

81.53

0.029

85.14

0.054

40

82.07

0.029

85.83

0.053

50

82.07

0.029

84.58

0.054

60

81.90

0.029

84.30

0.053

Minimum

81.53

0.029

84.30

0.053

Maximum

82.07

0.029

85.83

0.054

Mean

81.90

0.029

84.96

0.053

0.22

0.000

0.58

0.001

Standard deviation Scenario 2

Standard deviation

and MAE value is about 0.029 and 0.053 for the training set and testing set, respectively. The mean R2 value is about 82.94 with standard deviation 0.45 and 84.20 with standard deviation 0.36 for the training set and testing set, respectively. When utilizing GOA-SVR and input Scenario 2 as prediction approach, MAE value is around to be 0.029 and 0.053 for the training set and testing set, respectively. The mean R2 value is about 81.90 with standard deviation 0.22 and 84.96 with standard deviation 0.58 for the training set and testing set, respectively. By comparisons, it can be found that MAE values brought by GOA-SVR prediction models are stable for two input scenarios. But input Scenario 1 produced better performance for MAE. For R2 values, the results from two input scenarios are comparable in which input Scenario 2 brings better performance in the testing set while input Scenario 1 has better performance in the training set.

356 Applications of artificial intelligence in mining and geotechnical engineering

TABLE 4 KHA-SVR prediction performance. Training set Input scenario

Swarm size

R

Scenario 1

30

Testing set 2

MAE

R

MAE

83.23

0.026

83.96

0.048

40

83.53

0.026

83.54

0.048

50

83.20

0.026

83.93

0.048

60

83.56

0.026

83.52

0.048

Minimum

83.20

0.026

83.52

0.048

Maximum

83.56

0.026

83.96

0.048

Mean

83.38

0.026

83.74

0.048

0.17

0.000

0.21

0.000

30

81.58

0.029

85.11

0.054

40

82.01

0.029

84.65

0.054

50

82.04

0.029

84.55

0.053

60

82.03

0.029

84.64

0.053

Minimum

81.58

0.029

84.55

0.053

Maximum

82.04

0.029

85.11

0.054

Mean

81.91

0.029

84.74

0.054

0.19

0.000

0.22

0.000

Standard deviation Scenario 2

2

Standard deviation

When utilizing KHA-SVR as prediction methods, some similar phenomena can be observed, i.e., stable performance of MAE, superior performance of MAE induced by input Scenario 1 than Scenario 2, better performance of R2 produced by input Scenario 1 than 2 for the training set, inferior performance of R2 brought by input Scenario 1 than 2 for the testing set. Overall, two developed prediction approaches produced desirable prediction performance. The mean performance of R2 for the training set and testing set of GOA-SVR-Scenario 1, GOA-SVR-Scenario 2, KHA-SVR-Scenario 1 and KHA-SVR-Scenario 2 is [82.94 81.90 83.38 81.91] and [84.20 84.96 83.74 84.74], respectively. The mean performance of MAE for the training set and testing set of GOA-SVR-Scenario 1, GOA-SVR-Scenario 2, KHA-SVR-Scenario 1 and KHA-SVR-Scenario 2 is [0.027 0.029 0.026 0.029] and [0.048 0.053 0.048 0.054], respectively. The optimization process of these four approaches with the mean squared error (MSE) as the fitness value can be seen in Figs. 5 and 6.

Fragmentation by blasting size prediction Chapter 0.020

357

Swarm size = 30 Swarm size = 40 Swarm size = 50 Swarm size = 60 Swarm size = 30 Swarm size = 40 Swarm size = 50 Swarm size = 60

0.019 0.018 Fitness value

19

0.017 0.016 0.015 0.014 0.013 0

100

200

300

400

500

Iteration FIG. 5 GOA-SVR optimization process (dashed lines represent input Scenario 1 and solid lines represent input Scenario 2).

0.024

Swarm size = 30 Swarm size = 40 Swarm size = 50 Swarm size = 60 Swarm size = 30 Swarm size = 40 Swarm size = 50 Swarm size = 60

Fitness value

0.022 0.020 0.018 0.016 0.014 0.012

0

100

200 300 Iteration

400

500

FIG. 6 KHA-SVR optimization process (dashed lines represent input Scenario 1 and solid lines represent input Scenario 2).

5

Conclusion

Blasting fragment prediction is always a crucial task in mining engineering. However, due to the complexity and uncertainty of blasting operations and environment, MFS is difficult to be predicted. Regarding this, two kinds of novel prediction methods were employed to predict MFS, i.e., GOA-SVR and KHA-SVR in this study. Meanwhile, two different input scenarios were

358 Applications of artificial intelligence in mining and geotechnical engineering

tested and compared based on the correlation analysis results from “Spearman”, “Pearson” and “Kendall”. MAE and R2 were calculated and compared based on different prediction approaches. Finally, it can be concluded that proposed prediction approaches are feasible and stable. In the future study, more advanced AI techniques are worthwhile to be investigated and applied in the area of blasting operations [30–38]. Meanwhile, more data and parameters of blasting fragment are expected to bring better prediction performance. Finally, proposed GOA-SVR and KHA-SVR prediction methods can also be considered applying to other subjects.

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[14] R. Biswas, E. Li, N. Zhang, S. Kumar, B. Rai, J. Zhou, Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete, Constr. Build. Mater. 346 (2022) 128483. [15] R. Biswas, P. Samui, B. Rai, Determination of compressive strength using relevance vector machine and emotional neural network, Asian J. Civil Eng. 20 (8) (2019) 1109–1118. [16] E. Kahraman, A.C. Ozdemir, The prediction of durability to freeze–thaw of limestone aggregates using machine-learning techniques, Constr. Build. Mater. 324 (2022) 126678. [17] Z. Jia, Z. Song, J. Fan, J. Jiang, Prediction of blasting fragmentation based on GWO-ELM, in: Shock and Vibration, 2022, p. 2022. [18] R. Amoako, A. Jha, S. Zhong, Rock fragmentation prediction using an artificial neural network and support vector regression hybrid approach, Mining 2 (2) (2022) 233–247. [19] X.Z. Shi, J. Zhou, B.B. Wu, D. Huang, W. Wei, Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction, Trans. Nonferrous Metals Soc. China 22 (2) (2012) 432–441. [20] W. Gao, M. Karbasi, M. Hasanipanah, X. Zhang, J. Guo, Developing GPR model for forecasting the rock fragmentation in surface mines, Eng. Comput. 34 (2018) 339–345. [21] M. Hasanipanah, H.B. Amnieh, H. Arab, M.S. Zamzam, Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting, Neural Comput. & Applic. 30 (2018) 1015–1024. [22] S.K. Sharma, P. Rai, Establishment of blasting design parameters influencing mean fragment size using state-of-art statistical tools and techniques, Measurement 96 (2017) 34–51. [23] E. Li, J. Zhou, X. Shi, D. Jahed Armaghani, Z. Yu, X. Chen, P. Huang, Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiberreinforced cemented paste backfill, Eng. Comput. 37 (2021) 3519–3540. [24] E. Li, F. Yang, M. Ren, X. Zhang, J. Zhou, M. Khandelwal, Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms, J. Rock Mech. Geotech. Eng. 13 (6) (2021) 1380–1397. [25] V. Vapnik, The Nature of Statistical Learning Theory, Springer, 2013. [26] B. Scholkopf, P.L. Bartlett, A.J. Smola, R. Williamson, Shrinking the tube: a new support vector regression algorithm, in: Proceedings of the 11th International Conference on Neural Information Processing Systems, 1999, pp. 330–336. [27] S. Thomas, G.N. Pillai, K. Pal, Prediction of peak ground acceleration using E-SVR, ν-SVR and Ls-SVR algorithm, Geomat. Nat. Haz. Risk 8 (2) (2017) 177–193. [28] S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: theory and application, Adv. Eng. Softw. 105 (2017) 30–47. [29] A.H. Gandomi, A.H. Alavi, Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simul. 17 (12) (2012) 4831–4845. [30] Z. Yu, X. Shi, J. Zhou, D. Rao, X. Chen, W. Dong, et al., Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models, Eng. Comput. 37 (2) (2021) 991–1006. [31] Z. Yu, X. Shi, J. Zhou, Y. Gou, D. Rao, X. Huo, Machine-learning-aided determination of postblast ore boundary for controlling ore loss and dilution, Nat. Resour. Res. 30 (6) (2021) 4063–4078. [32] J. Zhou, E. Li, M. Wang, X. Chen, X. Shi, L. Jiang, Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories, J. Perform. Constr. Facil. 33 (3) (2019) 04019024. [33] J. Zhou, Y. Qiu, M. Khandelwal, S. Zhu, X. Zhang, Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations, Int. J. Rock Mech. Min. Sci. 145 (2021) 104856.

360 Applications of artificial intelligence in mining and geotechnical engineering [34] J. Zhou, Y. Dai, M. Khandelwal, M. Monjezi, Z. Yu, Y. Qiu, Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations, Nat. Resour. Res. 30 (6) (2021) 4753–4771. [35] J. Zhou, X. Shen, Y. Qiu, X. Shi, M. Khandelwal, Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms, Tunn. Undergr. Space Technol. 126 (2022) 104570. [36] J. Zhou, S. Huang, Y. Qiu, Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations, Tunn. Undergr. Space Technol. 124 (2022) 104494. [37] J. Zhou, S. Zhu, Y. Qiu, D.J. Armaghani, A. Zhou, W. Yong, Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm, Acta Geotech. 17 (4) (2022) 1343–1366. [38] J. Zhou, S. Huang, T. Zhou, D.J. Armaghani, Y. Qiu, Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential, Artif. Intell. Rev. (2022) 1–33.

Chapter 20

Application of machine vision in two-dimensional feature characterization of rock engineering Jiayao Chena,b, Dingli Zhanga, Qian Fanga, Hongwei Huangb, and Anthony G. Cohnc,d,e,f a

Key Laboratory for Urban Underground Engineering of Ministry of Education, College of Civil Engineering, Beijing Jiaotong University, Beijing, China, bKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai, China, cSchool of Computing, University of Leeds, Leeds, United Kingdom, dDepartment of Computer Science and Technology, Tongji University, Shanghai, China, e School of Civil Engineering, Shandong University, Jinan, China, fLuzhong Institute of Safety, Environmental Protection Engineering and Materials, and School of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao, China

1

Introduction

In recent years, rock tunnel has developed rapidly as an important part of China’s infrastructure construction. Tunnel construction has also gradually passed the stage of long, large, and deep tunnels. Due to the highly uncertain geological conditions in the surrounding area and the limited number of experienced experts, the construction process faces great challenges, especially in assessing the quality of the surrounding rock and the safety of the excavation. In addition, geological surveys in the initial stage are limited and can hardly reflect the technical and hydrological characteristics of the whole tunnel. Therefore, there is an urgent need to use the continuously circulating information about the tunnel wall to accurately assess the quality of the surrounding rock. Currently, mainly contact and non-contact methods are used for in-situ assessment. Although these methods improve the acquisition of geological parameters, there are also the following problems: (1) although the contact method is simple and direct, its process is dangerous, time-consuming, and subjective; (2) non-contact methods, especially digital photography, are easily influenced Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00018-7 Copyright © 2024 Elsevier Inc. All rights reserved.

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by the external environment. In addition, most non-contact methods lack the recording and analysis of environmental and photographic parameters during the recording process. The number of features that can be extracted by current non-contact methods is relatively limited, and there is also a lack of efficient feature extraction methods, which makes it difficult to integrate expert judgments. Thus, despite the large amount of information contained in the excavated area, there is still a lack of methods for comprehensive, objective, and accurate extraction of geological information. In addition, it is more difficult to build an accurate prediction model for the quality of the surrounding rock based on the extracted key features, which is also a major problem for the evaluation of tunneling safety based on the geological conditions in the field. Therefore, it is of great importance to effectively represent the information about the face of a tunnel using machine vision. In the study of rock mass feature characterization based on two-dimensional images, digital image processing technology (digital photography and image processing technology) is currently the most direct and widely used means [1,2]. Image segmentation algorithms are typical of digital image processing technology; representative algorithms include edge detection algorithms (such as Roberts, Prewitt, Laplacian, Sobel, Canny, wavelet transform, Laplacian of a Gaussian, etc.), line detection algorithms (such as Topographic, Dony, Steger, Hough transfer, Line Segment Detector, FLD, LSWMS, CannyLines, etc.), and other detection algorithms (e.g., Fourier transform, Hough transform, etc.) [3–5]. Deep learning (DL) is a major breakthrough in the field of artificial intelligence in the last decade. In 2006, Professor Geoff Hinton, a computer science department at the University of Toronto, officially opened the deep learning prologue [6,7]. DL method contains a large number of parameters, because of its unique convolutional operations and well to classify and identify image data. Provided that big data is fully utilized, the recognition effect can be far better than conventional methods [8]. As an important branch of artificial intelligence technology, deep learning technology also includes a number of important characteristics: It cannot rely on human experience, it can achieve self-circulation, it can achieve positive feedback, and it can be self-learning. With this background, this chapter examines in detail the application of machine vision in two-dimensional feature characterization of rock tunnels and highlights the current state of research in informatizing the surface of rock tunnels. The second section mainly presents the information acquisition method, the third section presents the research background on traditional image characterization, the fourth section shows the deep learning-based feature characterizations, and the last section is the concluding part.

2 Rock mass information acquisition method In rock tunnel excavation, contact and non-contact measurement methods are the two main methods to obtain information about the current working face

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in the tunnel excavation. Contact measurements in the field are mainly performed by geotechnical engineers using traditional tools such as rulers, geological compasses, calipers, and so on. However, on-site geologists often have to directly expose themselves to the environment of dangerous rock masses such as rock avalanches, pumice, and rock explosions to collect data, and manual measurement and geological sketching during this time are not only dangerous and time-consuming, but also often do not meet the requirements of rapid construction of the project. The results of geologic sketches obtained in this manner often vary from person to person and are strongly influenced by the subjective experience and judgment of the engineers. In addition, a large area of the tunnel working surface cannot be effectively touched due to the limited measurement range of engineering personnel and sometimes can only be roughly estimated by visual measurement, so the artificial geological sketch dominated by contact measurement has major limitations. In practice, due to the negative influence of construction time and subjective geological sketching technology, the important and effective geological information of the working surface cannot be fully obtained, identified, extracted, analyzed and used, and it is impossible to establish effective construction feedback. Therefore, it is of great importance to explore new technologies to obtain the key information of the excavation area accurately and quickly. On the other hand, the non-contact measurement methods to acquire geological information about rock masses mainly include two-dimensional digital images (mainly by digital photography) and threedimensional point cloud data (mainly by three-dimensional reconstruction of images and laser scanning measurements). This study focuses on the acquisition and processing of two-dimensional images. The most widely used method of two-dimensional image acquisition is digital photogrammetry, which is a combination of digital imaging and photogrammetry and uses visual imaging, image matching, image interpolation, pattern recognition, and other multidisciplinary measurement methods [9]. It can accurately and quickly capture the relative relationship and spatial information of the object without touching the measured object, record the condition and position of the measured object in space, and measure the dangerous area that cannot be directly reached by a person. With the development of digital photography technology and deep learning, researchers are increasingly inclined to obtain information from ordinary digital cameras and conduct feature extraction and research, which can now achieve a high degree of measurement accuracy. Meanwhile, digital cameras are less expensive and easy to transport. Therefore, digital photography technology, with its advantages of high efficiency, high precision, and comprehensive information, is also widely used by scientists at home and abroad in the field of engineering geology. After a long period of theoretical research and technical practice, the use of digital photo technology to obtain information about the structural surface of rock has matured and is gradually showing advantages in practical application.

364 Applications of artificial intelligence in mining and geotechnical engineering

Tape

Camera (a)

LED LighƟng

Micro Phone (b)

FIG. 1 Schematic diagram of representative methods for 2D image field acquisition [10,11]. (A) Fixed acquisition mode of camera with tripod. (B) Convenient access to handheld phone.

As shown in Fig. 1, the capture method for two-dimensional image scenes has evolved from digital cameras to portable cell phones [10,11]. It can be seen that the threshold for obtaining two-dimensional data samples is lowered year by year to ensure the quality of the samples. Exploration of digital photography in geological engineering began early, and much research has been done. Among them, Ross-Brown and Atkinson [12] used the images captured by the camera to characterize the structural surfaces of rock masses for the first time and obtained information such as the trend and trace length of the major structural surfaces of rock masses for the first time. In 2003, CSIRO Australia launched Ciro3D and Sirojoint, a digital photography-based rock image modeling and processing software that can be used to analyze fracture characteristics of rock masses [13]; Ohnishi et al. [14] combines digital photography technology with marker point data to calculate the three-dimensional coordinate information of key points of the rock slope and monitor the deformation data of the slope. Lepisto et al. [15] applied a combination of color space and texture analysis to classify image samples of rock masses. Miura et al. [16] used digital camera measurements to calculate tunnel convergence and compared the results with on-site total station measurement results, confirming the high accuracy of digital photogrammetry. Mohammadi and Barati [17] used digital photography to obtain images of rock blocks during tunnel excavation by drilling and blasting and used graphical methods to analyze block distribution. Yang et al. [18] used digital imaging technology to obtain the equivalent variable development field of the rock sample and effectively monitor the rock fracturing process. Zhang et al. [19] used digital photography to determine the size and shape of the slag particles produced by the tunnel boring machine (TBM) to show the potential relationship between the slags, TBM operating parameters, and rock mass quality. Leu and Chang [20] employed image processing and information management technologies to store, manage, process, and visualize images of the face of the new tunnel, identify key geological features such as fractured strata and joint fractures on the tunnel face, and effectively assist engineers in capturing geological features. As can be seen, the current application of digital photography technology in rock engineering is mainly focused on basic theoretical research and related key technologies [21,22], surface investigation

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of rock structure [23], and landslide monitoring [24,25]. The use of digital imaging technology to process the obtained rock surface photos has significant advantages, such as fast processing speed and high degree of automation, but the calculation accuracy, detection efficiency, and robustness are significantly affected by discontinuous surface texture, color, water stains, shadows, and other factors, and the traditional digital image processing algorithm for feature extraction is cumbersome, and the extraction accuracy is greatly affected by the distortion of image projection. Three-dimensional information on rock structures (e.g., discontinuous surface structures) is not applicable.

3

Traditional image algorithms

Researchers have previously applied image processing algorithms for rock feature detection, such as Krishnan and Sommer III [26] in combination with the Fourier transform and the Hough transform, to generate accurate quantitative information related to image texture (e.g., fracture direction, distribution, and amount) to support rock mass stability analysis. Fitton and Cox [27] proposed an improved Hough transform algorithm for extracting fracture parameters in rock mass images, and this shows that pre-processing and noise reduction of the image before the Hough transform significantly improves the results, especially in the extraction of short fractures. Lepisto et al. [15] use an effective natural color texture classification method to extract texture features from twodimensional images of rock samples and enable accurate classification of rock samples with uneven texture. Lemy and Hadjigeorgiou [1] use various types of edge and straight line detection algorithms to extract the discontinuous surface traces of rock masses and compare and select algorithms suitable for in-situ fracture segmentation of rock masses. In summary, digital imaging technology has achieved a large number of satisfactory research results in the identification and segmentation of rock mass features, but currently such methods are mainly used for rock mass fracture occurrence-related features such as trace length, dip, distribution, spacing, openness, roughness, etc. Havermann and Vogt [28] integrate the TUCIPS system for calculating and characterizing blockiness based on images of rock blocks and have been proven in practice with ideal accuracy. Sirveiya and Thote [29] comprehensively analyze the overall effect of rock blasting by analyzing the joints and blockiness of rock images to describe the strength and structure of the rock. Blom and Daily [30] provide an overview of the preprocessing and analysis techniques applied to radar images in geological problems such as rock type identification and incorporate texture information into the classification analysis of different rock types, making radar images a good medium for extracting texture measurements. Kemeny et al. [31] used a high-resolution camera to capture images in the field, calculated the size distribution of rock fragments using a computer program, and verified the accuracy of the method by comparing it to laboratory experiments. The above features (i.e., line, edge, and other detection strategies, shown in Fig. 2) are the main research targets of the current rock mass based on pixel

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FIG. 2 Traditional image algorithms and corresponding results for rock mass features, including line [27], edge [1], and other detection [32] algorithms. (A) Line detection algorithm. (B) Edge detection algorithm. (C) Edge and other detection algorithms.

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information [32], but the image features with semantic information as the core cannot be obtained by digital image processing technology. Therefore, it is urgent to develop a feature extraction algorithm that can be evaluated based on the engineer’s experience of rock mass features to realize the identification and extraction of image features with high robustness and high automation.

4

Deep learning algorithms

Convolutional neural network (CNN) is a kind of feedforward neural network with convolutional operation and deep structure, which is one of the representative deep learning algorithms. Traditional neural networks usually use full connection mode, which leads to problems such as large parameters, high training time, high energy consumption, and even difficult training. As shown in Fig. 3, CNN realizes the local connection and weight sharing of neurons through convolution operations, which means it is a kind of incompletely connected network. The construction system of CNN can greatly reduce the difficulty of network training and improve the comprehensive performance of the model, which is why CNN is also regarded as one of the most important algorithms in the field of deep learning. Although deep learning algorithms have been widely used in engineering for more than a decade, they have played a major role in disease detection and health monitoring. However, the rock mass features often show the phenomenon of delay in application due to the complexity of the sample itself and the difficulty of acquisition, especially the long-term limitation of the working area of the rock tunnel during the construction period due to the light conditions and complex construction procedures, resulting in a poor photographic environment and short usage time. In recent years, DL technology has been successfully used in tunneling during construction, including rock lithology classification and recognition, rock block and particle size analysis, rock fracture analysis, rock surface structure analysis and research.

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FIG. 3 Learning hierarchy CNN architecture [32].

368 Applications of artificial intelligence in mining and geotechnical engineering

4.1 Classification and detection of lithology of rock mass The complex and variable geological structure and frequent construction processes in rock tunnels have greatly increased the difficulty of monitoring and stability assessment of the working surface structure, and if timely and accurate prediction is not made, it is very likely that the rock structure will be severely damaged. The structural damage to the surrounding rock mass is often accompanied by the collapse of the working surface and the obstruction of construction work, which poses great challenges to normal tunnel construction. Accurate identification of the apparent structure will help engineers understand the rock mass structure for the first time and predict the deformation law of the surrounding rock. In the research of lithology classification and rock mass detection, Xu et al. [33] proposed a detector based on RPN and Fast R-CNN module for intelligent lithology identification method for rock images, which had higher accuracy and more stable detection capability when comparing the model with YOLO V4 model. Li et al. [34] used the VGG-16 architecture based on deep transfer learning to automatically classify four groups of Martian rocks, and the accuracy of the model for Martian rock images reached 100%. Ran et al. [35] used a convolutional neural network to classify images of six rock types from slopes collected in the field, and the results showed that the overall classification accuracy was higher than that of other CNNs and linear classification models, tentatively solving the problem of identifying rock types at current construction sites. Li et al. [36] used the Inception-v3 model and the transfer learning algorithm to realize the identification and classification of different rock lithologies. The results showed that the model can automatically search for rock image pixels and their features instead of manually extracting rock features, and that the model had good generalization ability and robustness. Zhang [37] used a generative adversarial network to identify microscopic rock datasets, combined with asymmetric residuals and global residual learning modules to more comprehensively fuse apartment convolutional network features and transfer apartment features to the end of the network, and the results showed that the network had an ideal effect for various types of rock detection. Xu et al. [38] proposed a transfer learning model combining deep supervised object recognition and ResNet network to identify rock mass lithology, which showed that artificial intelligence had wide applications in the field of geoscience. Liu et al. [39] applied a CNN neural network to predict lithology in seismic reservoirs to realize the nonlinear inversion of lithology identification. The results showed that the proposed method could effectively solve the problem of predicting complex lithology in seismic lithofacies. Wang and Wang [40] established a convolutional neural network for classifying various rocks on rocky slopes, and the results showed that deep learning had satisfactory performance in fast and automated identification of rocks on slopes and boundary identification. Also, the representative lithologic detections and datasets of the current studies are shown in Fig. 4. It could be seen that the differences between each dataset were still very large. More and more cases of lithological data are being studied to

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(a)

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FIG. 4 Representative lithologic detections and datasets. (A) Rock lithology detections. (B) Rock type classification datasets.

develop intelligent detection methods. If the barrier between the collection of lithologic data is to be broken, it makes more sense to further increase the diversity and quantity of samples.

4.2 Analysis of rock mass block and particle size In rock mass and particle size analysis, Bamford et al. [41] used deep neural network architecture to predict the size of rock sections from two-dimensional images of rock piles, and the results showed that the deep learning method has good accuracy compared to artificial image labeling. Alqahtani et al. [42] applied ResNet and ResNext to learn the geometry of pore space in threedimensional images of porous media and achieved an ideal level of automation and scalability. Zhou et al. [43] proposed a deep-learning algorithm consisting of an MSD-UNet network with multiscale input and lateral output, and a postprocessing algorithm to enable automatic surface segmentation and size and shape assessment of rock chip images. Karimpouli and Tahmasebi [44] used a convolutional neural network and a self-coding module to implement rock chip segmentation and extended the dataset based on cross-correlation simulations. The results showed that the CNN algorithm results are more accurate and reliable than conventional multiphase segmentation methods. To improve the efficiency and accuracy of conventional artificial particle size analysis, Cheng and Guo [45] proposed a particle size analysis method for thin film images based on convolutional neural network to select and extract image samples,

370 Applications of artificial intelligence in mining and geotechnical engineering

and the results showed that CNN networks can classify rock images with high reliability. In the analysis and exploration of rock structures, Chen et al. [46] used the Inception-ResNet-v2 deep learning model to classify five different types of rock structures collected in the field, which enabled highly accurate classification of rock structures. Liu et al. [47] used the Mask-RCNN convolutional network to perform case segmentation of the rock mass drill core, from which the single-row core was automatically identified and the core RQD value was calculated to describe the rock mass geology in detail. Wang et al. [48] used a deep-learning RNN model to intelligently extract the roughness of the three-dimensional structural surface of the rock, reducing the high labor cost of roughness extraction and improving the automation level of mine geological characterization. Fig. 5 presents a representative case of extraction

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FIG. 5 Representative cases of extraction and calculation of block and particle size. (A) Particle size distribution of rock mass. (B) Calculation of rock block size. (C) Rock quality design and stratum calculation [41,43,47].

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and statistics of block and particle size. It can be seen that the geometric features of the irregular boulders have been largely characterized. However, there is still much room for performance improvement, for example, in correlating block features and evaluating fractures that provide ideas for stability analysis.

4.3 Analysis of rock fracture In research on fracture analysis of rock joints, rock fracture is also an important structural feature, which is a measurable line segment formed by the intersection of discontinuous surfaces on the surface of the rock. Mastering the quantitative characteristics of rock joints and fractures has a major impact on accurately determining the surrounding rock classes and establishing construction parameters. Currently, the characteristics of these joint fractures mainly include trace length, slope, density, strength, spacing, etc. [49–51]. The conventional method of making fracture trace diagrams of face installations is usually carried out by engineers in the field using the naked eye in combination with contact tools, but engineers must accept the safety risks of possible tunnel collapse due to close contact with the face. Obviously, manual inspection is a timeconsuming and labor-intensive task and often leads to large deviations or even errors due to differences in expert experience and observation angles [52,53]. Therefore, there is an urgent need to explore a computer vision-based inspection method to automatically extract and evaluate rock tunnel face information. Lee et al. [54] proposed a semi-automatic method based on semantic segmentation through deep learning to detect common traces from digital images and calculate the length distribution of traces using three-dimensional data structures. It was worth mentioning that the encoding/decoding module and the hole convolution are the advanced modules of the current deep learning, which was first proposed and successfully developed by Liang-Chieh Chen [55], which greatly improved the accuracy and efficiency of semantic segmentation of images and provided a basic framework for the development of subsequent algorithms. For example, Chen et al. [11] proposed an image-based model for automatic rock fracture segmentation and quantification, which mainly integrated the encoding-decoding and void convolution modules, and the results showed that it had higher performance in pixel-level crack trace extraction and noise reduction. Liu et al. [56] used the AlexNet network in combination with a traditional image edge detection algorithm to realize the image recognition of rock joint fractures, roughness, and fracture degree, and first performed rock mass classification. Fig. 6 shows the case of characterization of boulder geometric

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FIG. 6 Representative extractions of rock fracture occurrence [11,57].

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parameters. It can be seen that the occurrence of current fractures was characterized very well. However, the accuracy of the deep learning algorithm can be significantly improved for samples with complex trace patterns or nonsignificant fracture patterns.

4.4 Analysis of other rock mass parameters As for research on other rock mass parameters, current deep learning research on the obvious features of rock masses is mainly concerned with the weak interlayer and groundwater. With the frequent excavation of tunnels, the problems of difficult detection, delayed identification, and inaccuracy of the weak interlayer are becoming more acute. If not accurately predicted, this will most likely lead to face collapse and impede construction, undoubtedly creating major challenges for tunneling. Chen et al. [52] used a deep convolutional neural network-based method DeepLab V3+ for detecting and quantifying weak interlayers in rock masses, and finally performed semantic segmentation of four rock mass images. The results showed that the model can effectively realize the semantic segmentation of complex interlayers and eliminate more noise. In groundwater extraction, water ingress during tunnel construction often leads to disasters such as collapse and debris flow, which inevitably leads to great economic and financial losses [58–60]. Undoubtedly, the stability of the tunnel wall is one of the most important concerns of engineers when excavating rock tunnels in water-rich areas [61]. The quantitative evaluation of the apparent water volume of the tunnel is of great importance for the stability evaluation of the tunnel face. This is because the effective evaluation of the water body of the tunnel face helps to quickly and accurately determine the tunnel face before a water collapse disaster occurs. Therefore, quantifying the water flow data of the working face is critical for engineers in the field to assess the rock mass condition and subsequently make appropriate construction decisions. Chen et al. [62] proposed a groundwater assessment method using a convolutional neural network (shown in Fig. 7) that simulated an engineer’s typical recognition process, combined with image classification and semantic segmentation. The results showed that removing dry samples early in the classification process could greatly reduce the segmentation process and improve segmentation accuracy.

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Go into next step

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FIG. 7 Representative extraction of other rock mass parameters [62].

Damage Segmentation and Extraction

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Conclusion

Computerized image processing methods can effectively replace the rapid and accurate extraction of key features of a rock tunnel by hand. Digital image processing technology essentially involves the use of various algorithms to summarize and analyze the size and distribution of pixels and other feature extraction laws. The traditional graphical algorithm mainly relies on pixel information as the main target of research, but the image features with semantic information as the core still cannot be captured by digital image processing technology, such as the weak interlayer, groundwater, structural categories and other features affected by the classification system of the surrounding rock. The possible reason is that the features of such rock masses with semantic information often need to be determined by involving expert experience, and it is difficult to summarize the corresponding distribution laws in the form of image pixels, coupled with the color, contrast, noise disturbance, noise disturbance of different types of rock masses. A number of features, such as gray levels, are highly variable, making pixel summarization and feature extraction much more difficult. The potential of deep learning in computer vision-based image recognition cannot be underestimated, e.g., classification, recognition, segmentation, visualization, etc. With the continuous improvement of digital photography and image processing algorithms, deep learning has been applied to the study of rock features in the past 5 years, especially the aspects of lithology, rock fractures, rock mass, structural surface, and other commonly used features. However, the major challenges of deep learning in extracting two-dimensional engineering features of rock masses are (1) the current rock samples mostly focus on slopes or bare rock, and there are large differences in rock structure and features that occur during tunnel excavation during construction, which makes image recognition difficult; (2) rock feature extraction is mainly used to determine the structural stability of rock, and more details of rock need to be excavated. (3) Some important features have not been deeply characterized in the visual information due to the complex distribution of pixels and textures and the difficulty of identification, such as the degree of weathering and the degree of fragmentation. Therefore, by using deep learning technologies based on artificial intelligence, it is possible to “fine-tune” the safety risk of rock tunnels.

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376 Applications of artificial intelligence in mining and geotechnical engineering [4] R.M. Haralick, L.G. Shapiro, Image segmentation techniques, Comput. Vis. Graph. Image Process. 29 (1) (1985) 100–132. [5] D.L. Pham, C. Xu, J.L. Prince, Current methods in medical image segmentation, Annu. Rev. Biomed. Eng. 2 (1) (2000) 315–337. [6] G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18 (7) (2006) 1527–1554. [7] G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313 (5786) (2006) 504–507. [8] D. Rotman, Mit technology review. Meet the Man with a Cheap and Easy Plan to Stop Global Warming, 2013, Retrieved from http://www.technologyreview.com/featuredstory/511016/acheap-and-easy-plan-to-stop-globalwarming. [9] W. Linder, Digital Photogrammetry, Springer, 2009. [10] W. Cai, H. Zhu, W. Liang, B. Vu, C. Su, K. Zhang, X. Wang, Three-dimensional forward analysis and real-time design of deep tunneling based on digital in-situ testing, Int. J. Mech. Sci. (2022) 107385. [11] J. Chen, M. Zhou, H. Huang, D. Zhang, Z. Peng, Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning, Int. J. Rock Mech. Min. Sci. 142 (2021) 104745. [12] D.M. Ross-Brown, K. Atkinson, Terrestrial photogrammetry in open-pits: 1-descripion and use of the Phototheodolite in mine surveying, Inst. Mining Metall. 81 (1) (1972) 7–11. [13] G. Roberts, G. Poropat, Highwall Joint Mapping in 3-D at the Moura Mine Using SOROJOINT, 2000. [14] Y. Ohnishi, S. Nishiyama, T. Yano, H. Matsuyama, K. Amano, A study of the application of digital photogrammetry to slope monitoring systems, Int. J. Rock Mech. Min. Sci. 43 (5) (2006) 756–766. [15] L. Lepisto, I. Kunttu, J. Autio, A. Visa, Classification method for colored natural textures using gabor filtering, in: 12th International Conference on Image Analysis and Processing, 2003. Proceedings, IEEE, 2003, pp. 397–401. [16] S. Miura, T. Yamamoto, I. Kuronuma, M. Imai, Vision metrology applied for configuration and displacement, Int. J. JCRM 1 (1) (2005) 1–6. [17] H. Mohammadi, B. Barati, Development of a rock fragmentation model for using in tunnel blasts, Geotech. Geol. Eng. 36 (4) (2018) 2379–2390. [18] S. Yang, D. Yang, H. Jing, Y. Li, S. Wang, An experimental study of the fracture coalescence behaviour of brittle sandstone specimens containing three fissures, Rock Mech. Rock. Eng. 45 (4) (2012) 563–582. [19] X.-P. Zhang, W.-Q. Xie, K.-Y. Cai, Q.-S. Liu, J. Wu, W.-W. Li, Evaluation of rock muck using image analysis and its application in the TBM tunneling, Tunn. Undergr. Space Technol. 113 (2021) 103974. [20] S.-S. Leu, S.-L. Chang, Digital image processing based approach for tunnel excavation faces, Autom. Constr. 14 (6) (2005) 750–765. [21] W. Qiuping, Z. Shunbing, D. Chunquan, Study on key technologies of internet of things perceiving mine, Proc. Eng. 26 (2011) 2326–2333. [22] H. Xing, Q. Zhang, C. Braithwaite, B. Pan, J. Zhao, High-speed photography and digital optical measurement techniques for geomaterials: fundamentals and applications, Rock Mech. Rock. Eng. 50 (6) (2017) 1611–1659. [23] D. Hou, X. Zheng, Y. Zhou, C. Gong, C. Wang, Analysis and application of surrounding rock mechanical parameters of jointed rock tunnel based on digital photography, Geotech. Geol. Eng. (2022) 1–19.

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378 Applications of artificial intelligence in mining and geotechnical engineering [45] G. Cheng, W. Guo, Rock images classification by using deep convolution neural network, J. Phys. Conf. Ser. 887 (2017) 012089. [46] J. Chen, T. Yang, D. Zhang, H. Huang, Y. Tian, Deep learning based classification of rock structure of tunnel face, Geosci. Front. 12 (1) (2021) 395–404. [47] F. Liu, Y. Liu, T. Yang, J. Xin, P. Zhang, X. Dong, H. Zhang, Meticulous evaluation of rock mass quality in mine engineering based onmachine learning of core photosx, Chin. J. Geotech. Eng. 43 (5) (2021) 968–974. [48] Y. Wang, J. Lu, Y. Jia, Intelligent extraction method of 3D rock structure surface roughness and its application, Min. Res. Dev. (2021). [49] Y. Fadakar Alghalandis, ADFNE: open source software for discrete fracture network engineering, two and three dimensional applications, Comput. Geosci. 102 (2017) 1–11. [50] D. Healy, R.E. Rizzo, D.G. Cornwell, N.J.C. Farrell, H. Watkins, N.E. Timms, E. Gomezrivas, M. Smith, FracPaQ: a MATLAB™ toolbox for the quantification of fracture patterns, J. Struct. Geol. 95 (2017) 1–16. [51] C. Zeeb, E. Gomezrivas, P.D. Bons, S. Virgo, P. Blum, Fracture network evaluation program (FraNEP): a software for analyzing 2D fracture trace-line maps, Comput. Geosci. 60 (2013) 11–22. [52] J. Chen, D. Zhang, H. Huang, M. Shadabfar, M. Zhou, T. Yang, Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning, Autom. Constr. 120 (2020) 103371. [53] C. Zhang, K. Han, D. Zhang, Face stability analysis of shallow circular tunnels in cohesive–frictional soils, Tunn. Undergr. Space Technol. 50 (2015) 345–357. [54] Y.-K. Lee, J. Kim, C.-S. Choi, J.-J. Song, Semi-automatic calculation of joint trace length from digital images based on deep learning and data structuring techniques, Int. J. Rock Mech. Min. Sci. 149 (2022) 104981. [55] G.P. Liang-Chieh Chen, I. Kokkinos, K. Murphy, A.L. Yuille, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. CV, 2017, pp. 1–14. [56] H. Liu, W. Li, H. Zha, W. Jiang, T. Xu, Method for surrounding rock mass classification of highway tunnels based ondeep learning technology, Chin. J. Geotech. Eng. 40 (10) (2018) 1809–1817. [57] J. Chen, Y. Chen, A.G. Cohn, H. Huang, J. Man, L. Wei, A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face, J. Rock Mech. Geotech. Eng. (2022). [58] L.G. Baltazar, F.M.A. Henriques, F. Jorne, M.T. Cidade, Combined effect of superplasticizer, silica fume and temperature in the performance of natural hydraulic lime grouts, Constr. Build. Mater. 50 (2014) 584–597. [59] I. Indacoechea-Vega, P. Pascual-Munoz, D. Castro-Fresno, M.A. Calzada-Perez, Experimental characterization and performance evaluation of geothermal grouting materials subjected to heating-cooling cycles, Constr. Build. Mater. 98 (2015) 583–592. [60] S. Li, B. Liu, X. Xu, L. Nie, Z. Liu, J. Song, H. Sun, L. Chen, K. Fan, An overview of ahead geological prospecting in tunneling, Tunn. Undergr. Space Technol. 63 (2017) 69–94. [61] J.Y. Rafi, H. Stille, Basic mechanism of elastic jacking and impact of fracture aperture change on grout spread, transmissivity and penetrability, Tunn. Undergr. Space Technol. 49 (2015) 174–187. [62] J. Chen, M. Zhou, D. Zhang, H. Huang, F. Zhang, Quantification of water inflow in rock tunnel faces via convolutional neural network approach, Autom. Constr. 123 (2021) 103526.

Chapter 21

Groundwater potential assessment in Dobrogea region of Romania using artificial intelligence and bivariate statistics Romulus Costache National Institute of Hydrology and Water Management, Bucharest, Romania

1

Introduction

Within the critical zone of the Earth’s crust, groundwater is one of the most vital resources. In addition to providing a source of water for domestic, industrial, agricultural, and industrial purposes, it also serves as a development tool for other groups. There exists an urgent need for quantitative methodologies by which to evaluate groundwater production in the aquifer system as a result of the increased demand for high-quality water, as well as the pressures that are expected to result from global climate change [1,2]. There has been a relatively short history of investigative research in this field, so sound tools to assess the efficiency of aquifers have not been widely adopted because of the relative lack of sound tools to do so [3]. The development of a reasonable model for the potential of groundwater is, therefore, one of the most essential activities to secure the future development of groundwater resources, as well as their efficient management and sustainable use [4]. Several factors affect the appearance and movement of groundwater in a particular area, especially fractured bedrock aquifers, in terms of topography, lithology, geological structures, fracture density, aperture and connectivity, secondary porosity, the distribution of groundwater tables, the recharge of

Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00009-6 Copyright © 2024 Elsevier Inc. All rights reserved.

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groundwaters, the slope, the drainage pattern, the landforms, the land use and the land cover of the area, and the interrelationships between all of these factors [5]. There is no doubt that conventional exploration methods, such as hydrogeological and geophysical resistivity surveys conducted on the ground, are not always able to account for the occurrence and movement of groundwater due to the diverse factors that govern their movement and occurrence. The use of remote sensing and geographic information system (GIS) techniques in groundwater hydrology has great potential due to the wide range of information they can provide. Geospatial information systems (GIS) are valuable tools for handling spatial data and making informed decisions in many different areas, including geology and the environment [6]. The use of remote sensing is one of the most important methods for collecting information about features of the surface, including watercourses, land uses, and landforms, which are related to groundwater. A GIS environment can be easily used to bring together such information with other types of data, which can then be analyzed before integration with other data types [7]. Traditionally, for identifying, determining, and mapping the boundaries of groundwater potential zones, ground surveys have been conducted using geophysical, geological, and hydrogeological methods, which are generally expensive and time-consuming, and are generally based on ground surveys using geophysical, geological, and hydrogeological tools [8]. All these factors were taken into account during the last years and were integrated in many advanced multicriteria decision-making analysis or machine learning techniques intended to provide better accuracy for the groundwater potential assessment. From this category of algorithms, we should mention: AdaBoost, random forest, classification and regression tree [9], logistic regression, cascade generalization, dagging [10], analytical hierarchy process [11], support vector regression, and convolutional neural network [12]. At the level of Romanian literature, there is a single study in which the groundwater potential was estimated using two multicriteria decision-making methods like analytical hierarchy process and catastrophe theory [13]. Therefore, we consider that a more in-depth study of this scientific topic is necessary at the level of Romania. Given these aspects, the present study will propose a new approach for Romanian territory, in which machine learning advanced algorithms will be used to estimate the groundwater potential within the Dobrogea region. The involved methods will be represented by: support vector machine (SVM), deep learning neural network, and classification and regression tree. Also, the frequency ratio bivariate statistical method will be involved to derive the

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coefficients for each class/category of groundwater potential predictors. The validation of the results will be ensured by using the ROC curve method.

2

Study area

The present study is focused on the Dobrogea region. It is located in the southeastern part of Romania and is composed of two counties: Tulcea and Constanta. The eastern limit of Dobrogea is the Black Sea, the northern limit is represented by the Chilia arm of the Danube, which also represents the border with Ukraine, the western limit is represented by the Danube, while the southern limit is represented by the state border with Bulgaria. This region covers an area of about 15,500 km2 and has three main landform units: The Dobrogea plateau, which comprises the majority of the zone to be analyzed, the Razim-Sinoe lagoon complex in the north-eastern part of the zone, and the Danube Delta in the north-east. A typical multiannual temperature range within this region ranges between 10.1 and 11.8°C, while the mean annual rainfall falls between 257.5 and 535 mm and the mean potential evapotranspiration falls between 639.2 and 700.9 mm, respectively [14]. It is for this reason that Dobrogea is known as the driest region in the country in terms of its climate. The high aridity conditions are the result several of specific factors, such as the Eurasian anticyclone (humidity deficit generator) that is present throughout the year. In Romania, the analyzed region lies at the extremes of oceanic influences, which drastically affect the formation and intensity of coastal thermal inversions (precipitation formation inhibitors), as well as the influence of the Black Sea. Hydrological and groundwater quality conditions within the area are affected by the climate characteristics and also due to a number of natural and anthropogenic conditions. Availability of groundwater can be an issue in some places, and its quality can be an issue in others, while in others, both are issues.

3

Data

3.1 Wells inventory In most cases, the assessment of the groundwater potential is done by analyzing the data collected about good yields. In the present study, 154 wells were identified throughout Dobrogea. To be included in the groundwater potential estimation methods based on the application of machine learning, the 154 wells were divided into two samples: 70%—training (108 wells) and 30%— validating (46 wells) (Fig. 1).

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FIG. 1 Study area location.

3.2 Groundwater predictors Among the most significant characteristics of the terrain is its slope, which is an obvious indication of the steepness of its surface. It is essential to understand the slope to gain a better understanding of the geological and geodynamic processes that are taking place on a regional scale. The slope of the surface affects the rate of runoff and infiltration from the surface as well as the rate of rainfall [15]. There is less recharge on steep slopes because the water that is received during rainfall flows down the slope rapidly as a result of precipitation. In this case, there is not enough time for the water to infiltrate and recharge the saturated zone because it does not have sufficient residence time. To recharge groundwater systems, hydrological soil group and lithology play a significant role in determining how much water can infiltrate into the subsurface formations. There are two main factors that must be taken into consideration when estimating the rate of infiltration of a soil group: hydrological characteristics and hydraulic characteristics. Among all the water sources that make up the hydrological cycle, rainfall is

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one of the most important factors that affect the groundwater of an area most significantly. The amount and duration of rainfall determine the amount and duration of infiltration. In the presence of high-intensity and short-duration rains, infiltration is less influenced than surface runoff; in the absence of high-intensity and long-duration rains, infiltration is more influenced. To estimate groundwater potential, TWI plays an important role, since it measures the relationship between topography and moisture. In the preparation of an aspect map, which indicates the relationship between water accumulation and retention on the surface, nine classes were selected that indicates the degree to which water can accumulate and be retained. The convergence index can highlight very useful information regarding the degree of river network concentration across a specific region. Plan and profile curvatures are also two very important morphometric conditioning factors that will help to create a clear overview regarding the groundwater potential across the study area. The distance to rivers is another important influencing factor for the groundwater potential that will be also considered in the present research work (Figs. 2–4).

FIG. 2 Groundwater predictors: (A) Slope; (B) rainfall; (C) aspect; and (D) TWI.

384 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 3 Groundwater predictors: (A) Profile curvature; (B) convergence index; (C) lithology; and (D) distance to rivers.

FIG. 4 Groundwater predictors: (A) Hydrological soil group and (B) plan curvature.

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Methods

4.1 Multicollinearity assessment It was necessary to investigate multicollinearity among groundwater predictors before applying machine learning models to these data. The multicollinearity assessment will make us able to remove the problematic observations from the groundwater potential predictors data before applying these models [10,11]. There can be groundwater conditioning factors that have near-linear relationships with each other and this is referred to as multicollinearity. The variance inflation (VIF) and tolerance (TOL) were used to investigate the multicollinearity issues [16].

4.2 Weights of evidence (WOE) Normally, reports of WOE can be classified as a nonlinear statistic based on the log-linear model of Bayesian probability that is employed in the method [17]. The WOE method is based on the natural logarithm of relative risk and the ratio of conditional probabilities for the presence or absence of a response for a set of discrete, categorical variables, weights represent the statistical relationship between the predictor variable and the response [18]. Based on Bayes’ theorem, conditional probabilities are calculated based on categorizing continuous variables. The next equations are used to calculate the WOE coefficients for each class or category of groundwater predictors [19]: PfBjSg W + ¼ ln  P BjS  P BjS  W ¼ ln  P BjS

(1)

(2)

where W+ represents the positive weight, W represents the negative weight, P is equal to the probability, B is defined as the presence of groundwater predictor, B is the absence of a groundwater predictor, S is the presence of a well, S the absence of well. The final weight or WOE coefficient can be calculated using the following formula [20]: Wf ¼ Wplus + Wmintotal  W min,

(3)

where Wplus represents the value of the positive weight associated with a class predictor, Wmin represents the value of the negative weight associated to a class predictor, and Wmintotal is the sum of all negative weights from a predictor.

4.3 Support vector machine (SVM) A SVM is one of the most versatile systems for classification and regression, which was created based on the statistical learning theory [21,22]. With the

386 Applications of artificial intelligence in mining and geotechnical engineering

help of a decision surface, it separates the classes into two groups maximizing the margin between the two groups. An ideal hyper-plane is often called a surface, and the points on the surface that are closest to the hyper-plane are called support vectors [23,24]. For the training set to be successful, support vectors must be used correctly. There is a variety of SVM algorithms out there that can be used to estimate functions, and these algorithms often involve the use of decision rules that are based on convex (quadratic) optimization problems, which are implemented using SVM algorithms [25,26]. By minimizing the normality of the hyper-plane’s normality, the classification approach aims at maximizing the margin between the two classes, rather than simply separating them. There are two main advantages of using a hyper-plane with a large margin over a hyper-plane with a small margin, these are its greater resistance to noise and better generalization [27,28]. It can be concluded that the uniqueness of the solution to a problem is one of the significant advantages of SVMs when compared with other data mining methods (e.g., multi-layer perceptrons). Fig. 5 highlights the optimal support vector machine hyperplane in both cases: linearly and non-linearly separable [29].

FIG. 5 Optimally hyper-plane: (A) Linearly separable and (B) non-linearly separable.

It is worth noting that the input data for SVM algorithm was represented by Weights of Evidence coefficients calculated according to the steps described above. The application of SVM was facilitated by Weka 3.9 software.

4.4 ROC curve for validation It is common to use the ROC curve as a tool for testing the predictive accuracy of a model, where the AUC value is between 0 and 1, the x-axis indicates how

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many false positives were generated, and the y-axis shows how many true positives were generated [30]. There is usually an AUC calculation used in prediction analysis as a way of indicating the accuracy of the analysis. An AUC value is a measure of how much area around the lower axis of the curve is surrounded by the line, which is typically within the range [0.5–1] [31]. It is considered reliable when the AUC is greater than or equal to 0.7. To be able to compare the ROC curves of all models conveniently, both positive and negative examples must be determined [32]. The accuracy of the results was assessed using the AUC of the groundwater inventory map.

5

Results and discussion

5.1 Multicollinearity assessment As it was noted in Section 4.1, two multicollinearity indices (TOL and VIF) were calculated for the 10 groundwater predictors. In terms of TOL, it can be seen that the aspect factor has the highest TOL value equal to 0.948. It is followed by profile curvature (0.938), distance to rivers (0.928), hydrological soil groups (0.898), slope (0.878), rainfall (0.868), convergence index (0.848), elevation (0.838), TWI (0.828), and plan curvature (0.818). If we bring into discussion the VIF values we will see that slope predictor has associated the highest one (1.222), followed by rainfall (1.202), convergence index (1.172), aspect (1.152), plan curvature (1.132), elevation (1.122), TWI (1.102), distance to rivers (1.092), profile curvature (1.052), and hydrological soil groups (1.052) (Table 1). According to these results, it can be concluded that no multicollinearity was detected among the groundwater predictors.

TABLE 1 VIF and TOL values for groundwater potential independent variables. Groundwater predictor

TOL

VIF

Slope

0.878

1.222

Plan curvature

0.818

1.132

TWI

0.828

1.102

Aspect

0.948

1.152

Convergence index

0.848

1.172

Hydrological soil groups

0.898

1.052

Distance to rivers

0.928

1.092

Rainfall

0.868

1.202

Elevation

0.838

1.122

Profile curvature

0.938

1.052

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5.2 Weights of evidence The application of Eqs. (1)–(3) revealed that the highest weights of evidence coefficient was assigned to the lithological category represented by marl, gray clays, gypsum, and salt and was equal to 0.97. The next positions were located on the following classes/categories of factors: distance to rivers between 400 and 700 m (0.92), rainfall between 370 and 402 mm/year (0.64), distance to rivers between 700 and 1000 m (0.62), distance to rivers between 100 and 150 m (0.49), slopes between 3° and 7° (0.48), lithological categories represented by Loess-like deposits (0.46), hydrological soil group B (0.42), distance to rivers between 0 and 100 m (0.38), aspect category represented by South-East slopes (0.32), hydrological soil group C (0.23), TWI class between 2.96 and 8.01 (0.22), profile curvature class between 3.56 and 0.07 (0.22) (Table 2).

TABLE 2 Weights of evidence table. Predictor

Class

Class pixels

Wells number

WOE

Slope

25°

4186

1

1.50

322–370

7,336,705

42

0.65

370–402

3,811,061

53

0.64

402–432

3,503,532

34

1.50

432–456

1,909,932

19

1.50

456–496

665,467

6

1.50

Flat areas

511,262

1

1.69

North

1,562,122

14

0.15

North-East

2,105,206

22

0.03

East

2,413,361

29

0.21

South-East

2,669,107

35

0.32

South

1,632,990

11

0.46

South-West

2,250,252

15

0.48

West

2,268,997

12

0.73

North-West

1,788,306

15

0.22

Rainfall (mm/ year)

Aspect

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TABLE 2 Weights of evidence table—cont’d

Predictor

Class

Class pixels

TWI

2.96–8.01

5,308,382

56

0.22

8.02–10.18

6,041,374

57

0.06

10.19–12.79

3,599,589

25

0.34

12.8–16.58

1,726,202

13

0.22

16.59–25.96

544,340

3

0.52

3.560.07

808,267

9

0.22

0.06–0.07

14,725,689

132

0.01

0.08–3.45

1,692,741

13

0.18

(100)–(3)

4,565,545

41

0.09

(2.99)–(2)

952,702

5

1.5

(1.99)–(1)

1,272,483

15

1.5

(0.99)–0

7,003,171

69

1.5

0.1–99

3,432,796

24

0.26

A

3,312,957

16

0.84

B

3,353,337

45

0.42

C

8,840,577

92

0.23

D

1,743,037

1

2.96

Waterbody

534,082

1

0.95

Granites, diorite

872,886

11

2.50

Conglomerates, sandstones

961,134

12

2.50

Gravels, sands, and loesslike deposits

4,854,230

18

0.77

Loess-like deposits

9,429,771

106

0.46

Green and red clays

532,823

5

0.09

Marls, gray clays, gypsum, and salts

36,981

1

0.97

0–100 m

573,164

9

0.38

100–150 m

275,776

5

0.49

150–200 m

204,876

1

0.97

200–400 m

985,231

10

2.5

Profile curvature

Convergence index

HSG

Lithology

Distance to rivers

Wells number

WOE

Continued

390 Applications of artificial intelligence in mining and geotechnical engineering

TABLE 2 Weights of evidence table—cont’d

Predictor

Plan curvature

Class

Class pixels

Wells number

400–700 m

1,430,631

22

0.92

700–1000 m

1,359,758

16

0.62

>1000 m

12,489,489

91

0.27

3.070.06

799,752

4

0.62

0.05–0.02

8,574,645

75

0.06

0.03–3.58

7,852,300

75

0.11

WOE

5.3 Groundwater potential The groundwater potential was first computed using the weights of evidence method by summing up the WOE coefficients of all classes/categories associated with the groundwater potential predictors. The groundwater potential index-weights of evidence (GPI-WOE) values, indicated in Fig. 7A, were divided into five classes according to the Natural Breaks method. The very low values cover around 24.48% of the Dobrogea region from Romania. The low values are spread over around 20.14% of the study zone, while the medium values span 25.13% of the Dobrogea region. The high GPI-WOE is present at approximately 20%, while the very high groundwater potential has a percentage of 10.1% of the Dobrogea region (Fig. 8A). To compute the GPI SVM-WOE the weight for each predictor was determined. Thus, the most important weight was assigned to distance to rivers (0.31), followed by lithology (0.25), TWI (0.25), rainfall (0.23), slope (0.23), convergence index (0.19), plan curvature (0.17), hydrological soil group (0.15), aspect (0.12), and profile curvature (0.09) (Fig. 6).

FIG. 6 Predictors’ importance in terms of the SVM-WOE model.

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These weights were multiplied with the WOE coefficients and the multiplication results associated with each predictor were summed up to derive the GPI SVM-WOE. The results of GPI SVM-WOE were classified into five classes according to the Natural Breaks method (Fig. 7B). The first class of very low GPI SVM-WOE values covers approximately 27.56% of the study area, meanwhile to the second class is associated with a percentage of 37.11%. The medium value of groundwater potential index are spread over 20.03% of the study zone, while high and very high values have together a total percentage that exceeds 15% (Fig. 8B).

5.4 Results validation The results validation procedure was carried out with the help of the ROC curve method. Thus, in terms of success rate both the applied models achieved very high performances: GPI-WOE (AUC ¼ 0.956) and GPI SVM-WOE (AUC ¼ 0.967) (Fig. 9A). The difference between the models was a little bit higher in the case of prediction rate. Thus, the highest value was achieved by GPI SVM-WOE (AUC ¼ 0.97), while GPI-WOE has an AUC of 0.959 (Fig. 9B).

6

Conclusions

The importance of groundwater as a natural resource is unquestionable. In the past few decades, governments and research institutions around the world have tried to estimate the potential and distribution of groundwater in different parts of the world. As part of this study, there was presented an artificial intelligence approach that used also the geographic information system to estimate the groundwater resources that might exist in an area. It was crucial to select 10 of the most important variables to be taken into account when mapping groundwater potential as the first step. To map the groundwater potential, a weights of evidence model was used, which represents the correlation between the location of wells and the variables associated with groundwater availability. The weights of evidence method highlighted that 30% of the Dobrogea region from Romania is covered by a high and very high groundwater potential, while the combination of evidence weights and support vector machine (SVM-WOE) indicated that around 15% of the same region is characterized by a high and very high groundwater potential. In terms of results validation, both of the models achieved very accurate outcomes.

392 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 7 Groundwater potential indices: (A) weights of evidence and (B) support vector machineweights of evidence.

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FIG. 8 Percentages of ground water potential indices classes: (A) weights of evidence and (B) support vector machine-weights of evidence.

394 Applications of artificial intelligence in mining and geotechnical engineering 1.0

(a)

Sensitivity

0.8

0.6

0.4

GPI WOE (AUC = 0.965) GPI SVM-WOE (AUC = 0.967) Reference Line (AUC = 0.5)

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

1.0

(b)

Sensitivity

0.8

0.6

0.4 GPI WOE (AUC = 0.959) GPI SVM-WOE (AUC = 0.97) Reference Line (AUC = 0.5)

0.2

0.0 0.0

0.2

0.4

0.6

1 - Specificity FIG. 9 ROC curves: (A) Success rate and (B) prediction rate.

0.8

1.0

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References [1] N.W. Arnell, S.N. Gosling, The impacts of climate change on river flood risk at the global scale, Clim. Change 134 (2016) 387–401. [2] I. Didovets, V. Krysanova, G. B€urger, S. Snizhko, V. Balabukh, A. Bronstert, Climate change impact on regional floods in the Carpathian region, J. Hydrol. Reg. Stud. 22 (2019) 100590. [3] A. Othman, A.Z. Abotalib, Land subsidence triggered by groundwater withdrawal under hyperarid conditions: case study from Central Saudi Arabia, Environ. Earth Sci. 78 (2019) 1–8. [4] D. Machiwal, M.K. Jha, B.C. Mal, Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques, Water Resour. Manag. 25 (2011) 1359–1386. [5] O. Rahmati, A. Nazari Samani, M. Mahdavi, H.R. Pourghasemi, H. Zeinivand, Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS, Arab. J. Geosci. 8 (2015) 7059–7071. [6] B.T. Pham, A. Jaafari, I. Prakash, S.K. Singh, N.K. Quoc, D.T. Bui, Hybrid computational intelligence models for groundwater potential mapping, Catena 182 (2019) 104101. [7] Y. Srinivasa Rao, D. Jugran, Delineation of groundwater potential zones and zones of groundwater quality suitable for domestic purposes using remote sensing and GIS, Hydrol. Sci. J. 48 (2003) 821–833. [8] B. Guru, K. Seshan, S. Bera, Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India, J. King Saud Univ. Sci. 29 (2017) 333–347. [9] A. Mosavi, F. Sajedi Hosseini, B. Choubin, M. Goodarzi, A.A. Dineva, E. Rafiei Sardooi, Ensemble boosting and bagging based machine learning models for groundwater potential prediction, Water Resour. Manag. 35 (2021) 23–37. [10] P.T. Nguyen, D.H. Ha, M. Avand, A. Jaafari, H.D. Nguyen, N. Al-Ansari, T. Van Phong, R. Sharma, R. Kumar, H.V. Le, L.S. Ho, I. Prakash, B.T. Pham, Soft computing ensemble models based on logistic regression for groundwater potential mapping, Appl. Sci. 10 (2020) 2469, https://doi.org/10.3390/app10072469. [11] K.G. Berhanu, S.D. Hatiye, Identification of groundwater potential zones using proxy data: case study of Megech Watershed, Ethiopia, J. Hydrol. Reg. Stud. 28 (2020) 100676, https:// doi.org/10.1016/j.ejrh.2020.100676. [12] M. Panahi, N. Sadhasivam, H.R. Pourghasemi, F. Rezaie, S. Lee, Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR), J. Hydrol. 588 (2020) 125033. [13] I. Minea, D. Boicu, O.-E. Chelariu, M. Iosub, A. Enea, Assessment of recharge capacity potential of groundwater using comparative multi-criteria decision analysis approaches, J. Geogr. Sci. 32 (2022) 735–756. [14] G. Bandoc, R. Pra˘va˘lie, Climatic water balance dynamics over the last five decades in Romania’s most arid region, Dobrogea, J. Geogr. Sci. 25 (2015) 1307–1327. [15] P. Arulbalaji, D. Padmalal, K. Sreelash, GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern Western Ghats, India, Sci. Rep. 9 (2019) 2082, https://doi.org/10.1038/s41598-019-38567-x. [16] R. Costache, H. Hong, Y. Wang, Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics, Catena 183 (2019) 104179. [17] G.F. Bonham-Carter, Weights of evidence modelling: a new approach to mapping mineral potential, Stat. Appl. Earth Sci. (1989) 171–183.

396 Applications of artificial intelligence in mining and geotechnical engineering [18] J.-H. Lee, M.I. Sameen, B. Pradhan, H.-J. Park, Modeling landslide susceptibility in datascarce environments using optimized data mining and statistical methods, Geomorphology 303 (2018) 284–298. [19] R. Costache, L. Zaharia, Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment–case study: B^asca Chiojdului River catchment (Romania), J. Earth Syst. Sci. 126 (2017) 59. [20] R. Costache, D.T. Bui, Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: a case study at the Putna river catchment of Romania, Sci. Total Environ. 691 (2019) 1098–1118. [21] U. Barman, R.D. Choudhury, Soil texture classification using multi class support vector machine, Inf. Process. Agric. 7 (2020) 318–332. [22] L. Gao, M. Ye, X. Lu, D. Huang, Hybrid method based on information gain and support vector machine for gene selection in cancer classification, Genomics Proteomics Bioinformatics 15 (2017) 389–395. [23] H. Hong, B. Pradhan, C. Xu, D.T. Bui, Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines, Catena 133 (2015) 266–281. [24] B.T. Pham, A. Jaafari, I. Prakash, D.T. Bui, A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling, Bull. Eng. Geol. Environ. 78 (2019) 2865–2886. [25] M. Sahana, S. Rehman, H. Sajjad, H. Hong, Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: a study of Sundarban Biosphere Reserve, India, Catena 189 (2020) 104450. [26] D. Tien Bui, H. Shahabi, A. Shirzadi, K. Chapi, M. Alizadeh, W. Chen, A. Mohammadi, B. Ahmad, M. Panahi, H. Hong, Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in Cameron highlands, Malaysia, Remote Sens. (Basel) 10 (2018) 1527. [27] B. Choubin, E. Moradi, M. Golshan, J. Adamowski, F. Sajedi-Hosseini, A. Mosavi, An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines, Sci. Total Environ. 651 (2019) 2087–2096. [28] S.-W. Lin, Z.-J. Lee, S.-C. Chen, T.-Y. Tseng, Parameter determination of support vector machine and feature selection using simulated annealing approach, Appl. Soft Comput. 8 (2008) 1505–1512. [29] R. Costache, Flash-flood potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models, Sci. Total Environ. 659 (2019) 1115–1134. [30] R. Costache, Flood susceptibility assessment by using bivariate statistics and machine learning models—a useful tool for flood risk management, Water Resour. Manag. 33 (2019) 3239– 3256. [31] R. Costache, Flash-flood potential index mapping using weights of evidence, decision trees models and their novel hybrid integration, Stoch. Environ. Res. Risk Assess. 33 (2019) 1375–1402. [32] D.T. Bui, B. Pradhan, H. Nampak, Q.-T. Bui, Q.-A. Tran, Q.-P. Nguyen, Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS, J. Hydrol. 540 (2016) 317–330.

Chapter 22

Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review Chia Yu Huata, Danial Jahed Armaghanib, Ehsan Momenic, and Sai Hin Laia,d a

Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia, bSchool of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, Sydney, NSW, Australia, cFaculty of Engineering, Lorestan University, Khorramabad, Iran, d Department of Civil Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia

1

Introduction

Geotechnical engineering field is related to soil and rock that comprise various and unknown characteristics because of the inexplicit physical during the process of formation [1]. Due to the complexity of geotechnical engineering, many traditional engineering design models are simplified to solve the problems [2]. Foundation is part of an engineering system that able to transmit load to the ground (soil or rock). It is important to ensure the foundation design meets the expectations at the construction site; however, there is always a potential that the design goal of ensuring the system operates satisfactorily within a specific time frame cannot be met [3]. A pile foundation is a form of foundation that is described as a series of columns that are erected or inserted into the ground in order to transfer loads to a lower level of subsoil below the surface. The pile bearing capacity can be affected by two (2) main parameters which are soil or rock properties and pile geometries [4,5]. In general, there are two types of pile capacity which are geotechnical and structural capacity. The geotechnical capacity of piles is from that soil or rock provides friction along the pile shaft and base support for the piles. The pile friction and base capacity can be computed with the formula as shown below: Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00011-4 Copyright © 2024 Elsevier Inc. All rights reserved.

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Applications of artificial intelligence in mining and geotechnical engineering

Qsu ¼ τm x AS

(1)

where Qsu is the ultimate pile shaft friction capacity τm is the interface shear strength along the pile length AS is the surface area of the pile Qbu ¼ τb x Ab

(2)

where Qbu is the ultimate pile base capacity τb is the interface shear strength between pile base and soil/rock Ab is the cross-sectional area of the piles In general, the pile geotechnical capacity can be calculated as follows: Q ¼ Qsu + Qbu

(3)

where Q is the pile geotechnical capacity. Besides, pile installation can be one of the factors that affects the pile capacity. For example, driven piles are those where this method induces soil displacement, which indirectly densifies the loose deposits around the pile. Therefore, this can increase the geotechnical capacity of the pile. In the actual pile construction at the site, many piles are constructed, and it is crucial to ensure the pile capacity meets the minimum criteria. Mostly, during the design stage, many parameters from laboratory and fieldwork are very limited for pile design due to cost and uneconomical issues. As such, designers tend to generalize the input parameters and implement relevant correlations for design. In order to confirm the capacity of the constructed piles, several piles will be chosen for pile tests such as maintained load test (MLT) and high strain dynamic pile testing (HSDP). MLT can be carried out using several methods, such as the kentledge system, reaction anchor system, and bi-directional system. However, all these methods are time consuming to set up [6]. Although HSDP is convenient to carry out, these tests are not practical to carry out for all the constructed piles. In recent years, artificial intelligence (AI) has been widely used by many researchers for predictions in the engineering industry [7–17]. However, some researchers have realized that AI models do not always provide accurate predictions. Hence, this led many researchers to further explore hybrid models of AI in order to improve the prediction of the pile capacity. In this review paper, the method of using base AI and hybrid AI models for the prediction of pile capacity will be discussed. Furthermore, various AI models with different methods of analysis will be discussed in this paper. As such, the core objective of this review is to show the capability of these new methods in the construction industry for the prediction of pile capacity. In addition, the advantages and disadvantages of the available approaches will be explained.

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Background of soft computing

There are two types of computing, which are conventional (hard) computing and soft computing. Hard computing is a method that utilizes an analytical model or a mathematical approach to solve a problem. Soft computing (SC) is the computational method that implements AI and natural approaches to provide fast and cost-effective solutions to complex problems. The difference between these two models can be seen in Fig. 1.

FIG. 1 Problem-solving approach of hard and soft computing.

In contrast with the normal analytical approach, SC uses consciousness and cognition such as experience learning, simulation of the biological process, mapping of the inputs with outputs, and analytical representation. SC can be classified into several components, which are probabilistic reasoning (PR), fuzzy systems (FS), evolutionary computing (EC), neural computing (NC), and wisdom-based expert systems (WES). Several applications can be used in SC, which are communication systems, robotics, transportation, data mining, health care, clustering, and association rules. Data mining is a process that involves using algorithms to extract the patterns of the model based on the data inputs. The process of data analysis includes the preparation of data, filtering and cleaning of data, knowledge incorporation in the data, and data interpretation to ensure useful output is extracted from the data. Recently, various SC methods have been used to solve the challenges of data mining. In geotechnical engineering, most researchers have applied artificial neural network (ANN) methods to solve problems in the industry due to the non-linear behavior of the material. However, ANNs which are part of NC are unsuitable for data mining due to their black box nature [18–20]. No information was available for verification or interpretation by humans [21,22]. Machine learning (ML) is a group of techniques that have been developed to reproduce human intelligence by learning from previous historical information [23]. The concept of ML is based on various fields such as AI, computer science, and mathematics [24]. There are many types of ML algorithms, such as clustering algorithms, decision trees, ensembles, and regression. ML techniques can be classified into three (3) categories (supervised, unsupervised, and semi-supervised). Classification and regression analyses are important methods in supervised learning [25]. As for unsupervised learning, inputs are

400

Applications of artificial intelligence in mining and geotechnical engineering

provided to the learning system. Unsupervised learning has few output variables in the analysis that are able to be used for supervision [26], whereas for semisupervised learning, it is a combination of both supervised and unsupervised learning [27]. Fig. 2 illustrates different types of ML algorithms.

FIG. 2 Illustration of the type of machine learning.

In the engineering industry, supervised learning algorithms have been widely used. Supervised learning is the method of identification of unknown input and output data based on known input and output data, with the output being identified. A simple illustration of supervised learning can be referred to in Fig. 3. Traditional statistical regression analysis for non-linear analysis requires prior knowledge of the nature of non-linearity between input and Answer (Actual Output) = Pineapple

SUPERVISED LEARNING

= Lemon = Banana

Machine Learning Algorithm

Output = Pineapple = Lemon

Supervisor (Teaching)

= Banana

Training Data (Input & Output

Lemon, Pineapple, banana, orange, banana, apple

FIG. 3 Illustration of supervised ML process.

NEW INPUT DATA

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output prior to the analysis. A highly non-linear relationship between parameters is always encountered in practical foundation works. In this section, a brief overview of several selected AI methods, which are ANN, support vector machine (SVM), genetic programming (GP), and gene expression programming (GEP), is presented.

2.1 Artificial neural network (ANN) ANN is one of the SCs that uses techniques that mimic the information transfer process to the human brain that is similar to the biological nervous system. The first concept of ANN (perceptron) was introduced by Rosenblatt [28]. The biological neural network is comprised of a large number of interconnected neurons. In general, biological undefined comprises three important components, namely cell body, axon, and dendrites, to understand an ANN model. The cell body acts as a center to sum up the incoming signals and discharges when the inputs received by the cell are sufficient. The sign is transferred to other cells via axons. The dendrites form a fine filament brush surrounding the neuron’s body. As for the ANN model, artificial neurons play an undefined role as the constitutive units. Each of the artificial neurons accepts one or more inputs that are represented by dendrites, and the summation of these inputs forms an output that represents an axon. The output is weighted and goes through an activation function (transfer function) that is known as a nonlinear function. Fig. 4 shows an illustration of an ANN model where x is input, w represents weight, Ʃ stands for summation, and f represents the function of the model. The neuron is a processing element that retrieves several inputs, followed by weighing and summarizing. Lastly, the result is used as an argument for the activation function.

FIG. 4 An illustration of the ANN model with the parameters.

2.2 Support vector machine (SVM) SVM is created based on the statistical theory by Boser et al. [29]. The purpose of SVM is to determine the optimal hyperplane in N-dimensional space, where N is the number of features that differentiate the data points, as illustrated

402

Applications of artificial intelligence in mining and geotechnical engineering

y

y

Optimal Hyperplane

Support Vector

Class 2

Class 2

⑀ Class 1

M a M xim ar is gi ed n

Class 1

x Trial and Error of Hyperplane

x

Final Selected Optimal Hyperplane

FIG. 5 Illustration of the possibility’s hyperplane.

in Fig. 5. SVM regression can be categorized into two categories: linear and non-linear. Originally, SVM was proposed to deal with the classification issues by Cortes and Vapnik [30]. Subsequently, Smola [31] introduced the loss function, known as E-insensitive loss function, where this allowed SVM to apply to the regression problems. In general, the definition of this loss function is the total distance from the hyperplane to the nearest point of two different classes (Fig. 5). The E-insensitive loss function can be defined as follows: FE ðyÞ ¼ 0 for jf ðxÞ  yj < E, otherwise

(4)

FE ðyÞ ¼ jf ðxÞ  yj  E

(5)

where f(x) is the function of the model y is the output of the model FE(y) is the loss function In general, the procedure of SVM for estimation of the linear regression functions can be explained in three (3) procedures. Firstly, a linear set of function is used by SVM to estimate the regression. Next, the regression estimation is done by using risk minimization. This risk can be measured using E-insensitive loss function. For the nonlinear approach, Vapnik [32] initiates the concept of kernel function. There are several types of kernel functions, such as polynomial kernel functions, sigmoid kernel functions, RBF kernel functions, and several more. In this paper, the polynomial kernel function will be briefly explained. The kernel function with degree d, polynomials can be expressed as follows: d ¼ Fðx, yÞ  ðxy + CÞ

(6)

Application of artificial intelligence techniques Chapter y

y Degree of Polynomials = 1

22

403

y Degree of Polynomials = 2

x

Degree of Polynomials = 5

x

x

FIG. 6 Illustration of the kernel functions on different degrees.

where x and y are the space input vectors (or variable parameters), C is a parameter to lower the gap between the higher and lower orders of the polynomial. Fig. 6 shows the illustration of how the kernel reacts to the different degrees of polynomial.

2.3 Decision tree (DT) DT is a method using tree branch patterns for decision making. DT has several elements which are nodes, decision nodes, and root nodes in one tree structure. The brief description of these elements can be explained as follows: (1) Leaf Node: It is also called a “terminal node” because this node provides the output. (2) Decision Node: A node that produces child nodes based on the several variables where the analysis is carried out. (3) Root Node: The node that is located at the top of the tree structure that affects the growth of the tree. A diagram of the decision tree can be seen in Fig. 7. X1 d t1

X2 d t2

R1

Where Xj denote predictor variables, Xj d tk and Xj ! tk correspond to the left and right branches of each internal split respectively whereas Ri denotes the mean of the observations at leaf i.

X1 d t3

R2

Root Node Decision Node Leaf Node

R3

R4

R5

FIG. 7 Example for DT with one root node, two decision nodes, and five leaves node (terminal nodes).

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Applications of artificial intelligence in mining and geotechnical engineering

To predict the pile capacity, a regression tree can be used because the input and output variables are continuous or discrete values. James et al. [26] introduce binary recursive partitioning as part of the process of building a regression tree. This method of binary recursive partitioning is a method that uses an iterative process to split all the data into several partitions, and this process of separation continues to split into smaller groups for every process of the stage [33]. The process of this separation is described as follows: For instance, let y1, y2, …,yN to be a compilation of the observation from the output, yi. Each output value of yi, i ¼ 1, 2, …N relies on affecting variable X1, X2,.., Xp. The predictor space which is the group of values for X1, X2,.., Xp is classified into several non-overlapping regions, R1, R2,..RJ and Jth-dimensional. For every observation entered into the region Rj, similar prediction can be made. The shape of regions is varying. Nevertheless, it can be adjusted to separate the predictor space into j-dimensional for the ease of interpretation as predictive model. Subsequently, all the predictors X1, X2,..Xp and the possible values of the separation for every predictor are considered. A tree result of the predictor and split point with lowest residual sum square (RSS) shall be selected. In short, the main purpose is to determine regions R1, R2,..RJ that give the lowest of the RSS where the RSS is defined as follows: RSS ¼

J X X i

i ERj

yi  ybRj

2 (7)

where ybRj is the mean response for the training dataset within the jth. By taking into consideration every possible partition of the feature space into Jth boxes, it requires very long computational time. Recursive binary splitting is used to reduce the computational time by beginning from the top of the tree structure and having all the observations in one region separated into the predictor space. Each separation is classified into two new branches. The illustration of the partition is shown in Fig. 8.

FIG. 8 Process of recursive binary splitting with two-dimensional feature space.

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2.4 Genetic programming and gene expression programming (GP & GEP) GP was founded by Koza [34] and is based on the principle of genetic algorithms (GAs) by following the biological evolution of living organisms to optimize a population which is comprised of functions and terminals. The core objective of GP is to identify a model that best fits the problem. There are three (3) types of nodes that represent the GP, which are functional nodes and terminal nodes. In general, GP can be briefly described in five (5) stages, which are as follows: Initial Population: GP trees are created randomly based on the user-defined functions and terminals. Selection: The higher fitness from the randomly generated programs will be forged into the next generation. The selection is carried out using a roulettewheel selection, tournament, and ranking. Crossover: In this stage, two (2) trees are chosen randomly from the population, and each node from each tree is selected randomly. Subsequently, the subtrees exchange for the generation of two offspring. Mutation/Replacement: One of the GP trees is taken out of the population and the tree node is exchanged for another node based on the user-defined function or terminal set. Ferreira [35] has initiated GEP, which is a natural extension of GP. GP analysis shows that GEP has five (5) similar procedures. The core difference between GEP and GP is that the solution produced by GEP is represented by fixed-length string characters. There are two (2) major parameters, which are chromosomes and expression trees (ETs). Two (2) languages are used in GEP, which are the languages of genes and ETs.

3

Application of AI for pile capacity prediction

To utilize the SC for pile capacity verification, a large amount of data are required. SC works based on the data, which acts as fuel to ensure the model works. Thus, sufficient and good-quality data are required. However, in order to have a large amount of data, it is time-consuming and unpractical for researchers and engineers to carry out many laboratory and in-situ tests. The information can be derived from geotechnical construction projects. Most construction projects carry out subsurface investigation (SI) works prior to the commencement of any work. This SI work data can be collected from one project to another. Other than SI work, MLT and HSPDT are typically carried out for the verification of the pile capacity, with a certain percentage of the total constructed number of piles being used as the output of the analysis. Thus, this data should be collected and stored as a database reference. With this, more data can be used in the SC model.

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Applications of artificial intelligence in mining and geotechnical engineering

In construction practice of pile design, the Standard Penetration Test (SPT)N is typically used to calculate the pile capacity [36,37]. Many researchers have proposed empirical formulas for the calculation of pile friction bearing that based on the correlation of SPT-N with the formula as follows, qs ¼ns N [38–40]. Based on their findings, qs and ns are limit skin friction stress that varies according the depth, and these values dependent with SPT-N value which are directly proportional with depth. Nevertheless, the calculated capacity from these proposed equation did not give a good accuracy [41]. This inaccuracy attributes to the fact that empirical formula was derived with the assumption and simplification [2]. Beside using SPT-N for the computation of pile capacity, pile capacity can be computed using cone penetration test (CPT); however, this approach overpredicts the pile bearing capacity [32].

3.1 Base artificial intelligence (AI) models Many researchers have begun to explore AI for the prediction of pile capacity. Table 1 summarizes the input parameters, the type of AI model used, and the result of the prediction for pile capacity. From the table, it can be seen that the variables affecting the prediction of the pile capacity can be classified into

TABLE 1 Summary of the SC method for the prediction of pile axial capacity.

No.

Reference

Input

Model

Result of the analysis for testing set

1

Lee and Lee [42]

Model pile load test:

ANN

Pile load test: Maximum error of prediction 25°

1943

5

4.27

Flat areas

2325

0

0.00

North

60,594

21

0.57

Aspect

Landslide susceptibility in a hilly region of Romania Chapter

23

427

TABLE 1 Frequency ratio values—cont’d

Factor

Elevation

TWI

Profile curvature

Land use

Class

Class pixels

Landslide locations

FR

North-East

57,457

28

0.81

East

69,884

58

1.38

South-East

77,485

66

1.41

South

70,278

42

0.99

South-West

68,262

44

1.07

West

67,185

36

0.89

North-West

58,667

26

0.73

128.47–241.55 m

114,867

2

0.03

241.56–326.98 m

159,030

90

0.94

326.99–417.44 m

134,036

143

1.77

417.45–530.51 m

90,815

63

1.15

530.52–769.22 m

33,389

23

1.14

3.47–6.52

223,864

177

1.31

6.53–8.25

181,839

80

0.73

8.26–10.88

87,654

48

0.91

10.89–15.17

28,622

12

0.69

15.18–24.48

9927

4

0.67

(1.91) to (0.07)

115,887

93

1.33

0.06 to 0.07

227,219

108

0.79

0.08–1.54

189,031

120

1.05

Built-up areas

15,290

15

1.46

Agriculture areas

61,092

3

0.07

Fruit trees

126,530

108

1.27

Vineyards

20,003

13

0.97

Pastures

71,372

59

1.23

Forests

44,121

73

2.74

Shrubs

193,788

50

0.43 Continued

428 Applications of artificial intelligence in mining and geotechnical engineering

TABLE 1 Frequency ratio values—cont’d

Factor

Class

Class pixels

Landslide locations

FR

Hydrological soil group

B

169,898

60

0.59

C

320,677

253

1.31

D

41,621

8

0.32

1

23,169

12

0.64

2

12,681

0

0.00

3

118,976

109

1.13

4

58,736

52

1.09

5

17,642

8

0.75

6

96,058

72

1.24

7

113,761

2

0.03

8

91,101

66

1.20

9

72

0

0.00

0–100 m

156,008

21

0.22

100–150 m

54,950

14

0.42

150–200 m

36,405

19

0.87

200–400 m

125,082

63

0.84

400–700 m

91,150

101

1.84

700–1000 m

41,073

53

2.14

>1000 m

27,531

50

3.01

0–100 m

41,061

17

1.11

100–150 m

19,296

2

0.28

150–200 m

14,556

4

0.74

200–400 m

67,455

30

1.19

400–700 m

95,269

51

0.89

700–1000 m

83,687

46

0.91

>1000 m

210,820

171

1.34

Lithology

Distance to road

Distance to river

Landslide susceptibility in a hilly region of Romania Chapter

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5.2 Landslide susceptibility mapping In order to derive the first LSI FR was achieved by summing up the landslide predictors having assigned the FR values. This operation was performed in Map Algebra from ArcGIS 10.8 software. Following this procedure, the LSI FR index resulted, which was grouped into five classes of values by using the Natural Breaks method. Thus, the first class, with very low values, occupies an area of 13.62% of the total study area and occupies mainly the areas in the south and south-west where the slopes are reduced (Fig. 6A). Furthermore, the second class, with low values, occupies a percentage of 22.51% of the total area under study. These values are observed in the extreme south-west and north-west of the research area as well as along the main river valleys. The average class of LSI FR values has a weight of 26.79% of the total upper and middle basin of Cricovul Sa˘rat, while the other 37.11% of the percentages are occupied by areas high and very high susceptible to landslide triggering (Fig. 7). These zones appear on extensive areas in the central-northern part of the study area and characterize slopes with slopes of more than 7°. The optimal architecture for the MLP-FR structure is characterized by 10 input neurons, 21 hidden neurons, and 2 output neurons (Fig. 4).

FIG. 4 MLP-FR architecture.

430 Applications of artificial intelligence in mining and geotechnical engineering

Following the application of the multilayer perceptron-frequency ratio (MLP-FR) model, the values associated with the importance of each landslide predictor were derived. The highest importance was received by slop angle (0.217% or 100%), this landslide predictor being followed by: lithology (0.177% or 81.4%), distance from river (0.148% or 68.1%), land use (0.117% or 54%), distance from roads (0.095% or 43.9%), profile curvature (0.069% or 31.9%), topographic wetness index (0.067% or 31%), aspect (0.039% or 18%), hydrological soil group (0.038% or 17.8%), and elevation (0.032% or 14.6%) (Fig. 5). In the next step, the relative importance of landslide predictors was multiplied with the FR coefficients, and these products were summed up in order to derive the LSI MLP-FR values. The results were classified into five categories according to the Natural Breaks classification method. Thus, the very low values cover a percentage of 17.02% of the study area and are present in the same areas like those associated for FR LSI. The low LSI MLP-FR class is spread on 21.05% of the upper and middle catchment of Cricovul Sa˘rat river, while the medium values span on 27.61% of the study zone. The high and very high classes account for a total percentage of 34.32% of the research zone (Fig. 6B and 7).

Normalized Importance 0

20

40

60

80

100

Slope Lithology Distance from river Land use Distance from roads Profile curvature TWI Aspect HSG Elevation

0.00

0.05

0.10

0.15

0.20

Importance FIG. 5 Normalized and absolute importance of landslide predictors in terms of MLP-FR hybrid model.

Landslide susceptibility in a hilly region of Romania Chapter

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431

FIG. 6 Landslide susceptibility mapping across the upper and middle basin of Cricovul Sa˘rat river. (A) LSI FR; (B) LSI MLP-FR.

432 Applications of artificial intelligence in mining and geotechnical engineering

FR

13.62

MLP-FR

22.51

21.05

17.02

0

26.79

20 Very low

27.61

40 Low

11.35

25.74

23.73

60

Medium

80 High

10.59

100

Very high

FIG. 7 Weights of landslide susceptibility classes.

5.3 Results validation The results validation was done by using the ROC curve procedure. Thus, it can be observed that in terms of success rate, the LSI MLP-FR achieved a higher accuracy which is indicated by the AUC equal to 0.874, comparing to LSI FR which has associated an AUC value of 0.846 (Fig. 8A). Further, if we analyze the prediction rate, it should be noted that again the LSI MLP-FR obtained a higher AUC value (0.861) comparing to LSI FR which has associated an AUC of 0.85 (Fig. 8B).

6 Conclusions A complex procedure has been presented in the present paper in order to determine the landslide susceptibility across the Cricovul Sarat river catchment, Romania, based on the results of a spatial analysis. Originally, this workflow was developed in response to the fact that landslides have been causing an increasing amount of material damage and loss of human lives in the last few years, particularly in the study area recently studied, which is one of those most commonly affected areas in Romania. In this study, the methodology used was based on the computation of landslide susceptibility index on the basis of two different models in order to determine the levels of risk. The first result obtained from the hybrid integration of the bivariate statistical model, frequency ratio, with the multilayer perceptron was one of them. A note should be made regarding the fact that both models were trained with the help of 10 flood conditioning factors, which were used to predict flooding, as well as 321 landslide pixels, which represented the dependent variable. In terms of the results of this study, the mountainous area is the most vulnerable to landslides, as its slope angle value is very high, which indicates the likelihood of

Landslide susceptibility in a hilly region of Romania Chapter

23

1.0

(a)

Sensitivity

0.8

0.6

0.4

LSI MLP-FR (AUC = 0.874) LSI FR (AUC = 0.846) Reference Line (AUC = 0.5)

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

1 - Specificity 1.0

(b)

Sensitivity

0.8

0.6

0.4

LSI MLP-FR (AUC = 0.861) LSI FR (AUC = 0.85) Reference Line (AUC = 0.5)

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1 - Specificity FIG. 8 AUC-ROC curve values: (A) success rate and (B) prediction rate.

1.0

433

434 Applications of artificial intelligence in mining and geotechnical engineering

landslides. Thus, based on the results of the study area, it seems that around 35% of the areas are likely to have surfaces with a very high and high flood susceptibility. ROC curves are used to validate the results, and they tell us that MLP-FR is the best-performing model based on its AUC of 0.874, which is the maximum value that can be achieved. As a result of the present investigation, the results may be used as reference for future scientific works concerning the assessment of landslide susceptibility in the future.

References [1] O.F. Althuwaynee, B. Pradhan, H.-J. Park, J.H. Lee, A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping, Catena 114 (2014) 21–36. [2] J.M. Habumugisha, N. Chen, M. Rahman, M.M. Islam, H. Ahmad, A. Elbeltagi, G. Sharma, S.N. Liza, A. Dewan, Landslide susceptibility mapping with deep learning algorithms, Sustainability 14 (2022) 1734, https://doi.org/10.3390/su14031734. [3] D.T. Bui, B. Pradhan, O. Lofman, I. Revhaug, O.B. Dick, Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS, Comput. Geosci. 45 (2012) 199–211. [4] L. Lv, T. Chen, J. Dou, A. Plaza, A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping, Int. J. Appl. Earth Obs. Geoinf. 108 (2022) 102713, https://doi. org/10.1016/j.jag.2022.102713. ´ .M. Felicı´simo, A. Cuartero, J. Remondo, E. Quiro´s, Mapping landslide susceptibility with [5] A logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study, Landslides 10 (2013) 175–189. [6] W. Chen, W. Li, H. Chai, E. Hou, X. Li, X. Ding, GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China, Environ. Earth Sci. 75 (2016) 63. [7] R. Liu, X. Yang, C. Xu, L. Wei, X. Zeng, Comparative study of convolutional neural network and conventional machine learning methods for landslide susceptibility mapping, Remote Sens. (Basel) 14 (2022) 321, https://doi.org/10.3390/rs14020321. [8] D. Arca, H.Ş. Kutoglu, K. Becek, Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method, Environ. Monit. Assess. 190 (2018) 1–14. [9] K.C. Devkota, A.D. Regmi, H.R. Pourghasemi, K. Yoshida, B. Pradhan, I.C. Ryu, M.R. Dhital, O.F. Althuwaynee, Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya, Nat. Hazards 65 (2013) 135–165. [10] J. Dou, D.T. Bui, A.P. Yunus, K. Jia, X. Song, I. Revhaug, H. Xia, Z. Zhu, Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan, PloS One 10 (2015) e0133262. [11] D. Ba˘lteanu, V. Chendes¸ , M. Sima, P. Enciu, A country-wide spatial assessment of landslide susceptibility in Romania, Geomorphology 124 (2010) 102–112, https://doi.org/10.1016/ j.geomorph.2010.03.005. [12] D. Ba˘lteanu, M. Micu, M. Jurchescu, J.-P. Malet, M. Sima, G. Kucsicsa, C. Dumitrica˘, D. Petrea, M.C. Ma˘rga˘rint, Ş. Bilas¸ co, C.-F. Dobrescu, E.-A. Ca˘la˘ras¸ u, E. Olinic, I. Boți,

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F. Senzaconi, National-scale landslide susceptibility map of Romania in a European methodological framework, Geomorphology 371 (2020) 107432, https://doi.org/10.1016/j.geomorph. 2020.107432. [13] I. Yilmaz, Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey), Comput. Geosci. 35 (2009) 1125–1138, https://doi.org/10.1016/j.cageo.2008.08.007. [14] G. Bonham-Carter, Integration of geoscientific data using GIS, in: Geographic Information Systems: Principle and Applications, Longdom, London, 1991, pp. 171–184. [15] P. Kim, Matlab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence, Apress, 2017, p. 130.

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Chapter 24

Spatial prediction of bridge displacement using deep learning models: A case study at Co Luy bridge Thai Ha Vua, Ngoc Quang Vub, and Nguyen Van Thieuc a

Department of Geodesy, Hanoi University of Civil Engineering, Hanoi, Vietnam, bDepartment of Planning and Urban Transport, University of Transport Technology, Hanoi, Vietnam, cFaculty of Computer Science, Phenikaa University, Hanoi, Vietnam

1

Introduction

The development and advancements of the global navigation satellite system (GNSS) lead to an increase in the number of constellations in the sky. Therefore, the number of satellites in each epoch can be up to tens of satellites [1,2]. Along with that, the new positioning techniques [3,4] and new processing techniques [5–9] significantly improve the accuracy of GNSS positioning methods. As a result, GNSS applications have been widely applied in various fields. Regarding monitoring fields [2,10] listed a range of works with complex, large sizes, and scales that used GNSS for monitoring including high buildings [11,12], chimneys [13,14], dams [15,16], towers [17–19] with GNSS-RTK single base, postprocessing kinematic (PPK), Net-RTK, and PPP [2]. GNSS plays an important role in structural health monitoring systems to assess the safety status of the structure [20–22] during the operation and maintenance process. This technique had been widely deployed in many types of bridges including suspension bridges and stayed-cable bridges. Regarding materials, they are wooden bridges, steel bridges, and concrete bridges. In terms of the position of monitoring points, they are on the bridge girder, bridge surface, and bridge towers. The main purpose of GNSS in these systems is to determine absolute displacement, amplitude in different directions, frequencies, and the defection of the bridge [23]. GNSS had been used for determining dynamic displacement for many types of bridges, and the first study was carried out on the Humber suspension bridge,

Applications of Artificial Intelligence in Mining and Geotechnical Engineering https://doi.org/10.1016/B978-0-443-18764-3.00007-2 Copyright © 2024 Elsevier Inc. All rights reserved.

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438 Applications of artificial intelligence in mining and geotechnical engineering

in the United Kingdom [24,25], and displacement is 3 mm, the oscillation of longitudinal and cross direction is 14 and 40 cm respectively, and frequencies are 0.116 and 0.052 Hz. Following this study, a series of bridges had been applied the GNSS technique for dynamic monitoring including Nottingham Wilford suspension [26], Pierre Laporte suspension in Canada [27], Kaiyo suspension in Japan [28], Aizhai, Humen suspension in China [29,30]. For the cable-stayed bridge, Evripos cable-stayed bridge had been used GNSS for determining four modal frequencies 0.369, 0.389, 0.449, and 0.543 Hz [31], Batman in Australia [2], Shandong Binzhou Yellow [32], Tianjin Yonghe [18] in China, Nhat Tan, Nguyen Van Troi-Tran Thi Ly, Can Tho, Cao Lanh, Nhat Le, Thuan Phuoc in Vietnam [33–38]. Besides accelerometer sensors, GNSS plays a key role in structural health monitoring systems at all levels [39] and contributes to the safety assessment of the bridge [40,41]. It can be seen that GNSS had been widely used in monitoring fields in general and bridge monitoring in particular. The next part of the introduction will mention GNSS in dynamic displacement prediction of the bridges. There is no doubt about the importance of bridges in the development of the economy and society. However, bridges are often structurally thin when compared to their lengths and are subject to many influences from a variety of sources. This leads to bridge monitoring being an obligation, not an option. Monitoring the physical response of the structure under various conditions is crucial to evaluate its status and making maintenance plans. GNSS time-series data provide so much information about the structure due to the ability to acquire data in real time at very high frequencies. Dynamic displacement prediction is an interesting subject for researchers, and many approaches were discussed. Regarding conventional methods of statistical and analytical techniques [42], mentioned ARMA, ARIMA, Kalman, and Wavelet and compared them [43], studied the exponential smoothing method and provided some pieces of advice about the right smoothing constant at the end of the study, Neves and Cordeiro [44] suggested a resampling technique in time-series prediction, Ostertagova´ and Ostertag [45] used simple smoothing method to forecast, and Şanlio
glu and Kara [46] used AR and ARIMA model to analyze time-series data of IGS stations in Turkey. In terms of modern techniques, machine learning has recently become very popular in building prediction models in various application areas. Kis [47] listed a range of applications of machine learning in all fields including forecast and prediction. Forootan et al. [48] compared Kalman filter (KF) and neural technique (NN) in detecting and predicting bridge movement, and Moon et al. [49] used ANN for predicting vertical displacement. For a more detailed looking, a review of machine learning can be seen in Mahesh [50], a review of ANN can be seen in Mhatre et al. [51] and Bermejo et al. [52], a review of deep learning can be seen in Vargas et al. [53] and Abdel-Jaber et al. [54], and the advantages and disadvantages of artificial intelligence and machine learning can be seen in Kalicanin et al. [55] and Khanzode and Sarode [56]. It should be taken note that the above studies are for

Spatial prediction of bridge displacement Chapter

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439

either monitoring applications or other fields. A machine learning approach for structural health monitoring can be seen by de Castro Mota [57], and deep learning for the prediction model can be seen by Luo et al. [58]. Taking a look the modern techniques for predicting purposes, the occurrence of the studies is only about 5 years recently, and it is too difficult to find the answer to the question “Which is the best prediction model.” Paola Barba et al. [42] concluded that there is no standardized procedure for any time series when comparing four statistical and analytical techniques using GNSS geodetic time series although ARIMA was more highly appreciated than the others. Similarly, some remarkable conclusions can be seen in Almqvist [59]. In this study, the author compared ARIMA to three neural networks for time-series regression TCN, LSTM, and RNN, and ARIMA had the worst performance. The author also concluded that these findings have some uncertainties. Recently, machine learning becomes the centre of attention and the most popular topic in the research community [60]. It then covers a wide range of research with various approaches including clustering, Bayesian network, deep learning, and decision tree learning [53]. As a result, there is a boom in the number of publications on deep learning in all fields over a long period from 2006 to 2017 [53]. The originality of deep learning is ANN [54], and one of the uses of ANN models is prediction [52]; especially, ANN models have delivered good results for real-time estimations, especially when learning from dynamic changes in environmental conditions becomes a key factor to improve prediction accuracy [61]. One of the studies using deep learning to estimate dynamic displacement due to dynamic loads can be seen in Ok et al. [62], vertical displacement of the girder bridge under moving vehicles [49], or three machine learning techniques had been used for time-series prediction for the coastal bridge [63]. Going through some studies, it can be seen that deep learning is a research trend in time-series forecasting due to its benefits and advantages [55,56]. In this chapter, the group of authors studies five deep learning models for predicting dynamic displacement with GNSS time-series data combined with temperature data and strain gage data from sensors. The predicted results will be compared together.

2

Study area and data used

2.1 Co Luy bridge Co Luy is a river bridge that crosses the Tra Khuc River and is the longest stayed-cable bridge in Quang Ngai province. This bridge is 1877 m long, with 37 spans, 6 main spans, and 5 stayed-cable towers, and connects Tinh Khe and Nghia Phu, Quang Ngai City (Fig. 1). Co Luy Bridge has a complicated structural health monitoring system including hundreds of sensors with various types such as temperature sensors, wind sensors, displacement sensors, accelerometer sensors, strain gauge sensors, and GNSS sensors. The general layout of the sensors can be seen in Fig. 2.

440 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 1 Co Luy bridge. (A) Co Luy on Earth and (B) Co Luy on camera.

There are two GNSS sensors located on two sides of the middle stayed-cable tower. GNSS sensors are installed near temperature and strain gauge sensors (Fig. 3). Two GNSS devices are multi-frequencies, multi-channel devices of the Topcon brand. These devices are built for working in harsh environments. According to the catalogue of GNSS equipment that was used on the Co Luy bridge, the specific precision for the RTK method is
5 mm + 0.5 ppm (X baseline length) along to horizontal plane (X and Y directions) and
10 mm + 0.8 ppm along the vertical direction (Z direction), respectively.

2.2 Data used For building predicting model, the authors use GNSS data (X, Y, Z), strain gauge data (S), and temperature data (T) at 1-h sample intervals. The data start from July 01, 2022 to July 30, 2022, with 591 observations in total. The data used in this study are summarized and simulated in Tables 1 and 2, and Fig. 4.

3 Methods A general feed-forward neural network [64] transmits information from the input node to hidden nodes and from the hidden node to the output node. However, hidden layers are not connected. Therefore, sequence data like time series is a challenging problem for the traditional neural network. The drawback of the traditional neural network is solved by the RNN model [65], which has a sequence connection and feedback among hidden layers. The feedback aims

FIG. 2 The layout of the sensors in the SHM system.

442 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 3 The detailed layout of the GNSS sensors in the SHM system.

TABLE 1 Data used for spatial prediction of bridge displacement from GNSS sensor 1. Data classification

T

S

X

Y

Z

Min

30.7938

2504.8890

11.4218

889.5897

18.6388

25% data

35.0390

2506.4620

11.4271

889.6022

18.6886

Mean

36.5560

2508.4771

11.4290

889.6050

18.6946

Median

36.7346

2507.9940

11.4289

889.6052

18.6948

75% data

38.4582

2510.7240

11.4309

889.6078

18.7004

Max

40.7215

2514.6840

11.4429

889.6196

18.7418

to add the memory concept to the neural network. In other words, the training process depends not only on the current inputs fed to the input layer but also on the previous input values. Therefore, RNN can implement for sequence prediction like time series. Still, the vanilla RNN suffers from several drawbacks, such as vanishing gradients, so scholars have proposed more sophisticated types of RNNs to deal with this shortcoming. Long short-term memory (LSTM) [66], gated recurrent unit (GRU) [67], and bidirectional recurrent unit [68] are examples of RNNs. We introduce a brief overview of LSTM and GRU in the following sections.

Spatial prediction of bridge displacement Chapter

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443

TABLE 2 Data used for spatial prediction of bridge displacement from GNSS sensor 2. Data classification

T

S

X

Y

Z

Min

30.3173

2356.7990

4.8425

769.8824

18.6769

25% data

35.5544

2358.4100

4.8524

769.8958

18.7165

Mean

37.2435

2360.9327

4.8544

769.8989

18.7224

Median

37.5154

2359.8860

4.8541

769.8992

18.7225

75% data

39.4026

2363.4590

4.8563

769.9017

18.7284

Max

41.8366

2368.7450

4.8669

769.9251

18.7826

3.1 Long short-term memory (LSTM) The LSTM network model is built based on the basic RNN created by Hochreiter and Schmidhuber [66]. The goal is to overcome the shortcomings of RNNs, including long-term dependency in RNNs and vanishing and exploding gradients. In the first problem, the current state of the RNN cell is affected by the previous states even if the previous states’ timestamps are very far from the current ones. This is not logical in many real-world problems, such as text translations or voice recognition. In the second problem, the current weight is updated based on the partial derivative of the loss function. The problem is laying the gradient when it becomes tiny or vanishingly small, leading to stopping the updating process in RNNS. By proposing the gates concepts, LSTM can overcome these problems in RNN. There are three types of gates: forget gate, input gate, and output gate in LSTM. The forget gate uses the sigmoid function applied to the current input and previous hidden state to decide their contribution in the next state, as shown in Eq. (1). This gate is the key to solving the longterm dependency problem of RNN. The input gate is mixed by sigmoid and tanh functions applied to the input and hidden state to calculate the current state of the LSTM cell (Eqs. 2 and 3). The output of the last two gates is used for calculating the next hidden state of the cell (Eqs. 4 and 5).
 f t ¼ σ W f xt + U f ht1 + bf (1) it ¼ σ ðW i xt + U i ht1 + bi Þ

(2)

ct ¼ f t ct1 + it tanhðW c xt + U c ht1 + bc Þ

(3)

ot ¼ σ ðW o xt + U o ht1 + bo Þ

(4)

ht ¼ ot tanhðct Þ

(5)

FIG. 4 Distribution density of the data. (A) Station GNSS1 and (B) station GNSS2.

Spatial prediction of bridge displacement Chapter

445

24

yt

x

Ct-1

Ct

+ tanh

ft s

x

~ Ct

it

ot

s

x

s

tanh

ht-1

ht Xt

FIG. 5 The long short-term memory (LSTM) cell. (Credit: https://dprogrammer.org/rnn-lstm-gru.)

Fig. 5 presents the flow and relation among gates in the LSTM cell. As can be seen, the LSTM cell has many parameters and needs much more processing than the traditional neural network.

3.2 Gated recurrent unit (GRU) Gated recurrent unit (GRU) is also a variant of RNNs proposed by Chung et al. [67]. The idea of GRU is similar to LSTM, which is based on gates to control the flow of information. However, due to the simpler architecture and relatively new as compared to LSTM, GRU offers some improvement over LSTM. As shown in Fig. 6, GRU does not have a separate cell state (Ct). It only has a hidden state (Ht). Therefore with less computation, GRUs are faster to train than LSTM. In GRU, the input gate and forget gate are merged into a single update gate of LSTM. Therefore, GRU can also solve the problem of vanishing-exploding gradients like LSTM. The mathematical form of each unit in GRU is as follows [69]: zt ¼ σ ðwxz xt + whz ht1 + bz Þ RNN

LSTM

GRU

ht

ot ht

ht-1

(6)

Ct-1

x

ht Ct

+

ht-1

x

tanh x

tanh

ht-1 xt

s

xt

s

tanh

x

x s

1s

ht

s

+ x tanh

xt

FIG. 6 The comparison of RNN, LSTM, and GRU structure. (Credit: https://dprogrammer.org/ rnn-lstm-gru.)

446 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 7 The gated recurrent unit (GRU) cell. (Credit: https://dprogrammer.org/rnn-lstm-gru.)

r t ¼ σ ðwxr xt + whr ht1 + br Þ

(7)

het ¼ tanhðwxh xt + whh ht1 + bh Þ

(8)

ht ¼ ð1  zt Þht1 + zt :het

(9)

where xt and ht1 are the current input and the previous output of GRU, respectively. wxz, whz, wxr, whr, wxh, and whh are weights in GRU. bz, br, and bh are biases. zt and rt are outputs during intermediate procedures, and ht is the final output of the GRU cell. Fig. 7 shows the structure of the GRU cell.

3.3 Proposed deep learning models In this section, we described our proposed models for analyzing strain gage, temperature, and GNSS observations, and displacement prediction, including vanilla LSTM, stacked LSTM, bidirectional LSTM, vanilla GRU, and stacked GRU (Fig. 8). Vanilla LSTM

Input

LSTM cell

Dense

Output

Vanilla GRU

Input

GRU cell

Dense

Output

Bidirectional LSTM

Input

Bidirectional LSTM layer

Dense

Output

Stacked LSTM

Input

LSTM cell

LSTM cell

Dense

Output

Stacked GRU

Input

GRU cell

GRU cell

Dense

Output

FIG. 8 Proposed deep learning models.

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(a) The stacked RNN-based models The stacked RNN or deep RNN model is the type of RNN with multiple RNN layers combined to make the network deeper. The idea behind this architecture was inspired by Graves et al. [70]. In conclusion, more RNN layers stacked on top of each other lead to a more complex network and generalize more complex problems such as time-series forecasting. (b) The bidirectional LSTM model A bidirectional neural network makes a neural network have the sequence information in both directions, backward (future to past) or forward (past to future). In the vanilla LSTM, the input flows in one direction, either backward or forward. However, in bidirectional LSTM, the input flows in both directions to preserve future and past information. This network can be used in text classification, speech recognition, and forecasting models.

3.4 Setting parameters In this paper, for both GNSS1 and GNSS2 datasets, the objective is to predict the coordinates of X, Y, and Z of the GNSS sensors. The features used include the temperature, frequency, and X, Y, and Z coordinates of the sensors from the previous timestamp. Since the data is recorded in around 1 month with 1-h intervals, we use the previous 6 h0 history of GNSS sensor coordinates (lag features ¼ n-steps¼ 6). The output will be three outputs corresponding to three coordinates of the sensor. All experimental deep learning models use the “Adam” optimizer, MSE, as a loss function, the maximum epochs is 1000, and the batch size is 6. In addition, the number of hidden nodes for networks like vanilla LSTM/GRU and bidirectional is 50 hidden units. The number of hidden nodes for the LSTM/GRU stacked network is 50 and 30 for the first and second hidden layers, respectively.

3.5 Forecasting performance metrics In this paper, the analysis of output error using three metrics includes mean squared error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The mathematical representation and the characteristics of those performance indices can be found in Van Thieu [71]. ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sX N ðy  ybi Þ2 i¼1 i b RMSEðy, yÞ ¼ (10) N

448 Applications of artificial intelligence in mining and geotechnical engineering N 100% X j yi  ybi j N i¼1 j yi j XN j y  ybi j i¼1 i MAEðy, ybÞ ¼ N

MAPEðy, ybÞ ¼

(11)

(12)

where yi is the actual observed value; ybi predicted value of y; N the number of samples in the dataset. To carry out the choosing of a model, some suitable metrics need to be determined to assess the model’s accuracy. In a predicting model with time-series data, RMSE is normally used. This metric measures the error at each point in time and takes the square of it. The square root of the average of those squared errors is called the RMSE. Another frequently used metric is the mean absolute error (MAE): rather than taking the square of each error, it takes the absolute value here. The mean absolute percent error (MAPE) is a variation on this where the absolute error at each point in time is expressed as a percentage of the actual value. For the models used in this study, RMSE, MAE, and MAPE had been selected to assess model performance.

4 Results and analysis Before developing predicting models, the dataset had been taken into account and pre-processed to eliminate components that make noise or reduce the accuracy of the models. A total of 591 observations for each GNSS device are then divided into two parts: 70% for the training model and 30% for accuracy testing trained models. The paper used five advanced deep learning models and time-series data to predict, and the last purpose is to choose the best model. This model has the best results for the data used and will then be the potential model for the other similar dataset. The training results and the performance of the five models are shown in Tables 3 and 4 for GNSS1 and GNSS 2, respectively. The indices RMSE, MAE, and MAPE are used to evaluate the performance of deep learning models’ training process. Rating scores are ranked from low to high, equivalent to a score of 1–5 points. The training results and the performance of five models on GNSS1 and GNSS2 data are in Figs. 9 and 10. Based on the results in Tables 3 and 4, the training results of the LSTM and GRU models with two datasets of the two GNSS devices to dynamic displacement prediction of the bridge show very small errors and tiny differences among the models. Similar results are recorded on the testing datasets in Tables 5 and 6. Based on rating scores, it can be seen that Stack LSTM has the highest accuracy in the training process. This model has higher accuracy either on the training data or on the testing data in dynamic displacement prediction for all X, Y, and Z directions. Taking a look at four tables, five LSTM and GRU models have

TABLE 3 Performance of five deep learning models in the training process (GNSS1). Metrics

Rating score

Model

RMSE

MAE

MAPE

RMSE

MAE

MAPE

Total

Vanilla LSTM

0.00033

0.00024

0.00001

3

3

1

7

Stack LSTM

0.00016

0.00011

0

5

5

5

15

Bidirectional LSTM

0.00032

0.00023

0.00001

4

4

1

9

Vanilla GRU

0.00038

0.00028

0.00001

1

1

1

3

Stack GRU

0.00037

0.00028

0.00001

2

1

1

4

TABLE 4 Performance of five deep learning models in the training process (GNSS2). Metrics

Rating score

Model

RMSE

MAE

MAPE

RMSE

MAE

MAPE

Total

Vanilla LSTM

0.00031

0.00023

0.00002

3

3

1

7

Stack LSTM

0.00014

0.00011

0.00001

5

5

4

14

Bidirectional LSTM

0.00034

0.00025

0.00002

1

1

1

3

Vanilla GRU

0.00033

0.00024

0.00002

2

2

1

5

Stack GRU

0.00022

0.00016

0.00001

4

4

4

12

TABLE 5 Performance of five deep learning models in the testing process (GNSS1). Metrics

Rating score

Model

RMSE

MAE

MAPE

RMSE

MAE

MAPE

Total

Vanilla LSTM

0.00572

0.00402

0.00019

1

2

1

4

Stack LSTM

0.00520

0.00361

0.00017

4

5

4

13

Bidirectional LSTM

0.00517

0.00375

0.00017

5

4

4

13

Vanilla GRU

0.00542

0.00404

0.00019

2

1

1

4

Stack GRU

0.00529

0.00381

0.00018

3

3

3

9

TABLE 6 Performance of five deep learning models in the testing process (GNSS2). Metrics

Rating score

Model

RMSE

MAE

MAPE

RMSE

MAE

MAPE

Total

Vanilla LSTM

0.00558

0.00381

0.00026

1

3

2

6

Stack LSTM

0.00506

0.00365

0.00024

4

4

4

12

Bidirectional LSTM

0.00545

0.00392

0.00027

3

1

1

5

Vanilla GRU

0.00546

0.00389

0.00026

2

2

2

6

Stack GRU

0.00483

0.00350

0.00023

5

5

5

15

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FIG. 9 Performance of five models based on the training dataset of GNSS1 monitoring point.

good performance and small deviations. In the training process, Stack LSTM has better results compared to the rest of the models. For GNSS1, the RMSE and MAE of the Stack LSTM model are 0.00016 and 0.00011, respectively, while the RMSE and MAE of the four rest models range from 0.00032 to 0.00038 and from 0.00023 to 0.00028, respectively. For GNSS2, the RMSE and MAE of the Stack LSTM model are 0.00014 and 0.00011, respectively while the RMSE and MAE of the four rest models range from 0.00022 to 0.00034 and from 0.00016 to 0.00025, respectively. In the testing data, the Stack LSTM model performed as well as bidirectional LSTM for GNSS1 data and ranked right after the Stack GRU model for GNSS2 data. However, these deviations are small, and Stack LSTM still gives better results than the rest of

454 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 10 Performance of five models based on the training dataset of GNSS2 monitoring point.

the models in the group of five models. The predicting performance of five models based on temperature, strain gauge data, and X, Y, and Z data from the RTK technique of two monitoring points GNSS1 and GNSS2 are shown in Figs. 11 and 12. It can be seen that there are some abnormal variations between predicted and real observations. However, the dynamic displacement of the bridge is complicated and affected by many factors. It thus can be accepted. In addition, the dataset is limited in the number of observations and deep learning models need

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455

FIG. 11 Performance of five models based on the test dataset of GNSS1 monitoring point.

a lot of data to learn and train, so the results on the test dataset may not be as good as on the training dataset.

5

Discussions

The paper studies five deep learning models to predict the dynamic displacement of the bridge under the combination of X, Y, and Z time-series data, temperature, and strain gauge data. The results in the above tables and figures indicated the feasibility of these models in dynamic displacement prediction. Based on the error criteria, stack LSTM has the best performance compared to the rest of the models.

456 Applications of artificial intelligence in mining and geotechnical engineering

FIG. 12 Performance of five models based on the test dataset of GNSS2 monitoring point.

For all five models, the components X and Y have better results than Z components when comparing true and predicted observations. This is suitable with the reality of the GNSS technique for determining Z components because of the affection of meteorological conditions in general and the variation of the bridge in particular and previous research. The study is the initial result of combining various factors including wind data, humidity data, loading data of the vehicle, and load cell sensors for better performance. Dynamic displacement is one of the important types of physical data in a structural health system of the bridge. Bridge structures vary continuously

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under the affection of different factors, and monitoring physical response and prediction is necessary to ensure the safety of humans and structures although this task is relatively difficult. The development of AI and the support of computers allow to simultaneously perform at the same time, and prediction with complicated inputs is feasible. This is the biggest advantage of these models. Based on statistical criteria, the best model had been found. However, finding the best structure, the best active function, and the optimum number of nodes and hidden layers in each model is costly and time-consuming. In the present study, the number of independent variates is two, and the number of observations is 591 while the real factors are much more in reality. The factors including wind speed, speed, loading of the vehicles, and design parameters need to be taken account into in future studies.

6

Conclusions

Dynamic displacement prediction of the bridge is a challenging problem due to the affection of various factors including static loading of the bridge, dynamic loading caused by vehicles, wind, affection of temperature, and meteorological conditions. It is not easy to control the parameters of meteorological conditions and parameters of vehicles such as speed and weight. This means that the dynamic displacement prediction of the bridge is not simple. This research used five AI models (vanilla LSTM, stack LSMT, bidirectional LSTM, vanilla GRU, and stack GRU) to predict the dynamic displacement of the Co Luy bridge, and the data is a combination of multivariate. The relationship between temperature and strain gage with X, Y, and Z extracted from the GNSS-RTK technique is taken account into in five models. The initial results of the accuracy of the five models are the feasibility of artificial intelligence in spatial displacement prediction of the bridges. Stack LSTM has the best performance compared to the four rest models in this study based on error criteria and rating scores. However, these five models need to be extended in the future with the participation of other factors. The number of input variates needs to be optimized, and different AI models need to be taken account into to improve the accuracy in the dynamic displacement prediction process of the bridges.

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Index Note: Page numbers followed by f indicate figures and t indicate tables.

A Adams algorithm, 158 Adaptive neuro-fuzzy inference system (ANFIS), 226–231, 304 defuzzification layer, 228, 231 fuzzy model, 226–227 normalization layer, 227 rule base, 230t rule layer, 227 statistical performance indicators, 231t structure of, 229–230, 229f Air-overpressure, 269, 270t Ant lion optimizer (ALO), 327–328 Artificial intelligence (AI), 270–271, 299–306, 300f Artificial neural network (ANN), 49–51, 118, 132, 198, 270–271, 304, 321–322 hyperparameters, 60 pile capacity, construction site, 401 trained and developed, 60–64 Average absolute relative deviation (AARD), 150–151

B Backfill design, 221–223 Backpropagation neural network (BPNN), 255, 326 Baltic Dry Index (BDI), 150–151 Bayesian optimization (BO), 271, 276–277 Bivariate non-linear regression (BNLR), 150–151 Blast-induced ground vibration backpropagation neural network, 255 BPNN and PSO-BPNN models, 260, 260–261t empirical models, 256, 256t, 261, 262t loss function values, 259t model development, 257–258, 258f particle swarm optimization, 254–255 performance results, 262–266, 263–264f, 263t, 265t PSO algorithm, 259–260 statistical evaluation, 258–259 support vector machine, 255–256 SVM and PSO-SVM models, 261, 261t

Blasting artificial intelligence (AI), 299–306, 300f artificial neural networks, 301–302 empirical approach, 296–299, 297–298t, 298f fragmentation, 295–296 genetic algorithms (GAs), 302 hybrid approaches, 303–306, 304–305t machine learning, 302–303 prediction, 297–298t Blasting fragmentation correlation methods, 350, 350t data analysis and preprocessing, 344–351 GOA-SVR prediction models, 354–355, 355t grasshopper optimization algorithm (GOA), 351–353 KHA-SVR prediction models, 354–355, 356t krill herd algorithm (KHA), 353–354 mean fragment size (MFS), 343–344 MFS prediction model, 354–355 PSO-ANFIS method, 343–344 statistical results, 346t support vector regression, 351 ununiform distribution, 345 v-support vector regression (SVR), 343–344

C CAPEX estimation, 137–138, 138f, 143–145f Capital expenditure (CAPEX), 131 Cd2+ absorption, halloysite absorption efficiency values, 90–92 accuracy, 91f artificial neural network, 80, 82f DE algorithm, 83 efficiency prediction, 88–90t estimation, 84–85, 86–87f materials description, 77–80, 78–79t, 81f optimized ANN, 84, 85f particle swarm optimization, 82–83 performance metrics, 90t, 92 predicted values, 92 slime mold algorithm, 81–82 Cemented hydraulic backfill (CHB), 221

463

464 Index Cemented paste backfill (CPB) design key design parameters, 237t materials, 233–235, 238–239 mathematical optimization, 242–243t mixture designs, 234–235, 235t, 244f objective functions using ML, 235, 240–241 optimization, 236, 241–242 prepared CPB batches, 234t stope stability, 234–235 Chi-square automatic interaction (CHAID), 270–271 Classification and regression tree (CART), 24, 132, 185–186, 275 Cloud-inference expert semiquantitative (CESQ), 299 Co Luy bridge, 439–440 Convolutional neural network (CNN), 151, 153–155, 154f, 367 Adams algorithm, 158 dataset, 155–157, 156–157f early stopping technique, 158 forecasting performance, 160f performance metrics, 161t structure of, 155f training performance, 159f Covalent organic framework (COF), 76 Cubist algorithm, 117, 118f Cutting edge mining technologies, 315

D Decision tree (DT), 403–404 Deep learning (DL) algorithms, 362 analysis of rock fracture, 371–373 classification and detection, 368–369 CNN architecture, 367f rock mass and particle size analysis, 369–371 rock mass parameters, 373–374 Determination coefficient (R2), 100–101 Differential evolution (DE), 27–28, 28–29f Differential evolution multi-layer perceptron (DE-MLP) model, 92 Digital photogrammetry, 363 Discrete Fracture Network (DFN), 306 Dynamic displacement, 455–457

E Equivalent linear overbreak slough (ELOS), 322 Extreme gradient boosting (XGBoost), 275–276, 279f Extreme learning machine (ELM), 198–199

F Factor of safety (FOS), 97 Feed-forward neural network, 440–448 Five-folds cross-validation, 140, 140t Frequency ratio (FR), 425–432, 426–428t Fuzzy Delphi method (FDM), 270–271 Fuzzy expert system (FES), 322

G GA algorithm, 133, 134f Gated recurrent unit (GRU), 153 Gaussian maximum-likelihood classifier (GMLC), 198 Gene expression programming (GEP), 405 Generalized regression neural network (GRNN), 333–335 Genetic algorithm for neural network (GA-NN), 150–151 Genetic algorithms (GAs), 120, 302 Genetic programming (GP), 199, 405 classifier settings, 208t confusion matrices, 210–211, 210t description, 201–202 flowchart, 202f parse-tree structure, 208–209, 209f sensitivity analysis, 206–208t Genuine microseismic events classification performance, 209–212, 210–211t, 211f database and statistical analysis, 199–201, 200f, 201t linear discriminant analysis (LDA), 203 meta-heuristic algorithm, 201–203, 202f model construction, 203–209, 204f, 206–208t, 209f Geographic information system (GIS), 379–380 Geotechnical engineering field, 397 Global navigation satellite system (GNSS) Co Luy bridge, 439–440 data used, 440 dynamic displacement, 437–438, 455–457 feed-forward neural network, 440–448 forecasting performance metrics, 447–448 gated recurrent unit (GRU), 445–446 geodetic time series, 439 LSTM network model, 443–445 proposed deep learning models, 446–447 results and analysis, 448–455 setting parameters, 447 time-series data, 438–439 Gradient boosting machine (GBM), 25–27, 26f Granite quarry, 253f

Index

Granite, UCS compressional wave velocity, 46t dry density, 56f effective porosity, 46t experimental database, 52–53 histograms, 59f hyperparameters of ANN models, 60 limitations and future works, 67 materials, 47–49 non-linear multi-parameter association, 47 performance indexes, 53–55 prediction accuracy comparisons, 64–66 soft-computing models, 48t splitting of database datasets, 55–60 statistical parameters, 54t trained and developed ANN models, 60–64 Grasshopper optimization algorithm (GOA), 150–151, 351–353 Gray wolf optimization (GWO), 119 Groundwater potential assessment description, 379–381 groundwater potential, 390–391, 390f, 392–393f groundwater predictors, 382–384, 383–384f multicollinearity assessment, 385, 387, 387t ROC curve, 386–387 study of, 381 support vector machine (SVM), 385–386, 386f validation, 391, 394f weights of evidence (WOE), 385, 388–389, 388–390t wells inventory, 381, 382f Groundwater predictors, 382–384, 383–384f

H HHO algorithm, 136–137, 137f Hydrological Soil Group (HSG), 423

465

Imperialism competitive algorithm (ICA), 303–304 Imperialist competitive algorithm (ICA), 99, 99f Industry 4.0, 1–2 Intersection algorithm, 306

K k-nearest neighbors (KNN), 24, 270–271 Krill herd algorithm (KHA), 353–354

L Landslide inventory, 421 Landslide predictors, 421–424, 422f, 424f Landslide susceptibility frequency ratio (FR), 425–432, 426–428t landslide inventory, 421 landslide predictors, 421–424, 422f, 424f mapping, 429–431, 429–431f multilayer perceptron, 425 research study, 420, 421f ROC curve, 425–426 validation, 432, 433f weights, 432f Landslide susceptibility map (LSM), 419–420, 429–431, 429–431f Levenberg-Marquardt Artificial Neural Network, 150–151 Lithology, 423 Long and short-term memory neural network (LSTM), 151–153 Adams algorithm, 158 architecture, 152, 152f dataset, 155–157, 156–157f early stopping technique, 158 forecasting performance, 160f network model, 443–445 performance metrics, 161t training performance, 159f

I

M

ICA-RBFNN model, 99–100 data preparation, 101–104, 101–102f framework of, 100f model assessment metrics, 100–101 model development, 102–104 optimization performance, 103f performance metrics, 104–109, 105t, 106f vs. PSO-RBFNN, 106f ICT-based mine safety management system, 3, 3f

Machine learning models, 198, 270–271, 302–303 dynamic capability of models, 192–193 enhanced prediction capability, 188 optimization objective function, 190–192 proposed objective functions, 191 schedule parametric inputs, 187–188 symbiotic resemblance of model, 188–190 Matthew’s correlation coefficient (MCC), 209–210

466 Index Mean absolute error (MAE), 100–101, 150–151 Mean absolute percentage error (MAPE), 100–101, 153, 231 Mean square error (MSE), 150–151 Metaheuristic algorithm genetic algorithm (GA), 120 gray wolf optimization (GWO), 119 particle swarm optimization (PSO), 119–120 Metaheuristic algorithms GA algorithm, 133, 134f HHO algorithm, 136–137, 137f MFO algorithm, 136, 136f PSO algorithm, 134–135, 135f MFO algorithm, 136, 136f MFS prediction model, 354–355 Mine planning and scheduling conceivable production loss, 192 dynamic capability of models, 192–193 machine learning, 185–193 mitigatory attempts, 185 optimization objective function, 190–192 overview, 183–185 schedule parametric inputs, 187–188 symbiotic resemblance of model, 188–190 Mining supply chain advanced analytics tools, 172–173 blending grade constraints, 178 constraints, 174–178 decision variables, 174 distributor configuration, 166f equipment network, 178–179 grade blending, 170 hypothetical diagram, 168f impacts of distributors’ options, 180–181 linear and integer variables, 177 modified mine plan, 180 non-negativity constraints, 178 objective function, 174 optimization model, 173–178 optimization solvers, 169 overview, 165–166 planned maintenance, 181 results and comparison, 179 SAG mill rate, 172 SAG stockpile capacity constraints, 177 site’s process, 165 thousands of possible routes, 171–172 using mathematical programming, 167–168 utilizing SAG mills, 172 Min-max scaling method, 140 Mixed integer programming (MIP), 168 MLP Neural Nets model, 17 Monte Carlo simulation, 306

Multicollinearity assessment, 385, 387, 387t Multi-layer perceptron (MLP), 80 artificial neural network (ANN), 118 dataset, 114–116, 116t downscaling technique, 126 input variable selection, 117, 118f metaheuristic algorithm, 118–120 neural network, 76–77, 425 performance metrics, 124t swarm size, 120–122 training and validation phases, 123–124f training performance, 121–122f Multilayer perceptron neural networks (MLPNs), 76–77, 425 Multi-objective optimization (MOO) algorithm, 243 Multiple Linear Regression (MLR), 150–151 Multiple Non-Linear Regression (MNLR), 150–151

N “No Free Lunch (NFL)” theorem, 199 Non-linear multiple regression modeling, 225

O Open-pit mining, 23 Optimized extreme gradient boosting model, 277–278 Overbreak and underbreak area (OUA) ant lion optimizer, 327–328 BPNN, 326 comparation performance, 333–338 data preparation, 328–330 hybrid ALO-BPNN model, 331–333 performance evaluation, 328–330 sensitively analysis, 339

P Particle size distributions (PSD), 238 Particle swarm optimization (PSO), 24, 28–29, 30–31f, 119–120, 134–135, 135f, 251–252, 259–260 Particle swarm optimization-adaptive network-based FIS (PSO-ANFIS) method, 343–344 Pile capacity, construction site artificial neural network (ANN), 401 base AI models, 406–409 correlation coefficients, 412t CPT researchers, 412t

Index

decision tree (DT), 403–404 future aspects, 413–414 gene expression programming (GEP), 405 genetic programming (GP), 405 geotechnical capacity, 398 hybrid AI models, 409 installation, 398 SC method, 409–412, 413t soft computing, 399–405 support vector machine (SVM), 401–403 Planned and unplanned dilution, 316f PM2.5 emissions data acquisition, 33–35, 34–35f DE, PSO, GBM model integration, 30–32 differential evolution (DE), 27–28, 28–29f gradient boosting machine (GBM), 25–27, 26f input characteristics, 36t optimization performance, 38f particle swarm optimization (PSO), 28–29, 30–31f performance metrics, 33, 39t preprocessed dataset, 36 regression analysis, 40f training vs. testing datasets, 39f Powder factor (PF), 272 Predictive modeling practice, 224–232 Profile curvature, 423 Proposed UMS-AM system air quality monitoring measurement, 15, 15f, 16t air quality monitoring system, 12, 12t, 13f data packaging, 13 hardware interconnection, 13–14 MLP Neural Nets model, 17 multilayer perception neural network, 16–17, 16f optical sensors, 10–12, 11–12f statistics difference, 17t study site, 14–15, 14f trilinear interpolation method, 18, 20f UAV platform, 9–10, 9t, 10f PSO-RBFNN model vs. ICA-RBFNN, 106f optimization performance, 104f performance metrics, 104–109, 105t, 106f unbiased assessment, 103–104

R Radial basis function neural network model (RBFNN), 76–77, 98, 98f, 132, 133f benchmark ranking, 143t CAPEX estimation, 137–138, 138f, 143–145f

467

data preparation, 139, 139f five-folds cross-validation, 140, 140t metaheuristic algorithms, 133–137 min-max scaling method, 140 optimization, 141, 142f performance metrics, 138 testing dataset, 140t training dataset, 139t Random forest (RF), 24 Random search (RS) algorithm, 271 Receiver operating characteristic (ROC), 420 Recurrent neural networks (RNN), 151, 153 Relative standard deviation (RSD), 150–151 Remote sensing, 379–380 ROC curve, 386–387 Rock engineering system (RES), 299 Rock excavation, 325 Rock fragmentation, 291, 293–295t artificial neural networks, 301–302 challenges and future aspects, 307–309, 307–308t genetic algorithms (GAs), 302 hybrid approaches, 303–306, 304–305t machine learning, 302–303 traditional methods, 292 Rock mass characterization acquisition method, 362–365 analysis of rock fracture, 371–373 classification and detection, 368–369 CNN architecture, 367f deep learning algorithms, 367–374 groundwater extraction, 373 lithology classification, 368–369 rock mass and particle size analysis, 369–371 rock mass parameters, 373–374 traditional image algorithms, 365–367 Rock quality designation (RQD), 272 Root Mean Square Error (RMSE), 150–151

S SAG stockpile capacity constraints, 177 Set pair analysis (SPA), 306 Smart mining site artificial intelligence, 4–5 future perspectives, 5 ICT-based mine safety management system, 3, 3f implementation level, 2–4 modeling and simulation (M&S), 4–5 requirements, 1–2 VR and AR technology application, 4f

468 Index Stope dilution artificial neural networks (ANNs), 321–322 cutting edge mining technologies, 315 effective dilution control, 322 feature range and selection, 318–320 machine learning applications, 317–322 planned and unplanned dilution, 316f uneven break causative factors, 316–317 Support vector machine (SVM), 132, 255–256, 270–271, 380–381, 385–386, 386f, 401–403 Support vector regression (SVR), 304

T Topographic wetness index (TWI), 423 Total suspended particles (TSP), 23 Traditional image algorithms, 365–367 Trilinear interpolation method, 18, 20f Tunnel construction, 361–362

U Unascertained measurement (UM), 304–305t, 305 Unconfined compressive strength (UCS), granite compressional wave velocity, 46t dry density, 56f

effective porosity, 46t experimental database, 52–53 histograms, 59f hyperparameters of ANN models, 60 limitations and future works, 67 materials, 47–49 non-linear multi-parameter association, 47 performance indexes, 53–55 prediction accuracy comparisons, 64–66 soft-computing models, 48t splitting of database datasets, 55–60 statistical parameters, 54t trained and developed ANN models, 60–64 Uniaxial compressive strength (UCS), 221–222, 272 Unmanned aerial vehicle (UAV) contributions, 9 proposed UMS-AM system, 9–14 work related, 8–9

V VibraZEB seismograph, 272

W Wavelet neural network (WNN) models, 321 Weights of evidence (WOE), 385, 388–389, 388–390t