Proceedings of the Sixth International Conference of Transportation Research Group of India: CTRG 2021 Volume 1 (Lecture Notes in Civil Engineering, 271) 9811935041, 9789811935046

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Lecture Notes in Civil Engineering

Lelitha Devi Animesh Das Prasanta Kumar Sahu Debasis Basu   Editors

Proceedings of the Sixth International Conference of Transportation Research Group of India CTRG 2021 Volume 1

Lecture Notes in Civil Engineering Volume 271

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

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Lelitha Devi · Animesh Das · Prasanta Kumar Sahu · Debasis Basu Editors

Proceedings of the Sixth International Conference of Transportation Research Group of India CTRG 2021 Volume 1

Editors Lelitha Devi Department of Civil Engineering Indian Institute of Technology Madras Chennai, Tamil Nadu, India

Animesh Das Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur, Uttar Pradesh, India

Prasanta Kumar Sahu Department of Civil Engineering Birla Institute of Technology and Science Pilani, Hyderabad Campus Hyderabad, Telangana, India

Debasis Basu Department of Civil Engineering Indian Institute of Technology Bhubaneswar Bhubaneswar, Odisha, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-19-3504-6 ISBN 978-981-19-3505-3 (eBook) https://doi.org/10.1007/978-981-19-3505-3 © Transportation Research Group of India 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The proceedings of the 6th Conference of Transportation Research Group (CTRG) of India is being published in three volumes, and this is Volume-1. The 6th CTRG was held in Tiruchirappalli (Trichy), Tamil Nadu, India, during December 14–17, 2021, and all the papers finally selected for presentation went through a doubleblind review process. The conference had nine tracks, and the present volume hosts three such tracks, namely A01: Pavements and materials, C01: Transport planning, policy, economics and project finance, and G01: Transport and mobility networks (including public transportation, freight and logistics). There is a total of 24 papers in this volume, out of which 11 papers are related to track A01, eight papers are related to track C01, and five papers are related to track C01. We are thankful to the co-chairs of these tracks, namely Prof. Dharamveer Singh— IIT Bombay, Prof. Bhargab Maitra—IIT Kharagpur, and Dr. S. Velmurugan—CSIRCRRI, New Delhi, for their help during the entire process of developing the contents for this volume. We also take this opportunity to thank all the reviewers for helping us significantly during the review process. A wide spectrum of topics has been covered in the papers contained in this volume, including pavement design, pavement material characterization, data-driven transportation planning, traffic safety, and transit planning. We sincerely hope that fraternity of transportation researcher and practitioners will find this compilation useful. Chennai, India Kanpur, India Hyderabad, India Bhubaneswar, India

Prof. Lelitha Devi Prof. Animesh Das Prof. Prasanta Kumar Sahu Prof. Debasis Basu

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About TRG and CTRG

Transportation Research Group of India (TRG) is a not-for-profit registered society with the mission to aid India’s overall growth through focused transportation research, education, and policies in the country. It was formally registered on 28th May 2011 and has completed 10 years of its journey this year. The following are the Vision and Objectives of TRG

Vision • To provide a unique forum within India for the interchange of ideas among transportation researchers, educators, managers, policymakers from India and all over the world, with the intention of covering all modes and sectors of transport (road, rail, air, and water; public and private; motorized and non-motorized) as well as all levels (urban, regional, inter-city, and rural transport) and for both passenger as well as freight movement, in India. At the same time, to also address the transportation related issues of; safety, efficiency, economic and social development,

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About TRG and CTRG

local and global environmental impact, energy, land-use, equity and access for the widest range of travellers with special needs etc. • To serve as a platform to guide and focus transportation research, education, and policies in India towards satisfying the country’s needs and to assist in its overall growth.

Objectives • To conduct a regular peer-reviewed conference in India so as to provide a dedicated platform for the exchange of ideas and knowledge among transportation researchers, educators, managers, and policymakers from India and all over the world, from a perspective which is multi-modal, multi-disciplinary, multi-level, and multi-sectoral, but with an India centric focus. Initially, this conference will be held every two years; however, the frequency may change as per the decision of the society from time to time. • To publish a peer-reviewed journal of good international standard that considers and recognizes quality research work done for Indian conditions, but which also encourages quality research focused on other developing and developed countries that can potentially provide useful learning lessons to address Indian issues. • To conduct other activities such as seminars, training and research programs, meetings, discussions etc., as decided by the society from time to time, towards fulfilling the mission and vision of the society. • To identify pertinent issues of national importance, related to transportation research, education and policy through various activities of the society, and promote transportation researchers, educators, managers, and policymakers in an appropriate manner to address the same. • To collaborate with other international societies and organizations like, WCTRS, ASCE, TRB etc., in a manner that works towards fulfilling the mission and vision of the society. The Conference of Transportation Research Group of India (CTRG) is the premier event of TRG. It is held every two years and traditionally moves around India. In the past, CTRG has been organized in Bangalore (Dec. 2011), Agra (Dec. 2013), Kolkata (Dec. 2015), Mumbai (Dec. 2017), Bhopal (Dec. 2019), Trichy (Dec. 2021) and Surat (upcoming in Dec. 2023 jointly with SVNIT Surat). CTRG has been getting wide scale recognition from reputed Indian and international institutions/organizations like, IIT Kanpur, IIT Kharagpur, IIT Guwahati, IIT Bombay (Mumbai), SVNIT Surat, MANIT Bhopal, NIT Trichy, TRB, WCTRS, CSIR-CRRI, ATPIO, T&DIASCE, EASTS, to name a few. CTRG is a large conference typically attended by around 400-500 participants, usually from 12–15 countries, with about 200 doubleblind peer-reviewed technical papers being presented. The conference provides a wide range of Executive Courses, Tutorials, Workshops, Technical Tours, Keynote Sessions, and Special Sessions.

About TRG and CTRG

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Transportation in Developing Economies (TiDE) is the official journal of TRG and is published by Springer. TiDE was formally launched in 2014 and has so far published 8 volumes.

Prof. Akhilesh Kumar Maurya Indian Institute of Technology, Guwahati Current President, TRG

Reviewers

Ammu Gopalakrishnan, School of planning and architecture, Delhi, India Ankit Gupta, Shiv Nadar University Dadri, India Niraj Sharma, CRRI, Delhi, India Padmarekha, SRM, Chennai, India Saurabh Dandapat, PwC India, India Yogeshwar Navandar, NIT Calicut, India Abdul Pinjari, IISc Bangalore, India Abdullah Ahmad, NIT Srinager, India Aboelkasim Diab, Aswan University, Egypt Aditya Kumar Das, S’O’A University Bhubaneswar, India Aditya Medury, IIT Kanpur, India Agnivesh Pani, IIT (BHU) Varanasi, India Akhilesh Chepuri, GITAM School of technology Hyderabad, India Akhilesh Kumar Maurya, IIT Guwahati, India Ambika Behl, CRRI, New Delhi, India Ambika Kuity, NIT Silchar, India Amit Agarwal, IIT Roorkee, India Amit Kumar Yadav, Central University of Jharkhand, India Aneena Mohan, IIT Bombay, India Aninda Bijoy Paul, SVNIT, Surat, India Anjan Kumar Siddagangaiah, IIT Guwahati, India Ankit Kathuria, IIT Jammu, India Ankit Kumar Yadav, IIT Bombay, India Ankit Yadav, IIT Bombay, India Anna Charly, IIT Bombay, India Anuj Budhkar, IIEST, Shibpur, India Anush Chandrappa, IIT Bhubaneswar, India Anusha S. P., College of Engineering Trivandrum, India Apparao Gandi, NICMAR Hyderabad, India Aravind Krishna Swamy, IIT Delhi, India Arkopal Goswami, IIT Kharagpur, India xi

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Reviewers

Arpan Mehar, NIT Warangal, India Arpita Saha, VNIT, India Arpita Suchismita, IIT Bombay, India Arun Rajkumar, IIT Madras, India Asha Latha, College of Engineering Trivandrum, India Ashish Dhamaniya, NIT Surat, India Ashish Verma, IISc Banglore, India Ashok Julaganti, NIT Warangal, India Ashutosh Arun, Queensland University of Technology, Brisbane, Australia Avijit Maji, IIT Bombay, India Avinash Chaudhari, Government Engineering College Daman, India Ayyanna Habal, PDEU Gandhinagar, India B. Raghuram Kadali, NIT Warangal, India B. K. Bhavathrathan, IIT Palakkad, India Babak Mehran, University of Manitoba, Winnipeg, Canada Bachu Anil Kumar, IIT Patna, India Balaji Ponnu, The Ohio State University, USA Bandhan Majumdar, BITS Pilani Hyderabad, India Bharat Pathivada, IIT Bombai, India Bharat Rajan, IIT Bombai, India Bhargava Chilukuri, IIT Madras, India Bhaswati Bora, IIT Kanpur, India Bhupendra Singh, IIT Jalandhar, India Binanda Khungur Narzary, Tezpur University Assam, India Bishwajit Bhattacharjee, IIT Delhi, India Bivina G R, MANIT Bhopal, India Burhan Showkat, NIT Surathkal, India Darshana Othayoth, NIT Trichy, India Debapratim Pandit, IIT Kharagpur, India Debashish Roy, NIT Sikkim, India Debasis Basu, IIT Bhubaneswar, India Deepa L, IISC Banglore, India Digvijay Pawar, IIT Hyderabad, India Dipanjan Mukherjee, IIT Kharagpur, India Emanuele Renzi, Ministry of Sustainable Infrastructure and Mobility, Italy Fabio Croccolo, Ministry of Sustainable Infrastructure and Mobility, Italy Gaurang Joshi, SVNIT Surat, India Gavadakatla Vamsikrishna, IIT Bombay, India Gopal Patil, IIT Bombay, India Gottumukkala Bharath, CSIR-CRRI New Delhi, India Gourab Sil, IIT Indore, India Goutham Sarang, VIT Chennai, India Gowri Asaithambi, IIT Tirupati, India Gunasekaran Karuppanan, Anna University, Chennai, India Hareshkumar Golakiya, Govt. Engineering College Surat, India

Reviewers

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Hari Krishna Gaddam, National Rail and Transportation Institute, India Harikrishna Madhavan, NIT Calicut, India Harish Puppala, BML Munjal University Haryana, India Harsha Vajjarapu, iCED CAG Jaipur, India Hemanth Kumar, IIT Bombay, India Hyuk-Jae Roh, University of Regina, Canada Indrajit Ghosh, IIT Roorkee, India Ipsita Banerjee, University of California, Berkeley Jaikishan Damani, IIT Bombay, India Jakkula Nataraju, CSIR-CRRI Delhi, India Jaydip Goyani, SVNIT Surat, India Jiten Shah, IITRAM Ahmedabad, India Jithin Raj, IIT Bombay, India Kalaanidhi Sivagnanasundaram, Technion-Isreal Institute of Technology, Isreal Kinjal Bhattacharyya, LICIT-ECO7, Université Gustave Eiffel, France Kirti Mahajan, IIT Bombay, India Kranthi Kuna, IIT Kharagpur, India Kshama Puntambekar, SPA, Bhopal, India Lekhaz Devulapalli, Technion-Isreal Institute of Technology, Isreal M. N. Sharath, IIT Bombay, India M. Sivakumar, NIT Calicut, India M. R. Nivitha, PSG, Coimbatore, India Madhu Errampalli, CRRI, NewDelhi, India Madhumita Paul, IIT Kharagpur, India Madhuri Kashyap N R, IIT Thirupathi, India Malavika Jayakumar, IIT Bombay, India Mallikarjuna Chunchu, IIT Guwahati, India Manoj Malayath, IIT Delhi, India Manoranjan Parida, IIT Roorkee, India Marisamynathan S, NIT Trichy, India Minal Minal, CRRI, Delhi, India Mithun Mohan, NITK, Surathkal, India Mohan Rao, CRRI, Delhi, India Monalisa Patra, IIT Bombay, India Mounisai Siddartha Middela, IIT Madras, India Mukti Advani, CSIR-CRRI, India Munavar Fairooz Cheranchery, TKM College of Engineering, India Muthulingam Subramaniyan, IIT Ropar, India Naga Siva Pavani Peraka, IIT Tirupati, India Nagendra Velaga, IIT Bombay, India Nandan Dawda, SVNIT Surat, India Narayana Raju, TU Delft, Netherlands Naveen Kumar Chikkakrishna, VNRVJIET Hyderabad, India Nikhil Bugalia, IIT Madras, India Nikhil Saboo, IIT Roorkee, India

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Reviewers

Ninad Gore, Ryerson University, Toronto, Canada Nipjyoti Bharadwaj, IIT Guwahati, India Nishant Pawar, IIT Bombay, India Nivedya M K, Worcester Polytechnic Institute, USA Nur Izzi Md. Yusoff, Universiti Kebangsaan, Malaysia Padma Seetharaman, CRRI, Delhi, India Pallav Kumar, MIT Muzaffarpur, India Partha Pratim Dey, IIT Bhubaneswar, India Parthan K, BMS, Bangalore, India Phani Kumar Chintakayala, University of Leeds, England Phani Kumar Patnala, University of Manitoba, Canada Pooja Raj, Gopalan College of Engineering and Management, India Prabin Ashish, IIT Kanpur, India Pranamesh Chakraborty, IIT Kanpur, India Prasanta Sahu, BITS Pilani, Hyderabad Campus, India Prateek Bansal, National University of Singapore, Singapore Pravin Telang, IIT Bombay, India Pritikana Das, MANIT Bhopal, India Priyansh Singh, IIT Palakkad, India Pushpa Choudhary, IIT Roorkee, India R Sivanandan, IIT Madras, India Rahul T M, IIT Ropar, India Rajan Choudhary, IIT Guwahati, India Rajat Rastogi, IIT Roorkee, India Rajeev Mishra, Delhi Technological University, India Rajiv Kumar, NIT Jalandhar, India Ramachandra Rao K, IIT Delhi, India Ramesh Anbanandam, IIT Roorkee, India Ramya Sri Mullapudi, IIT Hyderabad, India Ranja Bandyopadhyaya, NIT Patna, India Ranju Mohan, IIT Jodhpur, India Raunak Mishra, University of North Carolina, USA Ravi Sekhar Chalumuri, CRRI, New Delhi, India Ravindra Kumar, CRRI, New Delhi, India Reema Bera, IIT Kharagpur, India Remya K P, IIT Bombay, India Rupali Zope, COEP, Pune, India Rushikesh Amrutsamanvar, TU Dresden, Germany S. P. Atul Narayan, IIT Madras, India S.Vasantha Kumar, VIT,Vellore, India Sadguna Nuli, Vardhaman College of Engineering, Hyderabad, India Saladi Sv Subbarao, Mahindra University Hyderabad, India Sandeepan Roy, IIT Bombay, India Sanhita Das, IIT Roorkee, India Sanjay Dave, MSU Baroda, India

Reviewers

Sarah Mariam, BITS Pilani, India Sarfaraz Ahmed, NUST, Pakistan Shahana A, IIT Bombay, India Shankar Sabavath, NIT Warangal, India Shashi Bhushan Girimath, IIT Bombay, India Shobhit Saxena, IISc Banglore, India Shriniwas Arkatkar, SVNIT Surat, India Shubhajit Sadhukhan, IIT Roorkee, India Siddharth S M P, IIT Bombay, India Siddhartha Rokade, MANIT, Bhopal, India Siksha Swaroopa Kar, CSIR-CRRI, India Sita Rami Reddy, VNIT, Nagpur, India Smruti Mohapatra, ISM Dhanbad, India Sonu Mathew, University of North Carolina at Charlotte, USA Sridhar Raju, BITS Pilani, Hyderabad Campus, India Srinath Mahesh, IIT Madras, India Srinivas Geedipally, Texas A&M Transportation Institute, USA Subhojit Roy, IIT Bhubaneswar, India Sudhakar Reddy Kusam, IIT Kharagpur, India Sudhir Varma, IIT Patna, India Sunitha K Nayar, IIT Palakkad, India Sunitha V, NIT Trichy, India Suresha S. N., NIT Suratkal, India Sushma Mb, IIT Bombay, India Suvin P. Venthuruthiyil, IIT Guwahati, India Swati Maitra, IIT Kharagpur, India Tarun Rambha, IISc Bangalore, India Tushar Choudhari, IIT Bombay, India Udit Jain, VNIT Nagpur, India Uma M. Arepalli, SRM University AP, Andhra Pradesh, India Umesh Sahoo, IIT Bhubaneswar, India Vasudevan N, SV NIT, Surat, India Vedagiri Perumal, IIT Bombay, India Veena Venudharan, IIT Palakkad, India Velmurugan S, CRRI, India Venkaiah Chowdary, NIT Warangal, India Venkatesan Kanagaraj, IIT Kanpur, India Vinamra Mishra, IIT Bombay, India Vinayaka Ram V, BITS Pilani-Hyderabad, India Vinayaraj V S, IIT Bombay, India Vishwas Sawant, IIT roorkee, India Vivek R Das, Ramaiah Institute of Technology, India Yawar Ali, S V NIT, Surat, India Yogesh Shah, IITRAM Ahmedabad, India

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Contents

Pavements and Materials Evaluation of Resilient Modulus of the Subgrade and Granular Layer—A FWD-Based Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alok Singh, Ashish Walia, and Rajat Rastogi

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Prediction of Properties of Asphalt Emulsion Residue Using Maturity Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. L. Anjali and Aravind Krishna Swamy

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Quantification of Aging of Polymer Modified Binder Using Creep Recovery and Yield Energy Test Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arpita Suchismita and Dharamveer Singh

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Effect of Short-Term Ageing on Mechanical Characteristics of Modified Bituminous Binders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Muhammed Rinshad, M. Sivakumar, and M. V. L. R. Anjaneyulu

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Systematic Approach to Optimize Roller-Compacted Concrete Pavements Mixes Through Particle Packing Method . . . . . . . . . . . . . . . . . . M. Selvam, Rishab Dane, and Surender Singh

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Experimental Investigation on Aging Behavior of Bitumen Mastic with Hydrated Lime Using FTIR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . K. L. A. V. Harnadh, M. R. Nivitha, and A. Padmarekha

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Numerical Analysis of Fiber-Reinforced Whitetopping Pavement Under Wheel Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. V. Jisha, M. Satyakumar, and Remya Valsalan

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Rheological Characterization of Unmodified and Modified Bitumen in the Temperature Range of 40–70 °C . . . . . . . . . . . . . . . . . . . . . . 101 T. Srikanth and A. Padmarekha

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Contents

Fatigue Damage Criteria for Bitumen Based on the Evolution of Lissajous Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 M. Jayaraman and A. Padmarekha Moisture Damage Prediction of Hot Mix Asphalt Using Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 A. Jegan Bharath Kumar, Mohit Singh Parihar, P. Murshida, V. Sunitha, and Samson Mathew Automatic Recognition of Road Cracks Using Sobel Components in Digital Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Deeksha Arya, Sanjay Kumar Ghosh, and Durga Toshniwal Transport Planning and Mobility Networks Ranking of Accident Blackspots Based on Economic Value Using Willingness to Pay Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Sivakumar Balakrishnan and Krishnamurthy Karuppanagounder Determinants of Users’ Perception of Fixed Route Paratransit Service Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Debapratim Pandit and Deepa Sharma Road Network Analysis of Major Destinations in Guwahati City Using GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Mayurakshi Hazarika and Amit Kumar Yadav Probabilistic Approach for the Evaluation of Two-Lane Two-Way Rural Highways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 P. Muhammed Swalih, M. Sangeetha, M. Harikrishna, and M. V. L. R. Anjaneyulu An Analysis of the Trip Attraction Pattern for an Urban Area in a Developing Country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 A. Nanditha, V. S. Sanjay Kumar, K. Athiappan, and Shabana Yoonus Location Choice Analysis for Transit Oriented Development (TOD): A Case Study of Kolkata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Kuldeep Kavta, B. S. Manoj, Nitin Srivastava, Arkopal K. Goswami, and Shigehisa Matsumara Assessment of Ecosystem for Sustainable PPP in City Bus System . . . . . . 261 Laghu Parashar, Sanjay Gupta, and Amruta Kulkarni Importance of Travellers’ Preferences in Planning Congestion Pricing Levels and Benefits: Case Study of Urban Corridor in Kolkata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Ankit Kumar Kushwaha, Anuj Kishor Budhkar, and Sudip Kumar Roy

Contents

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Evaluation of Bus Signal Priority and Dedicated Bus Lane for Efficiency Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 V. L. Baalaganapathy, Anagha Girijan, Lelitha Devi Vanajakshi, and Bhargava Rama Chilukuri A Full–Day Intercity Bus Frequency Setting Model Considering Dedicated Fleet Size and User Perception Using Genetic Algorithm . . . . 311 Ashish Jaiswal and Debapratim Pandit Potential of Electronic Ticketing Machine Data in Public Transport Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Susan Francis, V. Sunitha, and Samson Mathew Departure Time Planner for Multimodal Public Transport Network Using Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Mihir Kulkarni, Arshinder Kaur, and Lelitha Vanajakshi Transit Ridership—Influencing Factors and Usage for Environmental Impact Assessment: A Case of Bengaluru Metro . . . . 359 Vivek V. Gavimath and Srinath Mahesh

About the Editors

Dr. Lelitha Devi is a Professor in the Transportation Division of the Department of Civil Engineering at Indian Institute of Technology (IIT) Madras and holds a Ph.D. from Texas A&M University, USA. Her teaching and research interests are in the general area of transportation systems with emphasis on traffic flow modelling, traffic operations, and Intelligent Transportation systems. She has published around 90 referred journal papers and more than 100 conference papers. She serves in various editorial boards and is a member of various national and international societies. She has been involved in 15 research projects and has graduated more than 10 MS/Ph.D. scholars. Animesh Das Ph.D., is a Professor in the Department of Civil Engineering at Indian Institute of Technology (IIT) Kanpur. His area of interest is pavement material characterization, analysis, design and evaluation. His notable works include two books: Principles of Transportation Engineering and Analysis of Pavement Structures. He has several publications to his credit. He is a recipient of Fulbright-Nehru Senior Research Fellowship. Prasanta Kumar Sahu is an Assistant Professor in the Department of Civil Engineering, Birla Institute of Technology and Science (BITS) Pilani at Hyderabad, India. He holds a Ph.D. in Transportation Systems Engineering (TSE) from Indian Institute of Technology (IIT) Bombay. Dr. Sahu is an Adjunct Professor in Price Faculty of Engineering at University of Manitoba, Canada. Dr. Sahu has been a pioneering contributor to freight transportation planning and policy in India with numerous highquality publications aiming to improve the logistic efficiency of goods movement. He is a life member of Institute of Urban Transport (IUT) India and Transportation Research Group (TRG) India and Indian Road Congress (IRC). Additionally, he is a visiting faculty to University of Regina, Regina, Canada and Cardiff University, Cardiff, Wales, UK. Dr. Sahu has published more than 50 journal publications and more than 60 conference papers. He has been involved in 10 research projects and has graduated more than 15 MS/Ph.D. scholars.

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About the Editors

Debasis Basu is currently a Faculty in Transportation Engineering at School of Infrastructure, Indian Institute of Technology (IIT) Bhubaneswar, India. Prior to this, Dr. Basu was a Post doctoral research fellow at University of California at Davis, USA. He works in the domain of sustainable transport planning, assessment of transport infrastructure, economic evaluation of alternative transport projects, non-motorized transport, etc. Dr. Basu is currently involved in various prestigious research projects like IMPRINT, Future of Cities etc. He has numerous publications in various peer reviewed journals and conferences. He has also acted as reviewer of various international peer-reviewed journals and major conference proceedings.

Pavements and Materials

Evaluation of Resilient Modulus of the Subgrade and Granular Layer—A FWD-Based Analysis Alok Singh, Ashish Walia, and Rajat Rastogi

Abstract Resilient modulus of the layers is a key engineering property which is used in the design of the new pavement, as well as, while evaluating the existing pavement for possible rehabili-tation. These are based on IRC 37:2018 and IRC 115:2015 in India. As per IRC guidelines, the resilient modulus of the subgrade is evaluated using an empirical correlation with its CBR value, and for the base course, it uses an empirical re-lationship with the thickness of the granular layer and the resilient modulus of the subgrade. According to global prac-tice, this is a Level-2 design approach. Level-1 approach considers the resilient modulus based on the laboratory test for analysis. At present, the quality control during the con-struction stage is based on the examination of the density of a layer. In the absence of any standard relationship between density and resilient modulus, this approach does not guar-antee that the layer modulus is achieved. If this is done at this stage, then it will allow the engineer to take corrective measures at that time itself thus ensuring the functionality during design life. This paper attempts to use FWD-based analysis for subgrade and granular layer for proposing its possible use for construction stage quality assurance. The re-sults look promising and indicate that use of FWD is possi-ble with certain modifications in the loading mechanism. Keywords FWD · Resilient modulus · Pavement · Subgrade · Granular layer

1 Introduction Resilient modulus is an important input parameter in the mechanistic-empirical design of pavements. As per the existing approach [12], the resilient modulus is correlated to CBR of subgrade soil and the thickness of the layer, being considered. After the design, the CBR does not play any role in pavement quality construction. During the construction stage, the density of a compacted layer is used as a quality parameter. However, in the absence of a relationship between density and resilient A. Singh (B) · A. Walia · R. Rastogi Transport Reseasch Group of India, Bengaluru 560012, India e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_1

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modulus, assuring quality is difficult. Density is a property which is correlated with a number of engineering properties of the soil, but it is not a fundamental input in the design of the pavement. Particle arrangement of the soil may vary considerably without significant change in the dry density [10], which may result in different soil behaviors. Hence, the use of density alone is not a correct approach to ascertain quality. This also causes a mismatch between design guidelines and quality assurance guidelines. Hence, there is a need to develop the quality related guidelines, which are based on resilient modulus of the layers. The work presented here examined the possible use of falling weight deflectometer (FWD) to estimate the resilient modulus of the subgrade and granular layer in the field, as a part of the road construction and quality assurance. This will bring in the quality control in sync with the design approach being followed as per IRC 37:2018. It will be a direct in situ testing and evaluation which will provide an opportunity of taking corrective measures to ensure quality before laying of the next layer. It is expected that by ensuring the design parameters at the early stage itself the possibility of assuring the quality throughout the design life will get enhanced by many folds. This will also be beneficial monetarily as, at times, it happens that the deficiency in lower layers gets translated into distresses in the early age of the pavement and requires a higher level of periodical maintenance. FWD is an equipment that works based on the impulse loading and deflection caused due to it in the constituent layers of the pavement. This deflection is used to back-calculate the resilient modulus of the constituent layers. According to IRC 115:2015, the use of FWD has been suggested for examining the condition of the existing pavement and estimating the remaining life in it. This means it is used at the top of surface layer. The equipment is quite costly and that restricts its usage. This work is based on the premise that the regional availability and usage of FWD, along with minimal requirement of periodic maintenance will offset the initial costly evaluation. In the long term, it will be economical if the cost comparisons are spread over the pavement’s design life. In light of the above, the works done by different researchers are discussed in the following section.

2 Application Studies Using FWD In the Mechanistic-Empirical Pavement Design Guidelines (MEPDG, under NCHRP 1-37A, 2004), the key design inputs used are traffic volumes, axle loads, and material characteristics. Resilient modulus is the key characteristic property that governs the deflection in the layers. The resilient modulus of soil subgrade is used as an input to evaluate the deformation and stresses in the subsequent layers of the pavement. If incorrect values of resilient modulus are used, then it may lead to premature failure of the pavement or may lead to overestimation/underestimation, which may not be monetarily economical. MEPDG process requires the resilient modulus of the

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bituminous layer, granular layer, and subgrade as an input in the design. For resilient modulus of subgrade soil, the methods use: (a) (b)

Correlation with soil strength parameters such as UCS value and CBR value. Empirical relationship with soil properties such as grain size, grain angularity, Atterberg limits, and moisture content.

For unbound granular base layers, these are based on the level of design [20] as mentioned below: (a) (b) (c)

Level-1: Requires laboratory testing and non-destructive deflection testing using FWD for reconstruction/rehabilitation purposes. Level-2: Uses correlations that allow the use of material indices, strength parameters, etc. Level-3: Uses default values of resilient modulus recommended by design guidelines such as AASTHO.

For subbase layer, the Level-2 design is done using the Uzan [25] model, also mentioned in Ji et al. [17], and given as Eq. 1. Similarly, for the subgrade, the popular deviatoric stress model is discussed by Mossazadeh and Witczak [19] and Witczak et al. [24]. This is given as Eq. (2). The prediction accuracy of these models is dependent upon the selection of model parameters related to materials used. Various correlations developed using soil strength parameters are given in Table 1. (1) MR = k4 ∗ σdk5

(2)

where M R = resilient modulus; 8 = bulk stress; σ d = deviatoric stress; P = atmospheric pressure; ş = octahedral shear stress; and k 1 , k 2 , k 3 , k 4 , k 5 are regression coefficients. IRC 37:2018 uses the correlation with the CBR of subgrade to calculate the resilient modulus (MR ) of the granular layer (having thickness ‘h’ mm). This is given in Eqs. 3, 4, and 5. MR(Graular Layer) = 0.2 ∗ h 0.45 ∗ MR(Subgrade)

(3)

MR(Subgrade) = 10 ∗ CBR for CBR ≤ 5%

(4)

MR(Subgrade) = 17.6 ∗ CBR0.64 for CBR > 5%

(5)

It is also possible to estimate the resilient modulus through laboratory testing. The comparison of laboratory and back-calculated moduli suggests that the ratio between the laboratory-tested resilient modulus and back-calculated modulus depends upon the degree of variability between the site and laboratory conditions. AASTHO Design

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Table 1 Resilient modulus relationships with material properties Strength/index properties

Mode

Comments

Standard test

CBR

Mr = 2555(CBR)0.64 M r in psi

CBR = California Bearing Ratio (percent)

AASHTO T193 ‘The California bearing ratio’

R-value

M r = 1155 + 555R Mr in psi

R = R-value

AASHTO T190 resistance R-value and expansion

AASHTO Layer coefficient

+0.977 M r = 10* a20.249 M r in psi

a2 = AASHTO layer coefficient for base layer

AASTHO guide for design of pavement structures, 1993

PI and gradation

CBR =

PI = Plasticity index P200 = Percent passing the no. 200 sieve size

AASHTO T27 ‘sieve analysis of coarse and fine aggregates’ AASHTO T90 ‘determining the plastic limit and plasticity index of soils”

DCP is dynamic cone penetration index mm/blow

ASTM D6951 ‘standard test method for use of dynamic cone penetrometer in shallow pavement applications’

75 1+0.728(P200 )∗PI

DCP

CBR =

75 DCP1.12

Guide [1, 2] suggests that back-calculated moduli are approximately three times of the laboratory-calculated moduli. Ji et al. (2014) recommended this value as four times. Depending upon the test conditions, these are found varying between 1.6 and 5. Good resemblance was observed when the test conditions were reproduced [7]. The variations were observed due to the change in the moisture and temperature conditions. When these were controlled to simulate field condition, then the ratio was observed to be 1 [23]. Ng et al. [21] recommended to systemize the back-calculation process. It included applying temperature correction for sensor deflection reading and realistic selection of seed modulus based on local material. Indian practice is to use KGP-BACK software in association with guidelines given in IRC 115:2015. Apart from using FWD on the topmost asphalt layer/concrete layer, some researchers have used the equipment to evaluate other layers also. In Poland, FWD was used on the subgrade directly by Fengier et al. [8] to find out its secondary deformation modulus (Ev2 ). For the subbase layer, impulse load was adjusted to 30 kN, whereas on the Asphalt layer, a load of 50 kN was used. It was found that back-calculated moduli from elastic layer theory and finite element modeling were in an acceptable range of engineering judgment. Bertuliene and Laurinavicius [3] did comparative analysis on modulus of subgrade using LWD (Prima 100), FWD (Dynatest 8000), static beam (Strassen test), and light dynamic device (Zorn ZSG 02). Various relationships were used, and these are given in Table 2.

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Table 2 Summary of relationships for the deformation modulus Name of the Device

Relationships

Parameters

Static beam (press)

E v = 1.5 *r* σ /s

E v is the deformation modulus, r is radius of the plate, σ is the change in stress, and s is the soil deformation at the center of the plate

Static Benkelmen beam (BBD)

E v = k*P*D*(1 − μ2 )/I p

k is the load transfer coefficient measured by deflection indicator and wheel (k = 0.85), P is the pressure of wheel on Pavement, D is the reduced wheel path diameter, μ is Poisson’s ratio (μ = 0.3), Ip is the reduced pavement deflection

Light dynamic devices (Zorn ZSG 02)

Evd = 1.5 *r* δ/s

LWD(Prima 100) and FWD (Dynatest 800)

E o = f (1 − μ2 )σ o *r/l

‘r’ is the plate dia, δ is the dynamic load to 0.1Mn/m2 , ‘s’ is the soil deformation under the loading plate, l is the deflection under the plate ‘f’ is the stress distribution ratio (2-even segmented loading plate; /2-rigid plate; 8/3-granular soils rigid plate; 4/3-cohesive soils rigid plate), σ o is the contact stress under the loading plate

Field resilient modulus using the in situ deflection caused by the iMpact devices for a single-layer system can also be evaluated using the Boussinesq half-space equation assuming the material to be linearly elastic, isotropic, and homogenous. Equation 6 can be used for the same   2 ∗ k ∗ 1 − μ2 E= A∗r

(6)

   where E is the modulus of the subgrade; k is the average soil stiffness = Fδ ; F is load applied by the FWD device; δ is maximum deflection measured; r is plate radius; and A is stress distribution ratio, as given in Table 3. Burmister [4, 5] proposed a two-layer theory where a granular layer of thickness ‘h’ is laid over the subgrade. load was applied on a circular plate and deflection Table 3 Stress distribution factors for soils [22]

Soil type

Stress distribution factor

Mixed soil



Granular soil

3/4

Cohesive soil

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was measured under the center of the plate placed on the surface of the top layer. In order to calculate the modulus of the granular layer, Burmister proposed a formula given as Eq. 7. These surface deflections are used as a criterion for pavement design. The deflection factor ‘F’ used in calculating modulus is itself a function of elastic modulus of layers (E1/E2) and ratio of layer thickness and radius of contact area (h/r). It is given by Eq. 8.

F=

⎧ ⎨ ⎩

  2 1 − μ2 P W0 = ∗F ∗r0 1−   1 E2 1+ rh

0

2 + √ 3 E1 E2

(7) 1

( )2

1+ rh 0

E1

⎫ ⎬ ⎭

(8)

In a research report MD-17-SHA-UM-3-20 on standardization of light-weight deflectometer [6], the Maryland Department of Transportation developed charts for the surface to subgrade modulus ratio and base layer to subgrade modulus ratio for different compositions of the pavement. These are presented as Fig. 1. In a two-layer system, once the subgrade modulus is known, the FWD testing on the base layer would yield the surface layer modulus, which can be further used to evaluate the base layer modulus. A pertinent issue while conducting field or laboratory tests is to make sure that stress conditions as experienced by the material under actual road conditions are replicated during the testing process. Since the M R is an elastic property, it exhibits a nonlinear behavior with a change in stress. For subgrade and subbase, the resilient modulus is a function of confining stress and deviatoric stress. For subgrade, the resilient modulus decreases with an increase in deviatoric stress and increases with confining stress, whereas for subbase, the resilient modulus increases with an increase

Fig. 1 Surface modulus chart for compacted base layer of thickness ‘h’ and plate diameter ‘d’

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Fig. 2 Values of M R as a function of σ c and σ d

in confining stress [18] and the effect of deviatoric stress is relatively small. Variation of MR with stresses is depicted in Fig. 2. Based on the inputs taken from different studies, the study was planned which is now discussed in the following section.

3 Study Area and Pavement Profile The study has been conducted on a 1200 m long section of the under-construction bypass on NH-58 at the outskirts of the Roorkee city in the state of Uttarakhand. The study was conducted between chainage 158.7–159.9. The pavement is a four-lane divided highway with a raised median. Under-construction bypass is shown in Fig. 3. The pavement crust consists of the following layers: (a) (b) (c) (d) (e)

Subgrade: 500 mm Granular subbase (GSB): 200 mm Wet Mix Macadam (WMM): 250 mm Dense Bituminous Macadam (DBM): 110 mm Bituminous concrete (BC): 40 mm

Fig. 3 Study stretch on NH-58 Roorkee

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Table 4 Wet sieve analysis of soil samples [14] Weight of sample: 500 gm, Site: Roorkee Bypass Chainage: 158–159 Sieve size

Weight of soil, gm

% Soil retained

Cumulative % of soil retained

Percent passing (%)

425μ

27

5.40

5.40

94.60

212μ

256

51.20

56.60

43.40

150μ

47

9.40

66.00

34.00

75μ

64

12.80

78.80

21.20

Pan

106

21.20

100.00

0.00

4 Subgrade Soil Evaluation 4.1 Grain Size Distribution The sieve analysis results of soil sample procured from the site are given in Table 4. Soil is sandy as more than 50% gets retained between 4.75 mm and 75 microns. Further, as 73.4% of soil is retained between 425 and 75 microns, the soil can be classified as fine sand, as per the Indian Soil Classification System. Clay and silt content in the soil sample is 21%. Hence, the soil is classified as sandy silt (SM) or sandy clay (SC).

4.2 Soil MDD and OMC

MDD, kg/m3

The results of Proctor density test are shown in Fig. 4. The OMC and MDD of the borrow soil sample were determined as 11.2% and 1757 kg/m3 , respectively. 1.77 1.76 1.75 1.74 1.73 1.72 1.71 1.7 1.69 1.68 6%

y = 282.58x3 - 137.6x2 + 20.133x + 0.8307 8%

10% 12% 14% Moisture Content, %

Fig. 4 Soil MDD and OMC curve ([15] (Reaffirmed 2002))

16%

18%

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Table 5 Soaked CBR values for soil samples [16] (Reaffirmed 1997) Soil sample

Load, kg

CBR

Resilient modulus, MPa

2.5 mm

5.0 mm

2.5 mm

5.0 mm

2.5 mm

5.0 mm

1

116.6

311.04

8.5

15.1

69.24

100

2

129.6

304.5

9.4

14.8

73.84

98.1

3

116.6

304.5

8.5

14.8

69.24

98.1

Note Standard load for 2.5 mm penetration = 1370 kg; Standard load for 5.0 mm penetration = 2055 kg

4.3 CBR and Resilient Modulus Three soil samples were taken from the burrow area, and CBR test was conducted under soaked condition. Resilient modulus was then calculated using the CBR value as per IRC 37:2018. The results are given in Table 5. CBR of the soil was found to be around 15 at 5.00 mm penetration. The related resilient modulus was estimated as around 99 MPa. At a penetration of 2.5 mm, the CBR was estimated around 9 with related resilient modulus as 71 MPa. The design CBR, as informed by the field engineer and considered in the pavement design, is 8.9% which results in MR value as 71 MPa. Based on the data, it can be assumed that the resilient modulus of soil subgrade should be minimum 70 MPa.

4.4 Resilient Modulus Using FWD FWD test was conducted on the subgrade after its compaction to achieve 97% MDD. According to IRC 37:2018, the standard wheel load of 40kN was considered at the top of the pavement. This was reduced to simulate the load on the top of subgrade using Boussinesq stress distribution chart. The stress at subgrade level comes out to be 8–10% of the stress on the top layer (bituminous) which translates to 3.2–4 kN. Considering a three-layer system and using IIT-Pave software, the stress on top of the subgrade comes out to be 2% of the stress measured at the top of the pavement. Considering the limitation of load reduction in the FWD equipment, the drop height of the load train was set as 20 mm. This reduced the load on the subgrade to 10 kN. The surface, on which loading plate rests, was made even. Data were collected at 24 locations along length and width of the section. The test arrangement is shown in Fig. 5. One set of data consisted of three drops of impulse load at the same location. Deflection was measured at all the seven sensors which were placed at a radial distance of 0 mm, 300 mm, 600 mm, 900 mm, 1200 mm, 1500 mm, and 1800 mm from the point of loading. These measured deflections are plotted as shown in Fig. 6 using average values of load and deflections. Next, Boussinesq half-space continuum theory was used to calculate the resilient moduli value of the subgrade using Eq. 6

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Fig. 5 FWD testing on the subgrade at Roorkee bypass

0

0

1

2

FWD Sensors 3 4

5

6

7

50 100 150 200

Deflecon (μm)

250 300 350 400 450 500 550 600 650 700

Tes Loc 1 :11.9kN Test Loc 2:12.1kN Test Loc 3:12.5kN Test Loc 4:11.4kN Test Loc 5:12.1 kN Test Loc 6 :10.4kn Tes Loc 7: 12.9kN Test Loc 8 :14.3 kN Tes Loc 9: 11.9kN Test Loc 10 : 12.2kN Test Loc 11: 12.5kN Test Loc 12: 10.4kN Test Loc 13: 12.1kN Test Loc 14: 11.5 kN Test Loc 16: 11.9 kN Test Loc 15: 12.8 kN Test Loc 17: 14.3 kN Test Loc 18: 12.6 kN Test Loc 19: 12.0 kN Test Loc 20: 11.8kN Test Loc 21: 12.2kN Test Loc 22: 12.0kN Test Loc 23: 12.5kN Test Loc 24: 11.9kN

750

Fig. 6 Deflection bowl of FWD tests on the subgrade

considering the stress distribution factor of . The resilient modulus value calculated for different test locations is shown in Fig. 7. These are found varying between 60 and 99 MPa.

Resilient Moduli Subgrade (MPa)

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120 100 80 60 40 20 0

0

5

10

15

20

25

30

Test Locations Fig. 7 Resilient modulus of the subgrade using the FWD

Table 6 Resilient modulus calculated as per IRC37:2018

Soil sample

CBR 2.5 mm

MR subgrade (MPa)

MR granular (MPa)

1

8.5

69.24

216.43

2

9.4

73.84

230.81

3

8.5

69.24

216.43

5 Granular Layer Evaluation 5.1 Resilient Modulus as Per IRC Guidelines As per IRC 37:2018, the granular layers are considered as one layer. Hence, the thickness of granular layer along section is 450 mm. Using Eq. 3, the resilient modulus of the granular layer is calculated, which is given in Table 6. The use of resilient modulus of subgrade at 5.00 mm penetration CBR value results in a value of 310 MPa.

5.2 Resilient Modulus Using FWD Before conducting the FWD test, the impulse load was set to cause stress equivalent to reduced load compared to 40kN applied at the top of surface layer. Setting Impulse Load The reduction in stress at the top of granular layer will depend upon the ratio of elastic modulus of the two layers in contact and the thickness of the overlying layer. This reduction in stress on the granular layer has been calculated using single-layer and two-layer elastic theory.

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Stress Reduction Based on Homogenous Single-layer Elastic Theory Stress reduction factor is found from the vertical stress distribution curves provided by Foster and Ahlvin [9] for Poisson’s ratio of 0.50. Considering the depth of overlaying layer (z) as 150 mm, the radius of loading plate (a) as 150 mm, the ratio z/a as 1.0, and ratio r/a as zero, the stress reduction factor comes out to be 60%. This results in a load of 24 kN. However, in practice, this would depend up on the material and thickness of overlaid layer. Stress Reduction Based on Two-layer Elastic Theory In order to obtain more realistic stress reduction, two-layer elastic theory [5] was used. However, limitation of this theory is that it can only be used for pavements where the thickness of the top layer is equal to the radius of the loading plate. Using this theory, and considering E1/E2 as ranging between 5 and 10, the stress reduction factor at the interface of the layers comes out to be 40–30%. This translates into load ranging between 16 and 12 kN [11]. Stress Reduction Factor Using IIT-PAVE Software Considering the maximum elastic modulus of bituminous layer laid with VG30 or VG40 as 2000 MPa or 3000 MPa, respectively, wheel load as 40 kN, dual wheel per axle, and Poisson’s ratio as 0.35, the vertical compressive stress at the top of granular layer comes out to be 0.11 MPa and 0.08 MPa for the two types of mixes. Based on the constant area of loading plate, the load to be applied comes out to be 7.77 kN or 5.65 kN. Based on the above-mentioned concepts, finally the impulse load to be applied was varied between 12 and 21 kN. The loads were varied to see the behavior of resilient modulus with respect to change in deviatoric stress. Estimation of Resilient Modulus of the Granular Layer FWD test was conducted on the prepared granular WMM layer with an impulse load varying between 12 and 21 kN (30–50% of stress at the top surface). This is shown in Fig. 8. The average deflections measured at different sensor locations are shown in Fig. 9. The deflection factor(F) was calculated using Eq. 7 for each test location, and based on this, E1 (granular layer) was utilized using Eq. 8. The subgrade modulus E2 has been taken as 70 MPa for the calculation. A look at the deflection bowl and the calculated resilient moduli value for different test locations indicates that the deflection has reduced after laying a granular layer with higher moduli as compared to the subgrade. That shows an increase in the structural value of the constructed pavement. The resilient moduli of granular layer is found varying between 352 and 427 MPa, with an average value falling around 398 MPa and 15th percentile as 371 MPa (in accordance to IRC 115:2015). It is observed that that resilient modulus of the granular layer does not change much with an increase in the deviatoric stress. This finding is in line with the reported literature. It happens due the greater interlocking between the aggregates with an increase in the deviatoric stress (Fig. 10).

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Fig. 8 FWD testing on WMM layer at Roorkee bypass

FWD Sensors 0

0

2

4

50

Deflection (µm)

100 150 200 250 300 350

6

8

Test Loc 1:18.9kN Test Loc 2:16.6kN Test Loc 3:13.4kN Test Loc4:17.3 kN Test Loc 5 :20.7kn Tes Loc 6: 11.8kN Test Loc 7 :16.1 kN Tes Loc 8: 13.35kN Test Loc 9 : 16.2kN Test Loc 10: 13.1kN Test Loc 11: 18.3kN Test Loc 12: 21.1kN Test Loc 13: 14.0 kN Test Loc 14: 17.3 kN Test Loc 15: 18.7 kN Test Loc 16: 13.4 kN Test Loc 17: 13.3 kN Test Loc 18: 12.2kN Test Loc 19: 15.5kN Test Loc 20: 14.2kN Test Loc 21: 17.3kN Test Loc 22: 18.9kN Test Loc 23: 13.4kN Tes Loc 24 :15.6kN

Fig. 9 Deflection bowl of FWD tests on the granular layer

The deflections of the two layer as whole were utilized to estimate the surface modulus (using Eq. 5). The stress distribution ratio of  and the Poisson ratio of 0.35 has been selected. These are shown in Fig. 11. The values of the surface modulus of the combined layers are found varying between 215 and 240 MPa. The resilient

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Resilient Modulus MPa

450

400

350

300

0

5

10

15

20

25

30

20

25

30

Locaon Fig. 10 Resilient modulus of the granular layer

Surface Moduli (MPa)

300 250 200 150 100

0

5

10

15

Test Locations Fig. 11 Surface modulus of the granular and subgrade layer together

moduli of the granular layer as calculated using FWD [13] is found to be 67% higher than the moduli calculated as per IRC 37:2018 procedure. Using these values, the ratio of resilient modulus of granular layer and subgrade modulus, i.e., E1/E2, comes out to be 5.3. It should be understood that the resilient modulus of the purely granular layer will be higher than the surface modulus; however, values calculated using IRC method are found to be lower and similar to the surface modulus.

6 Applicability for Modulus-Based Quality Control Presently, FWD has been used for pavement distress evaluation and overlay design. Its use during construction phase for quality assurance has not been attempted. This creates a deviation as the design of the pavement is done using resilient modulus approach. FWD has been used for the evaluation of subgrade and granular layer, as is clear from the reported literature. There is a need to create resilient modulusbased quality assurance process and standards so that quality can be ensured during construction and results in the useful design life of the pavement. However, there

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exist numerous challenges while applying FWD-based approach. These relate to stress states and boundary conditions existing in the field vs. laboratory examination. The analysis carried out clearly indicates that quality lapses during construction may happen even if the density has been found to be within 98% of the design value. Considering a lower design value of resilient modulus for subgrade as 70 MPa, it is observed from the data that there are eight locations that need corrective measures before start laying granular layers. Further, considering 90% of resilient modulus for granular layer as acceptable, it is observed that there are five locations which need remedial measures. Four locations are found to have issues in both layers. The deflection bowl indicates that FWD can be used with five sensors instead of seven sensors, both for subgrade and for granular layer evaluation. The stresses for the subgrade layer can be kept around 20–25% of the stress that may occur at the top of the pavement surface layer, whereas for WMM layer, it can be fixed between 50 and 30%. The resulting deflections in subgrade are observed to be 2.2 times the deflections caused when the load is applied on the top of the granular layer. This indicates that a good-quality granular layer will reduce the resulting deflections due to same loading by a large extent. The use of deflection values in calculating the modulus resulted in two sets, one as resilient modulus and another as surface modulus. It was observed that modulus values calculated based on IRC guidelines for a granular layer are actually surface modulus. This surface modulus is 0.59 times the resilient modulus. This indicates that IRC guidelines provide modulus values toward the conservative side, which results in a lot of overdesign. Over the years, the pavement design has graduated from empirical design to mechanistic design which is based on resilient modulus approach. Therefore, the need is to shift to quality assurance which is also modulus based. This would have latent benefits as it would help in improving the performance of the pavement over its design life with much-reduced maintenance requirements over the design period. The use of FWD would bring in more efficiency as longer distance along road section per unit time can be measured to arrive at the QA and maintenance requirements. The results of this study are encouraging in this direction and suggest that FWD can be used for the evaluation of subgrade and the granular layers during construction period.

References 1. AASHTO (1986) Guide for design of pavement structures. American Association of State Highway and Transportation Officials, Washington, DC, USA 2. AASHTO (1993) Guide for design of pavement structures. American Association of State Highway and Transportation Officials, Washington, DC, USA 3. Bertuliene L, Laurinavicius A (2008) Research and evaluation methods for determining the modulus of road subgrade and frost blanket course. Baltic J Road Bridge Eng 3(2) 4. Burmister DM (1943) The theory of stresses and displacements in layered systems an d applications to the design of airport runways. Proc Highway Res Board 23:126–144 5. Burmister DM (1958) Evaluation of pavement systems of the WASHO road test by layered systems method. Bulletin Highway Res Board 177:26–54

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6. Charles WS, Afsharikia Z, Khosravifar S (2017) Standardizing lightweight deflectometer modulus measurements for coMpaction quality assurance. Maryland Department of Tranportation State Highway Administration, Report no MD-17-SHA-UM-3-20 Sept 2017 7. Dawson T, Baladi G, Sessions C, Haider S (2009) Back-calculated and laboratory-measured resilient modulus values. Transp Res Rec 2094:71–78. https://doi.org/10.3141/2094-08 8. Fengier J, Pozarycki A, Garbowski T (2013) Stiff-plate bearing test simulation based on FWD results. Proc Eng 57:270–277. https://doi.org/10.1016/j.proeng.2013.04.037 9. Foster and Ahlvin (1954 Monograph) Monograph title: proceedings of the thirty-third annual meeting of the highway research board, Washington, DC, 12–15 Jan 1954 10. Hveem FN, Carmany RM (1949) The factors underlying the rational design of pavements. In: Highway research board proceedings, vol 28 11. IIT-Pave Software for layered elastic analysis of the pavement developed by Indian Institute of Technology Kharagpur, India 12. IRC:37-2018 Tentative guidelines for the design of flexible pavements. Indian Roads Congress, New Delhi, India 13. IRC:115-2014 Guidelines for structural evaluation and strengthening of flexible road pavements using falling weight deflectometer (FWD) technique. Indian Roads Congress, New Delhi, India 14. IS:2720 Part 4, 1985 (Reaffirmed 1995). Method of test for soils. Grain size analysis 15. IS:2720 Part 7, 1980 (Reaffirmed 2002). Method for test of soils. Determination of water content and dry density relation by heavy coMpaction 16. IS:2720 Part 16, 1987 (Reaffirmed 1997). Method for test of soils. Laboratory Determination of CBR 17. Ji R, Siddiki N, Nantung T, Kim D (2014) Evaluation of resilient modulus of subgrade and base materials in indiana and its implementation in MEPDG. Sci World J 2014. https://doi.org/ 10.1155/2014/372838 18. Lee J, Kim J, Kang B (2009) Normalized resilient modulus model for subbase and subgrade based on stress-dependent modulus degradation. J Transp Eng 135(9):600–610. https://doi.org/ 10.1061/(ASCE)TE.1943-5436.0000019 19. Mossazadeh JM, Witczak W (1981) Prediction of subgrade moduli for soil that exhibits nonlinear behavior, 810, Transportation Research Board, Washington, DC, 9–17 20. NCHRP:1-37A, 2004. Mechanistic-empirical pavement design guide. National Cooperative Highway Research Program, USA 21. Ng K, Hellrung D, Ksaibati K, Wulff SS (2018) Systematic back-calculation protocol and prediction of resilient modulus for MEPDG. Int J Pavement Eng 19(1):62–74. https://doi.org/ 10.1080/10298436.2016.1162303 22. Terzaghi K, Peck RB, Mesri G (1996) Soil mechanics in engineering practice. John Wiley & Sons 23. Ping WV, Yang Z, Gao Z (2002) Field and laboratory determination of granular subgrade moduli. J Perform Constr Facil 16(4):149–159 24. Witczak MW, Qi X, Mirza MW (1995) Use of nonlinear subgrade modulus in AASHTO design procedure. J Transp Eng 121(3):273–282 25. Uzan J (1992) Resilient characterization of pavement materials. Int J Numer Analyt Meth Geomech 16(6):453–459

Prediction of Properties of Asphalt Emulsion Residue Using Maturity Method B. L. Anjali and Aravind Krishna Swamy

Abstract Asphalt emulsion is extensively used in pavement preservation techniques. The curing process through which the emulsion gains intended binder properties is highly influenced by the emulsion type, curing time, and temperature, and relative humidity. In the domain of cement concrete characterization, the maturity method has been used to predict strength gain under the combined effect of time and temperature. The present paper attempts to apply the maturity method to evaluate the influence of time and temperature on the curing process of asphalt emulsion. In this study, asphalt emulsion (slow-setting asphalt emulsion) was cured at different curing temperatures (120,135,150, and 165 °C) and curing times (1, 2, and 3 hours). The asphalt emulsion residue obtained after different curing conditions was tested for rheological characterization. The results indicate that the curing conditions influenced the development of the properties of the emulsion residue. The maturity as a function of equivalent age and temperature showed a reasonable trend at higher curing temperatures. Keywords Asphalt emulsion · Curing · Maturity method · Complex modulus

1 Introduction Asphalt emulsion is extensively used in the pavement industry for surface treatments. Motamed et al. reported that around 10–15% of the asphalt used in the paving industry is in the form of emulsion [1]. This extensive use of emulsion can be attributed to cost effectiveness, and environmentally friendly nature, reduction in energy consumption, and harmful gases (associated with the production of hot asphalt mixes) [2, 3]. Generally, asphalt emulsion is a heterogeneous system consisting of asphalt, water, B. L. Anjali · A. K. Swamy (B) Department of Civil Engineering, Indian Institute of Technology, Delhi, India e-mail: [email protected] B. L. Anjali e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_2

19

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emulsifier, and several other constituents. The asphalt droplets are dispersed in water with an emulsifier and prepared using a colloid mill. Depending on the intended application, the emulsion contains asphalt in the range of 40–80% (by weight). Based on reactivity, emulsion is classified into rapid setting (RS), medium setting (MS), and slow-setting (SS) emulsions. The highest and least setting time is observed with RS and SS emulsions, respectively. Based on the charge carried by asphalt droplets, emulsion is further classified into anionic or cationic types. It is well known that when the asphalt emulsion is mixed with aggregate and placed in the field, emulsion breaks into individual components. Thus, the water is either evaporated and released into the atmosphere or absorbed by aggregate on the application. Thus, the curing transforms the dispersed asphalt phase in the emulsion to a continuous binder phase on aggregates. During this process, the asphalt emulsion gains the intended properties of the binder, including adhesion on aggregate and holding individual particles together. The curing process depends on the surface characteristics of aggregate, emulsion properties [4], relative humidity, curing time, temperature [5], and weather characteristics [6]. It has always been a concern regarding the curing of the emulsion as it can affect the strength of asphalt emulsion. It is significant to identify the key parameters such as curing temperature and time affecting the curing process of emulsion. The present paper attempts to apply the maturity method to assess the influence of curing time and temperature on the curing process of asphalt emulsion.

2 Background The maturity method has been used to predict the strength gain of Portland cement concrete under the combined effect of time and temperature. The term ‘maturity’ indicates the extent of strength achieved by the Portland cement concrete. The maturity method states that given concrete cubes will attain similar strength at the same maturity level, even when subjected to different temperatures and times [7]. Thus, a maturity function is used to express the temperature history of the concrete sample over the entire curing period. The numerical value of maturity index is calculated using the maturity function [8]. In 1951, Saul developed a maturity function which is the product of temperature and time. The study was based on the steam curing of concrete. The maturity function (degree-days or degree-hours) proposed was known as the Nurse-Saul equation [9] presented in Eq. (1). M=

t 0

(Ta −T0 ) × t.

(1)

where M = temperature–time factor at time t, Ta = average concrete temperature (°C) during the time interval, T0 = datum temperature (temperature below which no curing occurs), and t = time interval in days or hours.

Prediction of Properties of Asphalt Emulsion …

21

In the Nurse-Saul equation, a linear relationship was assumed between the initial rate of strength development and temperature [10]. Subsequently, based on the Arrhenius equation [11], nonlinear relationship between strength development and temperature was used to arrive at a refined maturity method. Within the cement concrete domain, the Arrhenius function describes the chemical reaction rate during the hydration of concrete with reference to material sensitivity and temperature [7]. The maturity function is expressed as equivalent age at a specified temperature, and Te is given in Eq. (2). −Q

Te = e



1 Ta

− T1s



t.

(2)

where Q = activation energy (kJ/mol) divided by universal gas constant (8.314 J /K mol), t = time interval (days or hours), Ta = average concrete temperature (°K) during the time interval, and Ts = specified temperature (°K). Later, Carino and Lew proposed another expression for computing the equivalent age while incorporating the temperature sensitivity factor (B), as presented in Eq. (3). The temperature sensitivity factor in the function defines the effect of curing temperature on the rate of strength development [12]. It was stated that the temperature sensitivity factor has more importance than the activation energy. Further, almost the same results of equivalent age were obtained using both expressions [Eqs. (2) and (3)] [12]. te =



e B(T −Tr ) × t.

(3)

where B = temperature sensitivity factor (/°C), T = average concrete temperature (°C) during the time interval, and Tr = reference temperature in °C. Various researchers have applied the maturity method in cold mix asphalt. Doyle et al. [13] used the maturity method to evaluate the cold mix materials and correlated the stiffness and maturity for various conditioning times and temperatures. Later, Kuna et al. [10] studied the influence of time and temperature on the curing of foamed bitumen mixtures. The study showed that a good correlation exists between the maturity and stiffness values of specimens. Recently, a strength-maturity function was developed to predict the strength of cold mix asphalt [7]. It was reported that the linear–hyperbolic maturity function could predict the strength of cold asphalt emulsion mix with an accuracy of greater than 95% [7]. Using foamed bituminous mixtures data, Kuna et al. developed maturity as a function of the equivalent age and curing temperature as presented in Eq. (4). The function was developed by incorporating the curing temperature in the Arrhenius equation [10]. Similar to cement concrete, asphalt emulsions gain strength on curing under different curing temperatures and time conditions. The evaporation of water and recovery of asphalt in emulsion will lead to the strength development of asphalt. Consequently, the concept of maturity method can be used to estimate the stiffness development of asphalt emulsion. In Eq. (4), the maturity is defined as a function of equivalent age and curing temperature, and the equivalent age describes the curing time required for a sample to get

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cured at a reference temperature which is equal to the maturity of sample cured at different temperatures [10]. M = te × T.

(4)

where M = maturity (°C-days), T = curing temperature (°C), and te = equivalent age at reference temperature (°C-days). Even though several researches have been conducted at the mixture level using the maturity method, less research has been performed at the binder level. It is well known that the water present in asphalt emulsion escapes during the curing process. This in turn helps to increase the viscosity and stiffness in the residual asphalt binder. It is necessary to study the properties of the asphalt emulsion residue and the strength development of asphalt emulsion on curing. This study attempts to study the combined effect of temperature and duration on curing process of asphalt emulsion using the maturity method.

3 Experimental Investigation 3.1 Materials Asphalt emulsion or slow-setting emulsion (SS-2) grade was used in this research. The physical properties of asphalt emulsion are summarized in Table 1. The asphalt emulsion was cured at different curing temperatures (120, 135, 150, and 165 °C) and time durations (1, 2, and 3 hours). Based on the procedure recommended in ASTM D6934, curing temperature of 165 °C was used. Thus, the highest curing temperature was limited to 165°C. Further, lower testing temperature was expected to aid in limited aging of bitumen. Thus, the lowest curing temperature of 120°C was also selected. As all curing temperature is above evaporation temperature of the water, residual asphalt binder is obtained within a reasonable time. Initially, 50 g of asphalt emulsion was taken in a glass beaker and placed in a hot oven for pre-set curing conditions. Moisture loss through evaporation was measured by taking the weight of the container at every 30-min time interval. After the predetermined curing time, glass containers were taken out of the oven and left to cool to room temperature. Further, the test specimens were prepared using emulsion residue and tested for their rheological properties.

Prediction of Properties of Asphalt Emulsion …

23

Table 1 Properties of asphalt emulsion Property

Value

Test procedure Limit as per IRC SP:100-2014

Residue on 600 µm IS sieve (% by mass), Max

0.03

IS: 8887

0.05

Viscosity by Saybolt Furol 38 Viscometer at 25°C, in second

IS: 3117

30–150 s

Coagulation of emulsion at low temperature

IS: 8887

Nil

Storage stability after 24 h, %, 0.48 Max

IS: 8887

2

Particle charges, +ve/−ve

Positive

IS: 8887

Positive

Stability to mixing with cement (% coagulation)

1.20

IS: 8887

Maximum 2

Miscibility with water

No coagulation IS: 8887

No coagulation

i. Residue by evaporation, %, Min

67.34

IS: 8887

60

ii. Penetration at 25 °C /100 g/5 s

82

IS:1203

60–120

iii. Ductility at 27 °C, cm, Min 69

IS:1208

50

iv. Solubility in trichloroethylene, %, Min

IS:1216

98

Nil

Test on residue

99.1

3.2 Test Procedure The rheological characterization of asphalt emulsion residue was performed using a dynamic shear rheometer. The linear viscoelastic limit was found through the amplitude sweep test. This test was conducted by varying the strain amplitude from 0.001 to 0.3%. Based on the test results, the strain limit of 0.1% was selected. Dynamic modulus and complex viscosity values were obtained by performing temperature– frequency sweep tests. The temperature range of 10 and 60 °C (with an increment of 10 °C) and frequency range of 50–0.01 Hz were used in this work.

4 Analysis 4.1 Complex Modulus and Phase Angle Complex modulus (|G*|) values obtained from the temperature–frequency sweep test were used to construct the master curves. The asymmetric sigmoid function coupled with the free shifting approach was used to construct master curves as given in Eqs. (5) and (6). The model parameters of asymmetric sigmoid function and temperature shift

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B. L. Anjali and A. K. Swamy

factors were obtained by minimizing the total sum of the square of errors between the measured and predicted |G*| values. The complex modulus master curves were constructed at a reference temperature of 40 °C.   β2 log G ∗  = β1 ±  1 . 1 + β3 ∗ exp(β4 − β5 ∗ log( fr )) β3

(5)

f r = f × at

(6)

where βi = regression coefficients in the asymmetric function, fr = reduced frequency, f = test frequency, and at = time–temperature shift factor.

4.2 Maturity Function The Arrhenius maturity function presented in Eq. (3) was used to compute the maturity in terms of equivalent age of the asphalt emulsions. Various researchers have used the Arrhenius equation to characterize the temperature dependence of the viscosity of asphalt [14]. In this investigation, the temperature sensitivity factor in the function was computed from the measured complex viscosities (η*) at different testing temperatures. The complex viscosity values were obtained from the temperature–frequency sweep tests. The Arrhenius equation describing the relation between viscosity, temperature, and activation energy is given in Eq. (7). Ea 1 η∗ = Ae( R )( T )

(7)

where A = rate constant at 0 °C, E a = activation energy, R = universal gas constant, and T = temperature in °K. The temperature sensitivity factor, B, was determined by fitting an exponential curve of Eq. (8) in the plot of complex viscosity versus curing temperature (°C) at various curing durations. The computed B values were used to determine the maturity in terms of equivalent age based on Eq. (3). Finally, the estimated equivalent age values were used to determine the maturity using Eq. (4). η∗ = Ae BT .

(8)

Prediction of Properties of Asphalt Emulsion …

25

Fig. 1 Mass loss percent of SS-2

5 Results and Discussion 5.1 Effect of Curing Condition on the Percentage of Mass Loss As mentioned previously, mass loss of asphalt emulsion was recorded at every 30-min interval at all curing conditions. These measurements along with the initial weight of emulsion were used to compute the percentage of mass loss. Percentage mass loss as a function of time computed at different curing temperatures is presented in Fig. 1. As expected, the percentage mass loss was more during the initial stages of curing. This indicated a higher rate of mass loss during the initial stages of curing. In general, at any particular temperature, the evaporation process within asphalt emulsion takes place in three different phases. The first phase is associated with higher evaporation rate. In the second stage, the evaporation rate starts to decrease. Finally, in the third stage, the evaporation rate almost becomes zero [2]. Thus, it can be concluded that there exists an inverse relationship between curing time duration and the evaporation rate. This observation was in line with the study performed by Ouyang et al. [2]. Further, the evaporation rate was a function of curing temperature. In this research, at any particular time of interest, the least and highest percentage of mass loss (at terminal curing time) was observed at 120 and 165 °C, respectively.

5.2 Influence of Curing Temperature and Time on the Complex Modulus Asphalt emulsion cured for different curing conditions (temperature, duration) was tested for their complex modulus values using temperature–frequency sweep tests. Using the test results, complex modulus master curves were constructed. The master

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B. L. Anjali and A. K. Swamy

Fig. 2 Effect of curing condition on the complex modulus

curves constructed using binders cured for 1 and 3 h are presented in Fig. 2a and b, respectively. Master curves obtained at a testing temperature of 120 and 165 °C are presented in Fig. 2c and d, respectively. The master curve obtained with the unaged binder is also plotted in these figures for comparison. In general, at a particular curing time, the master curves obtained after curing were positioned above the original emulsion master curve. A significant increase in |G*| values was observed with 165 °C curing temperature than the original emulsion. On the contrary, the complex modulus values of emulsion residue obtained at 120 °C were found to be lower and were positioned closer to the original emulsion. Master curves obtained at the other two curing temperatures were positioned in between these two curves. Due to higher curing temperature of 165 °C, it is expected to remove water entirely from the asphalt emulsion. Further, a curing temperature of 120 °C was expected to leave a certain amount of water within the emulsion. Hence, |G*| values of emulsion residue obtained after curing (120 °C and 1 h duration) almost overlapped with the original emulsion. A significant increase in complex modulus

Prediction of Properties of Asphalt Emulsion …

27

Fig. 3 Influence of mass loss percent on the stiffness of asphalt emulsion

values at higher curing durations was observed at higher reduced frequencies as shown in Fig. 2c. While this observation was not seen at lower reduced frequencies, this could be attributed to the significant effect of testing temperatures and frequencies. At higher temperature or lower frequency, the asphalt binder exhibits viscous behavior. Besides, elastic behavior is exhibited at lower temperature or higher frequency. To check the effect of water content on modulus, complex modulus values observed at 60 Hz were plotted against percentage mass loss. The same is presented in Fig. 3. As seen in figure, lower |G*| values were observed at lower percentage mass loss values. However, significantly higher complex modulus values were observed at limiting percentage mass loss values. This was true for all curing conditions. This again reinforces the previous observation of the higher influence of water content on modulus values.

5.3 Maturity Function Maturity was determined for asphalt emulsion at different curing temperatures and time durations. The maturity in terms of equivalent age at a reference temperature is shown in Fig. 4. As evident from these figures, significant overlap can be seen with 150 and 165 °C data. However, no such overlap was seen with the other two temperature data. This can be attributed to consistent asphalt binder residue obtained at higher curing temperatures when compared to lower curing temperatures. Consequently, it can be stated that the maturity method as a function of equivalent age and temperature showed a reasonable trend at higher curing temperatures only. It can be attributed to the slow process of the evolution of stiffness due to low curing temperature and high moisture content. This is confirmed with the presence of water content which resulted in lowering the |G*| values (Fig. 5).

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Fig. 4 Effect of equivalent age on the stiffness of asphalt emulsion

Fig. 5 Effect of maturity on the complex modulus of asphalt emulsion

6 Conclusion This research demonstrated the influence of temperature and time on the curing process of asphalt emulsion using the maturity method. The maturity function indicated the amount of development of property of the asphalt emulsion. This maturity method assumes that the samples achieving equal maturity index will have equal strengths. In this study, an attempt has been made to evaluate the degree of development of stiffness of asphalt emulsion on the curing process. For this, the asphalt emulsion residue was obtained after subjecting to different curing temperatures and times. The temperature-frequency sweep tests were then performed for the rheological characterization of emulsion residues. Based on this investigation, the following conclusions are made: 1.

During the initial stages of curing, a higher evaporation rate was observed. This evaporation rate decreased with an increase in curing time. Further, at a particular curing time, higher mass loss was observed with higher temperature.

Prediction of Properties of Asphalt Emulsion …

2. 3.

29

This indicated that asphalt emulsion conditioned at higher curing temperatures would get cured faster than at lower temperatures. In general, the modulus of asphalt emulsion increased with decrease in water content within the emulsion. The maturity of asphalt emulsion as a function of equivalent age and temperature showed a reasonable trend at higher curing temperatures. It can be attributed to the slow process of the development of stiffness due to low curing temperature and high moisture content.

References 1. Motamed A, Salomon D, Sakib N, Bhasin A (2014) Emulsified asphalt residue recovery and characterization: combined use of moisture analyzer balance and dynamic shear rheometer. Transp Res Rec 2444(1):88–96 2. Ouyang J, Meng Y, Tang T (2021) Characterization of the drying behaviour of asphalt emulsion. Constr Build Mater 274:122090 3. Abedini H, Naimi S, Abedini M (2017) Rheological properties of bitumen emulsion modified with NBR latex. Petroleum Sci Technol 35(15):1576–1582 4. Gorman JL, Crawford RJ, Harding IH (2004) Bitumen emulsions in road construction—a review. Road Transp Res 13(1):25–38 5. Abouelsaad A, Swiertz D, Bahia HU (2019) Study of factors affecting curing of asphalt emulsion tack coats. Transp Res Rec 2673(12):619–627 6. Yaacob H, Chang FL, Rosli Hainin M, Jaya RP (2015) Curing of asphalt emulsified tack coat subjected to Malaysian weather conditions. J Mater Civ Eng 27(4):04014147 7. Otieno MN, Kaluli JW, Kabubo C (2020) Strength prediction of cold asphalt emulsion mixtures using the maturity method. J Mater Civ Eng 32(5):04020096 8. ASTM (2004) Standard practice for estimating concrete strength by the maturity method. ASTM C 1074 9. Saul AGA (1951) Principles underlying the steam curing of concrete at atmospheric pressure. Mag Concr Res 2(6):127–140 10. Kuna K, Airey G, Thom N (2016) Development of a tool to assess in-situ curing of foamed bitumen mixtures. Constr Build Mater 124:55–68 11. Kuna K (2015) Mix design considerations and performance characteristics of foamed bitumen mixtures (FBMs). PhD thesis, University of Nottingham 12. Carino NJ, Lew HS (1984) The maturity method: from theory to application. Cement, Concrete Aggregates 6(2):61–73 13. Doyle TA, McNally C, Gibney A, Tabakovi´c A (2013) Developing maturity methods for the assessment of cold-mix bituminous materials. Constr Build Mater 38:524–529 14. Narayan SPA, Little DN, Rajagopal KR (2019) Incorporating disparity in temperature sensitivity of asphalt binders into high-temperature specifications. J Mater Civ Eng 31(1):04018343

Quantification of Aging of Polymer Modified Binder Using Creep Recovery and Yield Energy Test Methods Arpita Suchismita and Dharamveer Singh

Abstract In the present study, multiple stress creep recovery (MSCR) test and binder yield energy test (BYET) have been used to quantify the intensity of aging of an SBS polymer modified binder (PMB). Three different long-term aging levels, i.e., 20 h pressure aging vessel cycles (1CPAV), 40 h (2CPAV), 60 h (3CPAV), were selected to simulate the range of field aging behavior of modified binder. The MSCR results showed that with an increase in the cycles of aging, the percentage recovery of PMB increases. However, this enhanced recovery behavior of the binder is attributed to the increase in viscosity of base binder due to oxidative hardening and the degraded polymer network with aging has a minimal role to play. Thus, it can be concluded recovery parameter obtained from the standard MSCR test condition may not be suitable to judge the aging intensity of SBS PMB subjected to different aging cycles. On the contrary, BYET results showed a reduction in elastic recovery with an increase in aging cycles. Therefore, BYET-ER is a better parameter to quantify aging, particularly for SBS PMB. Keywords Polymer modified binder · Aging · Recovery · Elasticity

1 Introduction Utilization of polymer modified binder (PMB) in asphaltic pavement construction is growing due to its enhanced resistance toward pavement distresses. However, there are concerns among researchers regarding the aging mechanism of PMB. The aging of asphalt binder is an inevitable phenomenon during preparation, construction, and service life of pavement due to many factors such as heating and construction temperature, traffic loading, and climatic variations. However, the aging of PMB A. Suchismita (B) · D. Singh Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India e-mail: [email protected] D. Singh e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_3

31

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A. Suchismita and D. Singh

becomes complex involving oxidation of binder as well as degradation of the polymer [1], which needs a proper understanding. The conventional approach adopted to simulate long-term binder aging in the laboratory includes pressure aging vessel (PAV) test as per ASTM D6521 (2013). It has been reported that PAV aging simulates 4–7 years of aging in the field which may also depend on the type of binder and modifier [2–4]. However, the heating and preparation temperature of PMB is quite high (165–185 °C); therefore, it may happen that conventional aging techniques developed and practiced for unmodified binders may not hold appropriate for PMB. Many researchers have also tried to modify PAV aging durations to get a closer behavior of binder to field aging of PMB. Rad et al. [5] adopted 40-h PAV aging to simulate highly aged binder and suggested more than two PAV cycles. He et al. [3] compared the performance of 40 and 60-h PAV aged samples with the performance of extracted field aged binder and found that the hightemperature performance of 60-h PAV aged sample was similar to that of field aged binder. To understand the aging mechanism of PMB, most of the studies have concentrated on the chemical and morphological analysis and few have considered the dynamic mechanical analysis of the aged binder. However, since PMB is appreciated for its better recovery after the removal of a sustained load, effects of aging on this property of PMB need to be addressed. Multiple stress creep recovery (MSCR) test is extensively used to study the resistance of an asphalt binder to rutting by measuring percentage recovery (R) and non-recoverable creep compliance (J nr ) and is believed to give comparable results with the field measured data. However, the challenge was to see how this method can be used to measure the intensity of aging of PMB. Very few researchers have assessed this parameter to quantify the aging of asphalt binder. Based on rheological parameters (G* and δ), Airey [6] has stated that “SBS copolymers derive their strength and elasticity from physical cross-linking of the molecules into a three-dimensional network and with aging there is a degradation of the polymer network in the binder”. Hence, it is expected that with aging the recovery of PMB must reduce. Cuciniello et al. [7, 8] have evaluated the effect of aging on %R and J nr value. They have observed that for binder with 2% SBS content, increase in %R value and decrease in J nr value with aging. However, with higher SBS content (4% and more) reverse trend has been reported. This indicates, with less polymer content in the binder the degradation in polymer network is not adequate to interact with the binder phase, and the increase in %R value may be due to oxidative hardening of the binder and not due to elasticity imparted by the polymer network. It is important to realize that with aging, the high-temperature performance of PMB may improve due to improved stiffness. However, this high stiffness may lead to cracking problems at intermediate and low temperatures. It is therefore equally important to learn the effects of aging on binder properties at intermediate temperatures. Therefore, the main focus of this paper is to quantify the aging of a styrene butadiene styrene (SBS) polymer modified binder in terms of percentage recovery of aged binder after the removal of the applied load. As it is believed that aging occurring infield is more severe compared to the conventional standard PAV aging, three different PAV aging durations were implemented, i.e., 20, 40, and 60 h based on

Quantification of Aging of Polymer Modified Binder Using …

33

the literature. However, future studies will be conducted to relate laboratory aging levels with field aging conditions. To compute the reduction in elasticity of the binder due to aging, the recovery of the aged samples was investigated by using the standard MSCR test (ASTM D7405) and binder yield energy test (BYET) (AASHTO TP123). BYET method has been used by researchers to predict fatigue characteristics of asphalt binder [9, 10]. BYET recovery strain can also be a good indicator of elastic recovery of asphalt binder. Overall, this study will help quantify the severity of aging that can occur in the field and also aid in selecting a suitable test method to understand the polymer degradation within PMB subject to different aging levels. The objective of the study was to quantify aging of PMB subjected to varying long-term aging durations using MSCR and BYET tests.

2 Objective of the Study The primary objective of the study is to quantify aging of PMB by evaluating the effect of varying long-term aging durations on its recovery property. To investigate the effects of aging on recovery of PMB, MSCR parameters were evaluated at high temperature and BYET elastic recovery parameter was evaluated at intermediate temperature.

3 Materials An SBS polymer modified binder with 3% SBS content, popularly used in India for the construction of roadway and airfield pavements was selected for the study. The selected PMB was found to have a penetration value of 45 dmm, which satisfies grade 40 of PMB as per Indian standards (IS15462; 2004). The high-temperature superpave performance grade (PG) of the binder was found to be 82 °C. Some of the physical properties and rheological properties of PMB are listed in Table 1. Table 1 Properties of PMB Characteristics

Unaged

RTFO aged

Standard

Penetration value, (dmm)

45

42

ASTM D5M

Softening point, °C

65

70

ASTM D 36M

Dynamic viscosity (Pa s) @ 150 °C

0.74

1.27

ASTM D 4402M

Elastic recovery @ 15 °C

–

75%

ASTM D 6084M

High PG grade, °C

82

82

ASTM D 6373

34 Table 2 Aging levels and abbreviations used for samples

A. Suchismita and D. Singh Aging levels

Abbreviation Aging conditions

Unaged sample

UA

No conditioning

RTFO aged sample

STA

163 °C, 85 min

One cycle PAV aged sample

1CPAV

100 °C, 2.1 MPa, 20 h

Two cycles PAV aged sample

2CPAV

100 °C, 2.1 MPa, 40 h

Three cycles PAV aged 3CPAV sample

100 °C, 2.1 MPa, 60 h

4 Experimental Methodology The following tests were conducted at all aging levels, and an average of three results have been reported for each sample.

4.1 Aging of PMB PMB was first subjected to short-term aging (STA) by rotational thin film oven (RTFO) in accordance with ASTM D2872 followed by long-term aging (LTA) using PAV as per ASTM D 6521. STA condition of the sample was the same for all three long-term aging levels. Binder was exposed to extended long-term aging durations of 40 and 60 h along with the conventional 20 h without a rest period. The abbreviations used at different aging levels are given in Table 2.

4.2 MSCR Test MSCR test which gives an idea about the elastic response of asphalt binder subjected to ten cycles of creep and recovery is usually conducted to assess the rutting performance of STA binder samples as per ASTM D 7405. Considering the fact that PMB may lose its basic elasticity, MSCR test was conducted to check its elastic behavior subjected to varying aging conditions. MSCR test was conducted in dynamic shear rheometer (DSR) having a 25-mm-diameter parallel plate arrangement with 1 mm gap between plates at 76 °C. Strain response was recorded, and J nr and %R values at 0.1 kPa and 3.2 kPa stress levels were estimated for further analysis.

Quantification of Aging of Polymer Modified Binder Using …

35

4.3 BYET Test BYET method B is a recently developed procedure to evaluate the elastic recovery of asphalt binder at intermediate temperature as per AASHTO TP 123. In this method, a constant strain rate of 2.315% s−1 is applied until 277.78% strain is achieved. Then a recovery phase binder is carried out for 30 min, and stress and strain values are recorded for both the loading and recovery period. The elastic recovery (ER) is computed by the two parameters, i.e., (a) the strain generated after initial loading (277.78% strain) and (b) strain generated after 1800 s of recovery, the percentage reduction in these two strains gives ER value. This test was conducted in DSR using 8 mm parallel plate geometry and 2 mm thick samples at 25 °C.

5 Results and Discussions 5.1 Recovery and Non-recoverable Creep Compliance (Jnr ) Figure 1a presents the MSCR recovery (%R) values of PMB at different aging conditions. It can be noticed that the %R value increased with an increase in the aging level. For example, at 3.2 kPa stress level %R value for UA, STA, 1CPAV, 2CPAV, and 3CPAV samples are 17%, 43.6%, 57.6%, 70%, and 81.9%, respectively. Improvement in percentage recovery indicates improved elasticity and thus better performance in terms of rutting at high temperatures. Figure 1b illustrates the J nr value of PMB at 0.1 and 3.2 kPa stress subjected to varying aging conditions. It can be derived that the J nr value is more for unaged binder irrespective of stress level, and it is decreasing with an increase in aging level. For example, J nr value of UA, STA, 1CPAV, 2CPAV, and 3CPAV samples at 3.2 kPa stress level are 3.2, 0.83, 0.20, 0.05, and 0.009 kPa−1 , respectively. This shows that with extended aging conditions; the binder has become more rut resistant. A similar trend is observed for 0.1 kPa stress. Cuciniello et al. [7] have also reported a similar kind of response (increase in %R value and decrease in J nr value with aging) for binder with 2% SBS content. However, it is already being established that with aging there is a degradation of the polymer network, which implies the elasticity imparted by the polymer network to asphalt binder should get reduced. However, the results showed that %R increases with an increase in aging level. Therefore, this improved recovery of the binder with aging may be attributed to the increase in viscosity of the base binder because of oxidative hardening and not due to the action of the polymer network. Moreover, to quantify the aging intensity of PMB binder, two other MSCR parameters (ε1 and ε10 ), i.e., creep and recovery strain, were computed and presented in Table 3. It can be observed from Table 3 that creep strain (ε1 ) and recovery strain (ε10 ) of UA binder is the highest followed by STA, 1CPAV, 2CPAV, and 3CPAV binders. A similar trend can be observed for both the stress levels, 0.1 and 3.2 kPa. Thus, it may be stated that due to the degradation

36

A. Suchismita and D. Singh

120

Fig. 1 a %R b J nr from MSCR test at different aging levels

Recovery (%) @ 76 °C

100

(a)

0.1 kPa 3.2 kPa

87 77

80

69

63

58

60 45

82

70

44

40 20

17

0 UA

STA

1CPAV 2CPAV 3CPAV Aging Level

10.0

Jnr (1/kPa) @ 76 °C

2.0

0.1 kPa 3.2 kPa

3.2

1.0

0.6

(b)

0.8 0.1

0.1

0.2 0.04

0.1

0.01

0.0

0.01

0.0 UA

STA

1CPAV 2CPAV 3CPAV Aging Level

Table 3 Creep strain and recovery strain values from MSCR test Applied stress

0.1 kPa

Aging level

ε1 a

ε10 b

ε1

3.2 kPa ε10

UA

35.92246

19.64598

1240.861

1028.659

STA

15.18847

5.66243

470.9974 147.333

1CPAV

4.532201

1.426966

2CPAV

1.788049

0.404504

58.47088

3CPAV

0.527515

0.068631

17.39193

a Creep

strain at end of 1 s loading cycle (average of 10 cycles) strain at end of 10 s unloading cycle (average of 10 cycles)

b Recovery

265.518 62.54368 17.48328 3.137069

Quantification of Aging of Polymer Modified Binder Using …

37

of the polymer network with aging, both creep strain and recovery strain are deteriorating. Therefore, the higher percent recovery observed with aging is because of lower creep strain developed with increased intensity of aging. Therefore, to get a clearer picture of the change in mechanical behavior with the aging absolute value of recovery strain from MSCR test may be evaluated instead of the MSCR percent recovery parameter.

5.2 BYET Elastic Recovery (BYET-ER) The strain–recovery curve recorded from the BYET-ER test for PMB at all aging levels is presented in Fig. 2a. It can be observed from the figure that the UA sample exhibited the highest amount of recovered strain followed by STA, 1CPAV, 2CPAV, and 3CPAV samples. Further, ER (%) at each aging level is presented in Fig. 2b. The ER for UA, STA, 1CPAV, 2CPAV, 3CPAV sample was found to be 52%, 46%, 33%, 27%, and 21%, respectively. Thus, the results showed that recovery potential of PMB deteriorated with the increased level of aging, which can be associated with the degradation of the polymer network with aging. Therefore, BYET-ER can be a suitable test parameter to quantify the aging of PMB.

6 Conclusion A declined trend was observed for BYET-ER value with aging, indicating a reduction in elasticity of PMB. This reduction in elastic recovery may be owing to the degradation of polymer networks occurring at higher aging levels. However, contradictory trends have been observed for MSCR—percent recovery value. Whereas, another MSCR parameter, absolute recovery strain value was observed to deteriorate with aging. Therefore, it may be stated that the enhanced MSCR recovery with aging is due to the lower creep strain developed as a result of increased viscosity, and degradation in polymer network plays a minimal role in it. It can be suggested that with aging PMB may perform better at high temperatures, but it loses its flexibility at intermediate temperatures. In addition to that, BYET-ER can be suggested as a better parameter than MSCR recovery to quantify aging in PMB. If required, the absolute value of MSCR recovery strain may be computed to quantify the intensity of aging compared to MSCR percent recovery. Moreover, MSCR test protocol may need further refinements, such as an increase in stress levels or temperature, loading time, or rest period to quantify the aging behavior of PMB. However, the results of this study are limited to SBS PMB, and they may be validated for other types of binder as well.

38

A. Suchismita and D. Singh 350

Fig. 2 a Strain–recovery curve from BYET test b BYET elastic recovery

300

STA

1CPAV

2CPAV

(a)

3CPAV

250 Strain, %

UA

200 150 100 50 0 0

500

1000 Time, Seconds

1500

2000

70 (b) 60 Elastic Recovery (%)

52 50

46

40

33 27

30

21 20 10 0 UA

STA

1CPAV 2CPAV Aging Level

3CPAV

Acknowledgements Authors would like to acknowledge Industrial Research and Consultancy Centre (IRCC, seed grant no. 13IRCCSG0021) and Department of Civil Engineering, IIT Bombay, for sponsoring projects, through which DSR, RTFO, and PAV instruments were purchased.

References 1. Lu X, Isacsson U (2000) Artificial aging of polymer modified bitumens. J Appl Polym Sci 76(12):1811–1824 2. Durrieu F, Farcas F, Mouillet V (2007) The influence of UV aging of a styrene/butadiene/styrene

Quantification of Aging of Polymer Modified Binder Using …

3. 4. 5.

6. 7. 8.

9. 10.

39

modified bitumen: comparison between laboratory and on site aging. Fuel 86(10–11):1446– 1451 He Y, Alavi Z, Harvey J, Jones D (2016) Evaluating diffusion and aging mechanisms in blending of new and age-hardened binders during mixing and paving. Transp Res Rec 2574(1):64–73 Zhao K, Wang Y (2018) Influences of aging conditions on the rheological properties of asphalt binders. Int J Pavement Eng 1–13 Rad F, Sefidmazgi N, Bahia H (2014) Application of diffusion mechanism: degree of blending between fresh and recycled asphalt pavement binder in dynamic shear rheometer. Transp Res Rec J Transp Res Board 2444:71–77 Airey GD (2003) Rheological properties of styrene butadiene styrene polymer modified road bitumens. Fuel 82(14):1709–1719 Cuciniello G, Leandri P, Filippi S, Presti DL, Losa M, Airey G (2018) Effect of ageing on the morphology and creep and recovery of polymer-modified bitumens. Mater Struct 51(5):136 Cuciniello G, Leandri P, Filippi S, Lo Presti D, Polacco G, Losa M, Airey G (2019) Microstructure and rheological response of laboratory-aged SBS-modified bitumens. Road Mater Pavement Design 1–25 Bahia H, Wen H, Johnson CM (2010) Developments in intermediate temperature binder fatigue specifications. In: Development in asphalt, p 25 Wang Y, Wang C, Bahia H (2017) Comparison of the fatigue failure behaviour for asphalt binder using both cyclic and monotonic loading modes. Constr Build Mater 151:767–774

Effect of Short-Term Ageing on Mechanical Characteristics of Modified Bituminous Binders K. Muhammed Rinshad, M. Sivakumar, and M. V. L. R. Anjaneyulu

Abstract The findings of a laboratory investigation into the ageing of asphalt mix are presented in this paper. VG30 and NRMB40, two binders, are investigated. Antioxidants were added to cashew nut shell liquid (CNSL) at a rate of 1–3% by weight of binder, with a 1% increase, in this research. Physical parameters of the binder, such as softening point, viscosity, ductility, and penetration, were used to assess the impact of employing CNSL in the binder. Fourier transform infrared spectroscopy is used to look at the characteristics of CNSL (FTIR). Multiple stress creep recovery test (MSCR) results using a dynamic shear rheometer show that adding CNSL to the VG30 increased the value of recovery at 1% CNSL, indicating better fatigue resistance; however, there are no significant effects on NRMB40 fatigue resistance, which continues to decrease as CNSL content increases. The fatigue test results acquired from repeated load tests for unaged and aged mixes are analysed using resilient modulus, which verifies the fatigue resistance values provided by MSCR. Keywords VG30 · NRMB40 · Ageing

1 Introduction Flexible pavement makes up about 80% of the world’s highways. A crucial component of flexible pavement is the bitumen binder. Flexible pavements are often built with hot mix technique. All of the procedures of road construction, including mixing, laying, and compaction, are carried out at a high temperature in hot mix technique.

K. Muhammed Rinshad (B) · M. Sivakumar · M. V. L. R. Anjaneyulu National Institute of Technology Calicut, NITC PO, Kozhikode 673601, India e-mail: [email protected] M. Sivakumar e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_4

41

42

K. Muhammed Rinshad et al.

Increased traffic volume and other factors, such as environmental conditions, hasten pavement deterioration and result in pavement distresses. Pavement deterioration is caused by a number of factors, one of which being ageing. In both application and service, ageing is the process of bitumen binders hardening and embrittlement. The impact of high temperatures during transit to the petrochemical plant, production of asphalt, storage, transportation, and placement of the mixture are all factors in the asphalt ageing process. Oxidation, hardness due to the loss of volatiles, physical hardening due to storage, and exudative hardening are all variables that influence ageing. There are two types of ageing: short-term ageing, which occurs at a faster rate during the storage, heating, mixing, shipping, and laying down of the bituminous mix, and long-term ageing, which occurs during the pavement’s service life. RTFOT and PAV are the laboratory processes used for short-term and long-term ageing, respectively. Because changes in bitumen properties that occur throughout the ageing process have a significant impact on surface durability, several studies have been done in search of modifiers/additives to the bitumen that will slow down the ageing process. The anti-oxidant agent cashew nut shell liquid (CNSL) is used in this study, and its efficiency in slowing down the short-term ageing of bituminous mix is investigated. With different concentrations of CNSL, physical qualities such as softening point, viscosity, penetration, and ductility are investigated.

2 Literature Review Many studies have done in the past years to check the ageing properties of warm mix asphalt by using different additives. CNSL is anti-oxidant agent [1], so the effectiveness of CNSL as an anti-oxidant is checked in this study in ageing. CNSL also have the impact on physical properties especially which helps in saving energy by reducing the viscosity [2, 3]. But the behaviour of other physical properties have not checked during this study and use of CNSL also gives better fatigue and rutting resistance [2, 3]. Comparison of WMA and HMA is also done using CNSL as an additive but WMA shows better rutting resistance compared to HMA [3]. Ageing behaviour of binders available in India were studied [4], and it is found that asphaltene content is increasing after ageing. And it is noted that binder with lower ageing index is suitable for hot climate. Rheological properties of unaged and aged binder modified with epoxidized natural rubber [5] were studied, and it is shown that ENR improves the penetration, softening point, and viscosity, and aged ENRMAs have better physical and rheological properties than the base asphalt. Laboratory evaluation of short-term ageing was done using additives crumb rubber and rise husk ash [6], and the study concluded that CR and RHA improves the ageing resistance of base asphalt. The influence of ageing on polymer-modified asphalt [7] depends upon the type of polymer and which has a positive influence on mix performance. Micro-structure and rheological properties aged and unaged polymer-modified asphalt [8] were done

Effect of Short-Term Ageing on Mechanical …

43

in another study, and this study shown that resistance to deformation under high temperatures is increased and fatigue and cracking resistance at low temperatures are reduced. Kassem et al. [9] studied the ageing of asphalt using anti-oxidant additives and copolymers like Irganox 1010, Hydrated lime, Carbon black, DLTDP, Calprene 6120, Solprene 1205, Redicote AP, and Furfural, and discovered that copolymers like solprene and calprene significantly reduced the ageing index. Redicote AP additive was discovered to be a promising additive for slowing down the ageing process, with studies indicating that it worked better with PG 67-22 binder than with PG 64-22 binder. Antioxidants like irganox 1010 and DLTDP have a tendency to make the binder soft, so if you require them, mix them along with other additions. The behaviour of additive with various binder is different. Rheological and fatigue characterization of bitumen modified by anti-ageing compounds [10] using the additives Tri methyl propane, furfural, sodium montmorillonite, furfural irganox 1076, and irganox 1076 was done, and study shows that compounds irganox 1076 and furfural irganox 1076 provide good anti-ageing behaviour, when carbonyl content growth decreases ageing also decreases, sodium montmorillonite and furfural have no significant effect on ageing, and fatigue and smaller size particles improve the fatigue performance.

3 Objective of the Study The goals of this study are to see how effective CNSL is at preventing bituminous binders from ageing and to compare the fatigue characteristics of aged and unaged bituminous mixes. Most CNSL-related articles do not describe the ageing features of CNSL, nor do they discuss how the physical properties of CNSL alter with different percentages of CNSL content. So, these points are checked in this study.

4 Materials Used 4.1 Bitumen and Aggregate Bitumen (VG30 and NRMB40), crushed aggregate, and mineral filler were used to make the bituminous mixtures. In this investigation, aggregate was obtained from a local quarry in Kerala. Table 1 shows the results of testing aggregate and mineral filler qualities according to Indian standards (IS) 2386 parts I, III, and IV. Natural rubber modified bitumen (NRMB40) and VG30 grade bitumen were procured from Kochi refineries in India and employed in the manufacturing of HMA, with the properties of neat bitumen VG30 and NRMB40 presented in Tables 2 and 3 accordingly.

44

K. Muhammed Rinshad et al.

Table 1 Properties of aggregate Property

Test result

Specifications

Test method

Specific gravity (coarse aggregate)

2.71

2.6-2.8

IS:2386 (III)

Specific gravity (fine aggregate)

2.65

2.6-2.8

IS:2386 (III)

Aggregate impact value (%)

28

Max. 30

IS:2386 (IV)

Aggregate crushing value (%)

29

Max. 30

IS:2386 (IV)

Los Angeles abrasion value (%)

29.03

Max. 30

IS:2386 (IV)

Combined flakiness and elongation index (%)

34.3

Max. 35

IS:2386 (I)

Table 2 Properties of VG30 Property

Test result

Specification

Test method

Penetration value, 0.1 mm, 25 °C

62

50-70

IS:1203

Softening point (°C)

47.5

Min. 47

IS:1205

Specific gravity

1.01

0.97-1.02

IS:1202

Ductility (cm)

>100

Min. 40

IS:1208

Kinematic viscosity at 135 °C (cSt)

535

Min. 350

IS:1206

Property

Test result

Specification

Test method

Penetration value, 0.1 mm, 25 °C

49

30-50

IS:1203

Softening point (°C)

56

Min. 55

IS:1205

Specific gravity

1.02

0.97-1.02

IS:1202

Elastic recovery (%)

33

Min. 30

IS:1208

Kinematic viscosity at 135 °C (cSt)

578

300-900

IS:1206

Table 3 Properties of NRMB40

4.2 Cashew Nut Shell Liquid Cashew nut shell liquid (CNSL) is an organic additive that is natural, renewable, inexpensive, and readily available. The anti-oxidant oil CNSL is made from unsaturated phenols, a by-product of the cashew nut industry. The liquid that comes off the shells of cashew nuts is a caustic, combustible dark black liquid. It makes up around a quarter of the weight of a cashew nut. It is employed as an anti-oxidant for fuels and lubricants in a variety of industries, and it is crucial to the polymer industry. Unsaturated, long-chain, and natural phenolic chemicals such as anacardia acid, cardol, and polymeric components abound in CNSL. The addition of CNSL to asphaltic mixtures reduced stiffness, improved compatibility, and prevented aggregate stripping, all of which contributed to moisture damage resistance. The addition of CNSL reduces the mixture’s viscosity, which decreases the mixing temperature and reduces energy consumption and greenhouse gas emissions (Fig. 1, Table 4).

Effect of Short-Term Ageing on Mechanical …

45

Fig. 1 Chemical structure of CNSL. Source Bindu et al. [2, 3]

Table 4 Properties of CNSL

Property

Result

Specific gravity

0.96

Viscosity (cSt)

178.4 cSt at 25 °C

Bitumen is modified with CNSL at different percentages such as 1, 2, and 3% by weight of bitumen using mechanical stirrer. To get homogenous mixes, mixing is done at 150 °C with 4000 rpm for 10 min. Physical properties of the CNSL have shown in Table 5, and the chemical properties are also studied using FTIR test. FTIR. The infrared spectrum of absorption or emission of a solid, liquid, or gas is obtained using the Fourier transform infrared spectroscopy technique. The spectrum obtained as a result of this represents the sample’s molecular fingerprint. Each molecule or chemical structure has its own spectral fingerprint, which can be used to identify chemical bonds or functional groups in a molecule. FTIR Spectrum. The presence of anacardic acid can be seen in the FTIR spectrum by peaks of strong alkyl groups about 2854 cm−1 . The existence of cardol is confirmed by peaks in the FTIR spectrum of a long aliphatic chain around 2922 cm−1 . Cardanol is confirmed by the existence of C=C spanning around 1588 cm−1 (Fig. 2).

46

K. Muhammed Rinshad et al.

Table 5 Aggregate gradation IS sieve size (mm)

Cumulative % by weight of total aggregate passing

Mid value

Cumulative retained (%)

Retained (%)

Weight retained (g)

19.0

100

100

0

0

0

13.2

90–100

95

5

5

60

9.5

70–88

79

21

16

192

4.75

53–71

62

38

17

204

2.36

42–58

50

50

12

144

1.18

34–48

41

59

9

108

0.6

26–38

32

68

9

108

0.3

18–28

23

77

9

108

0.15

12–20

16

84

7

84

0.075

4–10

7

100

91

76

73

IS:1208

4

Kinematic viscosity (cSt)

535

489

382

434

IS:1206 (3)

5

Specific gravity

1.01

1.003

1.02

1.002

IS:1202

120

Penetraon (mm)

100 80 60 40 20 0 0.0%

0.5%

1.0%

1.5% 2.0% CNSL content

2.5%

3.0%

3.5%

2.5%

3.0%

3.5%

Fig. 7 Penetration-additive content curve NRMB40 60

Soening point (°C)

50 40 30 20 10 0 0.0%

0.5%

1.0%

1.5% 2.0% CNSL content

Fig. 8 Softening point-additive content curve NRMB40

52

K. Muhammed Rinshad et al. 35

Elasc recovery (%)

30 25 20 15 10 5 0 0.0%

0.5%

1.0%

1.5% 2.0% CNSL content (%)

2.5%

3.0%

3.5%

2.5%

3.0%

3.5%

Fig. 9 Elastic recovery-additive content curve NRMB40

700 600 Viscosity (cSt)

500 400 300 200 100 0 0.0%

0.5%

1.0%

1.5% 2.0% CNSL content

Fig. 10 Viscosity-additive content curve NRMB40

6.2 Fatigue Characteristics The fatigue characteristics of bituminous mixes are evaluated in terms of resilient modulus and initial tensile strain. The results of the repeated load test for VG30 and NRMB40 are shown in Tables 8 and 9. From the resilient modulus and initial tensile strain results of repeated load test, VG30 binder modified with 1% CNSL shows better fatigue results. For unaged VG30 with 1% CNSL content, the resilient modulus is 37% more than the unmodified VG30 and for aged it is nearly 17%. The phenolic chemicals found in the CNSL, such as anacardic acid and cardol, are thought to have aided in enhancing the affinity between

Effect of Short-Term Ageing on Mechanical …

53

Table 7 Properties of modified NRMB40 S. No.

Property (NRMB40)

Plain binder

1% CNSL

2% CNSL

3% CNSL

Test method

1

Penetration value 0.1 mm, 25 °C

49

70

93

113.5

IS:1203

2

Softening point (°C)

56

43.6

41.15

39.05

IS:1205

3

Elastic recovery (%)

33

21

20

18

IS:1208

4

Kinematic viscosity (cSt)

578

376

356

326

IS:1206 (3)

5

Specific gravity

1.02

1.01

1.01

1.01

IS:1202

Table 8 Resilient modulus of result of VG30 and NRMB40 Property

Pure binder

With 1% CNSL

With 2% CNSL

With 3% CNSL

Resilient modulus (MPa)-unaged VG30

6.334

8.725

3.623

3.149

Resilient modulus (MPa)-aged VG30

5.330

6.224

3.464

2.995

Resilient modulus (MPa)-unaged NRMB40

9.644

6.945

5.433

5.052

Resilient modulus (MPa)-aged NRMB40

7.088

5.818

4.540

3.976

With 1% CNSL

With 2% CNSL

With 3% CNSL

Initial tensile strain VG30 0.068

0.049

0.118

0.136

Initial tensile strain-aged VG30

0.080

0.068

0.124

0.146

Initial tensile strain NRMB40

0.044

0.061

0.078

0.084

Initial tensile strain-aged NRMB40

0.060

0.073

0.094

0.107

Table 9 Initial tensile strain result of VG30 and NRMB40 Property

Pure binder

aggregate and bitumen by boosting adhesion and the ability to absorb distresses. Due to repeated load application and when the CNSL content increases, it loses its adhesive property and for NRMB40, with an increase in CNSL content, fatigue characteristics reduces (Table 10). In all the above cases, F critical value is lesser than F value calculated, and hence, the null hypothesis is rejected. It means that the resilient modulus of CNSL-modified mixtures differs greatly from unmodified mixes.

54

K. Muhammed Rinshad et al.

Table 10 ANOVA test results of resilient modulus Binder

Source of variation SS

VG30 unaged

Between groups

31.328 1

31.328

Within groups

25.139 6

4.190

Total

56.467 7

Between groups

18.039 1

18.039

Within groups

11.999 6

1.999

Total

30.038 7

VG30 aged

NRMB40 unaged Between groups

NRMB40 aged

DF MS

F

P value F critical 7.477 0.034

5.987

9.020 0.024

5.987

18.474 0.005

5.987

29.730 165.542 0.007

5.987

55.514 1

55.514

Within groups

18.029 6

3.005

Total

73.543 7

Between groups

29.730 1

Within groups

10.784 6

Total

40.513 7

1.797

6.3 Multiple Stress Creep Recovery Test The MSCR test was used to evaluate the asphalt binder’s resistance to fatigue cracking by two parameters non-recoverable creep compliance (Jnr) and per cent recovery (R). It is done using dynamic shear rheometer. The results are shown in Table 11. If the recovery value is more, it is more resistant against fatigue. From the test result, VG30 binder modified with 1% CNSL content shows better recovery and more resistance against fatigue. And when the CNSL concentration increases in VG30, fatigue resistance decreases. In case of NRMB40, there is no significant effect; when the CNSL content increases, the resistance against fatigue decreases. Table 11 MSCR test results Property

Pure binder

With 1% CNSL

With 2% CNSL

With 3% CNSL

0.1 kPa 3.2 kPa 0.1 kPa 3.2 kPa 0.1 kPa 3.2 kPa 0.1 kPa 3.2 kPa Recoverable creep compliance (Jnr)-VG30

2.048

3.047

2.648

3.810

2.048

3.047

2.648

3.810

Recoverable creep compliance (Jnr)-NRMB40

0.555

1.977

2.108

6.782

0.555

1.977

2.108

6.782

Per cent recovery (R)-VG30

10.530

0.860

11.380

0

10.530

0.860

11.380

0

Per cent recovery (R)-NRMB40

48.37

5.14

30.78

0

48.37

5.14

30.78

0

Effect of Short-Term Ageing on Mechanical …

55

7 Conclusions Physical properties evaluation of the VG30 after modifying with CNSL shows that viscosity at 2% CNSL content is the lowest value. This shows that CNSL is an effective additive which reduces compaction and mixing temperature. The penetration value increases with increase in CNSL content upto 2% then then decreases. Ductility and softening point value decrease with increase in additive content. But in the case of NRMB40 with increase in CNSL content viscosity, ductility and softening point keep on decreasing and penetration value increases. Repeated load test evaluation gives that for VG30 binder after adding 1% CNSL CONTENT which gives better resilient modulus value and low initial tensile strain, which indicate that which shows better fatigue resistance at 1% CNSL content. This same result is shown for both aged and unaged mix sample. In case of NRMB40, the fatigue resistance keeps on decreasing with increases in CNSL content for both aged and unaged mis sample. The multiple stress creep recovery test result also follows the same trend of result of repeated load test, so we can confirm that these two sets of results are matchable.

References 1. Rodrigues FHA, Feitosa J, Ricardo NMP, Franca F, Oswaldo CJ (2006) Antioxidant activity of cashew nut shell liquid (CNSL) derivatives on the thermal oxidation of synthetic cis-1,4polyisoprene. J Braz Chem Soc 17(2) 2. Bindu CS, Joseph MS, Sibinesh PS, George S, Sivan S (2020) Performance evaluation of warm mix asphalt using natural rubber modified bitumen and cashew nut shell liquid. Int J Pavement Res Technol 13:442–453 3. Bindu CS, Joseph MS, Ullattampoyil M, Sajikumar S, Jayakumar M (2019) Performance comparison of hot-mix and warm-mix asphalts with cashew nut shell liquid. J Transp Eng Part B Pavements 145(1) 4. Kumbargeria YS, Biligiri KP (2016) Understanding aging behaviour of conventional asphalt binders used in India. Transp Res Procedia 17:282–290 5. Al-Mansob RA, Ismail A, Yusoff NIM, Albrka SI, Azhari CH, Karim MR (2016) Rheological characteristics of unaged and aged epoxidised natural rubber modified asphalt. Constr Build Mater 102:190–199 6. Abdelmagid AA, Feng CP (2019) Laboratory evaluation of the effects of short-term aging on high temperature performance of asphalt binder modified with crumb rubber and rice husk ash. Pet Sci Technol 7. Diab A, Enieb M, Singh D (2019) Influence of aging on properties of polymer-modified asphalt. Constr Build Mater 196:54–65 8. Wang L, Razaqpur G, Xing Y, Chen G (2015) Microstructure and rheological properties of aged and unaged polymer-modified asphalt binders. Road Mater Pavement Des 16(3):1–16 9. Kassem E, Khan MS, Katukuri S, Sirin O, Muftah A, Bayomy F (2017) Retarding aging of asphalt binders using antioxidant additives and copolymers. Int J Pavement Eng 1154–1169 10. Omairey EL, Zhang Y, Gu F, Ma T, Hu P, Luo R (2020) Rheological and fatigue characterization of bitumen modified by anti-ageing compounds. Constr Build Mater 265

Systematic Approach to Optimize Roller-Compacted Concrete Pavements Mixes Through Particle Packing Method M. Selvam, Rishab Dane, and Surender Singh

Abstract Typically, the strength gain mechanism in RCCP, especially at initial days (aggregate interlocking), is different from its counterparts and thus requires accurate proportioning of aggregates with good workability properties. In this study, the various fundamental parameters of RCCP mix design, viz. aggregate gradation, vebe consistency, water-cement ratio (W/C), and aggregate-cement ratio (A/C), are studied critically through the particle packing approach. The IRC: SP-68 blending approach is compared with the modified Andreasson model. The parameters considered for gradation optimization are packing density, consistency, maximum dry density (MDD), and compressive strength. The results indicated the effectiveness of IRC gradation in terms of higher packing density (1–4%), MDD (0.1–2.7%), and compressive strength (12–67%) over the modified Andreasson model. The finding from this analysis suggests that designing RCCP mixes with the A/C between 5.7 and 6 and W/C in the range of 0.36–0.40 could achieve better consistency and strength properties. Keywords Roller-compacted concrete pavement · Water-to-cement ratio · Aggregate-to-cement ratio · Packing model · Gradation

1 Introduction Roller-compacted concrete (RCC) technology has gained recent attention for pavement application owing to many-fold benefits over both types of conventional pavements, flexible and rigid pavements [1–3]. Some of the benefits are (a) lower construction cost due to lesser cement requirement and formwork-less construction, (b) lowertraffic delay cost due to early-age strength, and (c) high-speed construction due to the absence of joints [4–7]. The above benefits are usually due to the stiffer matrix of roller-compacted concrete pavements (RCCP) constituting around 80–85% aggregates, which, when compacted through steel/vibratory/pneumatic rollers, provides a M. Selvam · R. Dane · S. Singh (B) Indian Institute of Technology Madras, Chennai, India e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_5

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platform that could be used immediately [4, 8]. The high early-age strength is attained by virtue of aggregate-to-aggregate interlocking, which is manifested by denser packing of the aggregate skeleton. This is usually ensured by utilizing relatively well-graded aggregates [4]. Similar to the conventional concrete, the performance of RCCP is significantly governed by the gradation envelope [4]; however, there is no idealized gradation for RCCP since each country specifies its gradation envelope. Meanwhile, IRC: SP:68-2005 specifications (Indian guidelines for the design of RCCP) suggest an aggregate blending curve for RCCP [9]; however, employing these curves warrants many laboratory trials to achieve the maximum possible strength without affecting the workability requirements (zero-slump). Subsequently, the efficacy of the IRC: SP:68-2005 blending curve is to be studied thoroughly and fundamentally. Similarly, several packing models are available for improving the packing density of conventional concretes like modified Andreassen model (MA model) [10]; however, their applicability for RCCP remains uncertain. Therefore, an attempt has been made in this study to determine an ideal gradation for the Indian condition by comparing the blending curve of IRC: SP:68-2005 [9] and the most widely used theoretical packing model like modified Andreassen (MA) model [11]. Unlike conventional concrete, the RCCP entails lower water and binder content to achieve similar strength due to its dry nature [4]. However, similar to conventional concrete, decreasing the W/C ratio could improve the compressive strength of RCCP, which depicts the validity of Abram’s laws for RCCP mixtures [4, 12–14]. Contrarily, IRC: SP:68-2005 specifies that Abram’s law is not valid for RCCP [9]. Considering the above contrasting statements, a comprehensive study is carried out in the present study by varying the water-to-cement ratio (W/C) from 0.37 to 0.60 to validate the applicability of Abram’s laws for RCCP. Similar to the W/C ratio, the variation in the A/C ratio could affect the RCCP performance because aggregate constitutes about 80–85% of volume in RCCP mixtures [4, 15]. However, the desirable range of aggregate-to-cement ratio (A/C) for RCCP is unknown owing to the paucity of literature. Therefore, an extensive study is conducted in the current research by varying its ratio between 5.7 and 10.5 to evaluate the desirable range of A/C ratio for RCCP. The focus of the present study is to investigate: (a) the influence of gradation on the packing density, fresh properties, and the early-age performance of RCCP and (b) to study the effect of A/C ratio and W/C ratio on the fresh properties and mechanical behavior of RCCP mixes. To achieve the afforested objectives, an effort has been made to design and optimize the RCCP mixes by studying the IRC curve fundamentally and by following the packing density approach (MA model) with the selection of a wide spectrum A/C ratio and W/C ratio. The technical route of the present study is illustrated in Fig. 1. Broadly, the study would help in evaluating the optimum gradation, water/cement, and aggregate/cement ratios for RCCP mixtures. This would be helpful in reducing the cement content in the construction of RCCP, leading to the cost-effective and sustainable development of road infrastructure promoting the use of RCCP.

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Fig. 1 Outline of research methodology

2 Materials, Mix Proportioning, and Sample Preparation 2.1 Materials In the study, Ordinary Portland Cement (OPC) of grade 53 was used. The specific gravity and specific surface area of OPC were found to be 3.15 and 281 m2 /kg, which were determined according to IS 4031 (Part 11) [16] and IS 4031(Part 2) [17]. Three types of coarse aggregates of CA20, CA10, and CA4.75 were used for the current study. The numerical value after the character (CA) indicates the nominal maximum size of aggregates. The specific gravity, water absorption, impact value, density, and voids of CA20, CA10, CA4.75, and fine sand were determined according to IS 2386 (Part 3) [18] and IS 2386 (Part 4) [19], and the corresponding results are shown in Table 1. The natural river sand falling in grading zone II was incorporated as fine aggregate in all RCCP mixtures. Figure 2 shows the particle size distribution of both coarse and fine aggregates. The aggregates were blended to achieve the IRC blending curve, and mixtures were optimized using the MA model (Eq. 1). The distribution coefficient or exponent used for the present study was 0.32. The blending curve thus achieved is shown in Fig. 3.

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Table 1 Physical properties of aggregates Aggregate type

Specific gravity

Water absorption (%)

Impact value (%)

Density (kg/m3 )

Voids (%)

CA20

2.74

0.19

13.9

1692

38

CA10

2.76

0.30

–

1751

37

CA4.75

2.75

0.40

–

1596

42

Fine sand

2.66

0.65

–

1700

36

Fig. 2 Particle size distribution of coarse and fine aggregate

 Modified Andreassen models : P(d) =

d − do D − do

where P(d) is the cumulative (volume) percent finer than d, d is the particle size, d o is the minimum particle size of the distribution, D is the maximum particle size, and q is the distribution coefficient or exponent.

q × 100

(1)

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Fig. 3 Particle size distribution of IRC blending curve and MA model

2.2 Mix Proportioning The optimum moisture content (OMC) of the IRC blending curve and MA model was determined according to IS 2720 (Part 8) [20], wherein the modified proctor was employed. The test was performed to critically study the mix design parameters such as water-cement ratio and aggregate-cement ratio, which was varied from 5.7–10.5 to 0.37–0.60, as shown in Table 2. The moisture density plot was obtained for IRC and MA mix by varying water content from 4 to 7% at a 0.5% increment level. The MDD corresponding to OMC for different cement content and gradations are shown in Table 2. The quantities of coarse and fine aggregates, binder content, and water content for each mix are shown in Table 2.

2.3 Sample Preparation All the RCCP mixtures were prepared in the pan mixer at their corresponding OMC values, and the procedure used for making concrete is shown in Fig. 4. The cylindrical specimens of size 100 mm × 200 mm were prepared by compacting the concrete mix in 4 layers using the vibrating hammer for a maximum of 20 s or until the formation of the mortar ring conforming to ASTM C1435 [21]. The step-by-step process involved in the cylindrical specimen fabrication is shown in Fig. 4.

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Table 2 OMC, MDD, A/C, and W/C for IRC blending curve and MA model Cement content (Kg/m3 )

IRC:SP:68 OMC (%)

MDD (Kg/m3 )

A/C

W/C

200

4.50

2497

10.5

0.54

250

4.6

2471

8.4

0.45

300

4.9

2473

6.9

0.40

350

5.0

2421

6.0

0.37

MA model (q = 0.32) 200

5.5

2398

10.0

0.60

250

5.7

2424

8.0

0.54

300

6.2

2440

6.6

0.50

350

6.3

2395

5.7

0.45

Mixture proportions

IRC:SP:68-2005

MA model

Cement content (kg/m3 )

Water content (kg/m3 )

Coarse aggregate (kg/m3 )

Fine aggregate (kg/m3 )

200

108

1502

601

250

114

1424

538

300

123

1379

490

350

129

1297

425

200

119

1049

953

250

137

1020

922

300

151

990

891

350

157

924

826

Concrete mix prepration

• Two minutes of dry mixing of aggregate • Addition of cement and 40% water followed by mixing for 2-minutes • Addition of remaining water and mixing for 4 minutes

Filling concrete in molds (4-layers)

•Filling up of concrete in mold upto 1/4th level. •Tamping to ensure even distribution of concrete and make surface fairly horizontal

Compaction using Vibratory hammer

• Compaction until a mortar ring is formed in the annular space • Vibration is stopped after 20s even if complete mortar ring is not formed and next layer of concrete is added

Fig. 4 Procedure for mixing and casting of specimens

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3 Method of Testing The procedure involved in determining the packing density, optimum moisture content (OMC), maximum dry density (MDD), and compressive strength of RCCP mixtures is discussed in the following subsection.

3.1 Packing Density The experimental packing density of different aggregate blends was determined using the vebe apparatus, as demonstrated by Moini et al. [22]. Total weight of 5 kg was taken, and the aggregates were mixed homogeneously. The entire sample was poured into the cylindrical mold of the vebe apparatus. The surface of the sample was made horizontal, and the disk was placed on the top of the surface. Initial height was recorded before subjecting the sample to vibrations for 45 s with a surcharge of 50 lbs (22.7 kg). After vibrating the sample, the change in the height of the sample was recorded, and densities corresponding to loose and compacted conditions were calculated using Eq. 2 [22]. The average reading of 3 samples was used to define the loose and compacted packing density of the blended mix (IRC: SP: 68 mixes and MA model mixes) Bulk density(γ ) =

4000 W π D 2 (H − h)

(2)

where γ = bulk packing density of combined aggregates (in loose or compacted conditions) (kg/m3 ); W = mass of the aggregate blend (kg); H = initial height of the container (mm); D = diameter of the cylindrical container (mm); and h = height reduction after compaction (mm).

3.2 OMC and MDD Determining the OMC for each mix ensures that it can be compacted to its maximum dry density in the field, which offers better mechanical properties. The OMC and MDD for various cement dosages were determined using the modified proctor test as per IS-2720-Part-8 [20].

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3.3 Vebe Consistency The consistency of RCCP mixes was determined according to the ASTM C1170-14 [23] using the vibrating table with the surcharge load. The vebe consistency time is the qualitative term, which indicates the time required to form a mortar ring along the periphery of the plastic plate during the consolidation of zero-slump concrete. Additionally, two procedures (A and B) are available in ASTM C1170-14 [23] to estimate the vebe consistency time based on the stiffness of RCCP mixtures. In procedure A, a surcharge weight of 22.7 kg shall be used when the consistency of concrete is very stiff to extremely dry, whereas, in procedure B, a surcharge weight of 12.5 kg shall be used when the consistency of concrete is stiff to very stiff but not extremely dry [23]. Additionally, three kinds of consistency are defined in the ASTM C1170 [23] such as stiff consistency, when vebe time ranges between 5 and 20 s with procedure B; very stiff consistency, when vebe time ranges between 20 and 30 s and measured with procedure A or B; and extremely dry consistency when vebe time is greater than 30 s following procedure A.

3.4 Compressive Strength Testing Method The average of three cylindrical specimens was used to define the 7-day compressive strength of the mix. Total of 24 cylindrical specimens were prepared for IRC: SP: 68 blending curve and MA model. The compressive strength of the cylindrical specimen was performed as per ASTM C39 [24] with a loading/stress rate of 0.25 MPa/s.

4 Result and Discussions 4.1 Effect of Gradation on the Packing Density The aggregates of CA20, CA10, CA4.75, and sand were blended in proportion to achieve the particle size distribution of the IRC blending chart and MA model, as depicted in Fig. 5. The proportion of coarse aggregates in IRC: SP:68-2005 was 30% higher than the MA model. In contrast, the proportion of fine sand particles in the MA model was 33% higher than IRC SP:68-2005, which indicates that the IRC blending curve is coarser than the MA model. Meanwhile, the experimental packing density of the IRC blending curve and modified Andreassen was determined using modified vebe apparatus. The obtained loose and compacted experimental packing density for two different gradations is shown in Table 3. The IRC: SP:68-2005 blending curve attained the higher packing density of 0.77, which could be due to reduced interference effect (interference effect is the reduction in the packing density of coarser dominant grains due to entrapment of finer particles

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Fig. 5 Proportion of different classes of natural aggregate in different gradations

Table 3 Blend packing density for different gradations Packing density of aggregate blend Gradation

Loose

Compacted

Bulk density (kg/m3 )

Packing density

Bulk density (kg/m3 )

Packing density

IRC:SP:68-2005

1704

0.62

2099

0.77

MA model (q = 0.32)

1712

0.62

1990

0.74

in the voids of the coarser matrix that disturb the packing of coarser particles, or prevention of one complete layer formation of finer particles on the gap of coarse particles in finer dominant grains). It might also be attributed to the improved critical cavity effect (finer aggregates fill voids of the coarser aggregates without disturbing their arrangement). However, the MA model mix achieved a packing density of 0.74; this reduction in packing density compared to IRC gradation could be due to the loosening effect caused by the finer gradation. Notably, the packing density obtained in IRC: SP:68-2005 was 2.6% higher than the MA model, indicating that the better aggregate packing in IRC: SP:68-2005 was due to better filling of coarser aggregates voids by finer materials than the MA model.

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4.2 Effect of Gradation, W/C, and A/C Ratio on Vebe Consistency The consistency of RCCP mixtures was determined according to the ASTM C1170 [23], wherein the concrete mixtures were classified between stiff and extreme dry mix based on the vebe time. The consistency of RCCP was generally governed by several parameters like gradation, binder content, and cement content [4, 25]. Therefore, in this study, comprehensive research was conducted by varying the above factors. Table 4 shows the variation in the consistency of the mix with different W/C ratios and the A/C ratios for the current study. From Table 4, it can be observed that the vebe consistency of the IRC: SP:68-2005 mix varies from stiff to extremely dry consistency, whereas MA model mix exhibits a stiff to a very stiff consistency. The consistency results were found to be highly sensitive to the water-cement ratio. For instance, it can be noticed from Table 4 that if the water-cement ratio was greater than 0.5, the resulting mix was stiff consistency. Similarly, for the mix with a water-cement ratio between 0.45 and 0.4, the mix has a very stiff consistency, and extremely dry consistency was observed for the mix with a water-cement ratio of 0.37. The very stiffer consistency of the IRC mix could be attributed to its higher packing density when compared with the MA model mix. Overall, it can be concluded that the water-cement ratio, along with gradation, could be deemed to be a significant parameter affecting the consistency of RCCP mixes. This finding depicts that the selection of water-cement ratio shall not be based on the strength properties but also depends on the consistency of RCCP mixes. Table 4 depicts that the changes in the A/C ratio showed a least effect on the consistency of the RCCP mix. For instance, at a fairly same aggregate-cement ratio (IRC mix-6.9 and MA model mix-6.6) and constant cement content (300 kg/m3 ), the IRC: SP:68-2005 gradation results in a very stiff mix, whereas the MA model mix exhibited a stiff consistency. Moreover, it can be observed from Table 4 that at the Table 4 Consistency results and percentage decrease in the aggregate-cement ratio IRC: SP:68 Cement content (kg/m3 )

A/C

% Decrease

W/C

Vebe time (s)

Vebe consistency

200

10.5

–

0.54

15 (B)

Stiff

250

8.4

−20.000

0.45

22 (B)

Very stiff

300

6.9

−16.67

0.40

27 (A)

Very stiff

350

6.0

−14.30

0.37

56 (A)

Extremely dry

MA model 200

10.0

–

0.60

18 (B)

Stiff

250

8.0

−20.00

0.54

14 (B)

Stiff

300

6.6

−16.67

0.50

8 (B)

Stiff

350

5.7

−14.30

0.45

27.5(A)

Very stiff

Note: Character A or B in vebe time indicates the procedure used for consistency measurement

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same percentage decrease in aggregate-cement ratio, different consistency results were observed for the IRC mix and MA model mix. For instance, at 300 kg/m3 of cement content, a similar percentage decrease (16%) in aggregate-cement ratio caused a very stiff consistency mix in the IRC gradation and a stiff consistency mix in the MA model mix. From the above discussion, it can be concluded that the W/C ratio primarily governs consistency as compared to the A/C ratio.

4.3 Effect of Water-Cement Ratio and Packing Density on the Compressive Strength The W/C ratio is an important parameter, which significantly influences the compactability and mechanical properties of the RCCP mixtures [15]. For instance, a lower W/C ratio could cause inadequate compaction, whereas higher W/C ratio results in uneven compaction due to the heaving of concrete mixtures in front of roller wheels [4]. Therefore, to understand the true picture of the W/C ratio on RCCP performance and to validate Abram’s law, the W/C ratios were varied between the (0.36–0.60) with two different gradations. Figures 6 and 7 show that irrespective of the gradation type, the decrease in the W/C ratio increased the compressive strength. This could be attributed to the reduction in the free water content, which reduces the porosity and thereby increases the compressive strength of the RCCP mix. On the contrary, Hazaree et al. [26] and Qasrawi et al. [27] observed a parabolic behavior when the W/C ratio decreased from 1.27 to 0.27.

Fig. 6 W/C ratio and compressive strength for IRC SP:68

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Fig. 7 W/C ratio and compressive strength for MA Model

Table 5 shows the effect of the water-cement ratio on compressive strength for two different gradations with the supplementary results of the percentage decrease in the water-cement ratio and the corresponding increase in compressive strength. It can be observed that for cement content of 350 kg/m3 , a 10% decrease in watercement ratio value caused a 41% increase in compressive strength of IRC mix, despite a corresponding reduction in density of 0.35%. However, only a 10% increase in compressive strength was observed for MA mix with the same 10% decrease in watercement ratio, even with a 1.52% increase in corresponding density. The dissimilar Table 5 Percentage increment in W/C ratio and compressive strength IRC SP: 68 Cement content (kg/m3 )

W/C

% Increment

Density (kg/m3 )

% Increment

Compressive strength (MPa)

% Increment

200

0.54

–

2469

–

19

–

250

0.45

−16.0

2540

2.90

31

59.0

300

0.40

−10.1

2583

1.70

36

17.2

350

0.37

−10.0

2574

−0.35

51

41.6

MA model 200

0.60

–

2519

–

22

–

250

0.54

−8.5

2542

0.90

27

25.2

300

0.50

−7.8

2512

−1.15

28

0.67

350

0.45

−10.7

2551

1.50

31

10.45

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increase in strength indicates that compressive strength was dependent on the absolute value of the water-cement ratio rather than the variation in the percentage. Also, the aforementioned data suggest that compressive strength was more closely related to the water-cement ratio.

4.4 Effect of Aggregate-Cement Ratio (A/C) on the Compressive Strength The volume occupied by aggregate in RCCP is generally 10–15% higher than the conventional mixtures [4]. Subsequently, the quantity of binder required to fill the aggregate skeleton’s voids and surface coating of aggregate could be reduced considerably. As a result, the requirement of binder content could be lower in RCCP, which may lower the shrinkage and its associated cracking [4, 28]. As a result, the A/C ratio in RCCP could be deemed to be higher than in conventional mixtures. However, the optimum range of A/C for RCCP has not been known well due to the paucity of literature. Therefore, an attempt was made to determine the desired range of the A/C ratio by varying its ratio between 5.7 and 10.5. Figures 8 and 9 depict the relationship between water-cement ratio, aggregate/cement ratio, and compressive strength of IRC and MA model mix. It can be observed from Figs. 8 and 9 that variation of aggregate-cement ratio and water-cement ratio follows a similar trend in both the IRC blending curve and MA model. From Table 6, it can be seen that irrespective of gradation type, the strength of RCCP mixtures increases with the decrease in the A/C ratio. This could be primarily caused by the better lubrication imparted by the

Fig. 8 Relationship between W/C ratio, A/C ratio, and compressive strength (IRC SP:68)

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Fig. 9 Relationship between W/C ratio, A/C ratio, and compressive strength (MA Model)

Table 6 Percentage increment in A/C ratio and compressive strength IRC SP: 68 Cement content (kg/m3 )

W/C

% Increment

Density (kg/m3 )

% Increment

Compressive strength (MPa)

% Increment

200

0.54

–

2469

–

19

–

250

0.45

−16

2540

2.90

31

59.0

300

0.40

−10

2583

1.70

36

17.2

350

0.37

−10

2574

−0.35

51

41.6

MA model 200

0.60

–

2519

–

22

–

250

0.54

−8.5

2542

0.90

27

25.2

300

0.50

−7.8

2512

−1.15

28

0.67

350

0.45

−10.7

2551

1.50

31

10.45

increased cement volume and thus reduces the internal friction between the aggregates. Besides, it also provides a homogeneous RCCP mixture. Similar behavior was also observed by other researchers also [15]. However, as the cement volume was reduced, the compressive strength was negatively affected due to the increased internal friction between the aggregate, which offers more resistance to compaction. From Figs. 8 and 9, it can be concluded that to produce an RCCP with higher strength, the desirable range of A/C ratio is 5.7–6.0. However, further studies are required to determine the influence of aggregate morphology like angularity, roundness, and surface texture on the compressive strength.

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Table 7 Pearson’s coefficient of correlation between W/C, A/C ratio, and compressive strength for IRC SP:68 and MA model Parameter

IRC: SP:68-2005

MA model

W/C ratio

Compressive strength

W/C ratio

Compressive strength

W/C ratio

1

−0.96

1

−0.94

Compressive strength

−0.96

1

−0.94

1

A/C ratio

W/C ratio

A/C ratio

W/C ratio

A/C ratio

1

0.99

1

0.98

W/C ratio

0.99

1

0.98

1

A/C ratio

Compressive strength

A/C ratio

Compressive strength

A/C ratio

1

−0.96

1

−0.96

Compressive strength

−0.96

1

−0.96

1

4.5 Statistical Analysis A statistical correlation analysis was carried out with Pearson’s correlation coefficient to evaluate the effect of W/C and A/C ratio on the early-age compressive strength. Pearson’s coefficient statistically estimates the linear relationship strength between the variables. For instance, “+1 defines a positive relationship between the variables,” whereas “−1 states that negative relationship exists between the variables.” Table 7 shows that a higher correlation exists between the water-cement ratio and compressive strength, viz. −0.96 for IRC mix and −0.94 for modified Andreassen. A higher negative correlation value indicates that the decrease in W/C ratio could increase compressive strength. Also, a higher correlation of 0.99 and 0.98 was observed between the water-cement and aggregate-cement ratios, as illustrated in Table 7. A perfect correlation was observed between the water-cement ratio and aggregate-cement ratio. This might be due to the increase in cement content which could lower both the water-cement and aggregate-cement ratios, respectively.

5 Summary and Conclusion This study focused on determining the optimized gradation of RCCP for Indian conditions by considering the IRC blending curve and MA models. In addition, the influence of packing density, consistency, A/C ratio, and W/C ratio on the early-age behavior of RCCP was determined. The conclusions that can be drawn from the experimental study are summarized below. 1.

The current research study determined that IRC: SP:68-2005 performed better than MA model due to the higher compactness. Also, higher compressive strength could be achieved in IRC gradation at lower binder content and W/C ratio.

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2.

Irrespective of the type of gradation, the consistency of RCCP is mostly governed by the W/C ratio compared to the A/C ratio. The packing density of aggregate mixtures was found to be a better way of quantifying the effect of gradations on the mechanical properties of RCCP. At a similar A/C ratio, the RCCP designed with IRC gradation achieved higher compressive strength and MDD of about 12–67% and 1–4%, than modified Andreassen. The desirable range of W/C and A/C ratio for RCCP was found to be 0.36–0.40 and 5.7–6. The obtained compressive strength results showed that the minimum strength criteria suggested by the Portland Concrete Association (PCA) for the constructions of RCCP at 28 days (27.6 MPa) could be achieved with IRC gradation at 7 days with a higher safety factor.

3. 4.

5. 6.

The findings from the present study depict that IRC: SP:68-2005 blending chart could achieve a higher packing density and early-age compressive strength at lower binder content than MA models. Also, this analysis shows that employing the IRC gradation could reduce traffic opening time and subsequently decrease the traffic delay costs for RCCP. However, further exhaustive comparative studies are required to determine the desirable gradation that yields maximum compactness and better workability among other standardized gradations (Portland Concrete Association, American Concrete Institute, and American Concrete Pavement Association). Acknowledgements The authors would like to acknowledge and thank the funding received from the Indian Institute of Technology Madras, Chennai, India, under the project number: SB20210809CEMHRD008100.

References 1. Vahedifard F, Nili M, Meehan CL (2010) Assessing the effects of supplementary cementitious materials on the performance of low-cement roller compacted concrete pavement. Constr Build Mater (24):2528–2535 2. Khayat KH, Libre NA (2014) Roller compacted concrete: field evaluation and mixture 3. Abut Y, Yildirim ST, Ozturk O, Ozyurt N (2020) A comparative study on the performance of RCC for pavements casted in laboratory and field. Int J Pavement Eng 1–14 4. Harrington D, Abdo F, Adaska W, Hazaree C (2010) Guide for roller-compacted concrete pavements. Institute for Transportation, Iowa State Univerisity 5. Delatte N, Storey C (2005) Effects of density and mixture proportions on freeze-thaw durability of roller compacted concrete pavement. Transp Res Rec 45–52 6. Amer N, Delatte N, Storey C (2003) Using gyratory compaction to investigate density and mechanical properties of roller-compacted concrete. Transp Res Rec 77–84 7. Williams SG (2014) Construction of roller-compacted concrete pavement in the fayetteville shale play area. Arkansas Transp Res Rec 2408(1):47 8. Chhorn C, Woo Lee S (2018) Influencing compressive strength of roller-compacted concrete. Proc Inst Civ Eng Constr Mater 171:3–10

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9. IRC: SP:68-2005 (2005) Guidelines for construction of roller compacted concrete pavements. Indian Roads Congress, New Delhi, India 10. Kumar SV, Santhanam M (2003) Particle packing theories and their application in concrete mixture proportioning: a review. Indian Concr J 77:1324–1331 11. Reed JS (1995) Principles of ceramics processing, 2nd edn. Wiley, New York 12. Rahmani E, Sharbatdar MK, Beygi MHA (2020) The effect of water-to-cement ratio on the fracture behaviors and ductility of Roller Compacted Concrete Pavement (RCCP). Theor Appl Fract Mech (109):102753 13. Osipov AD, Sharkunov SV, Semenenok SN, Magiton AS (1992) Roller compacted concrete with high values of strength, frost resistance, and impermeability. Hydrotech Constr (26):400– 404. https://doi.org/10.1007/BF01545014 14. Sengün ¸ E, Alam B, Shabani R, Yaman IO (2019) The effects of compaction methods and mix parameters on the properties of roller compacted concrete mixtures. Constr Build Mater 228 15. Rahmani E, Sharbatdar MK, Beygi MHA (2020) A comprehensive investigation into the effect of water to cement ratios and cement contents on the physical and mechanical properties of roller compacted concrete pavement (RCCP). Constr Build Mater 253–19177 16. IS 4031-Part-11 (1993) Methods of physical tests for hydraulic cement—determination of density. Bureau of Indian Standards, New Delhi, India 17. IS 4031-Part-2 (1993) Methods of physical tests for hydraulic cement—determination of fineness of specific surface by Blaine air permeability method. Bureau of Indian Standards, New Delhi, India 18. IS 2386-Part-3 (1993) Methods of test for aggregates for concrete—specific gravity, density, voids, absorption and bulking. Bureau of Indian Standards, New Delhi, India 19. IS 2386-Part-4 (1993) Methods of test for aggregates for concrete—mechanical properties. Bureau of Indian Standards, New Delhi, India 20. IS:2720-Part-8 (1993) Determination of water content—dry density relation using heavy compaction. Bureau of Indian Standards, New Delhi, India 21. ASTM C1435 (2014) Standard practice for molding roller-compacted concrete in cylinder molds using a vibrating hammer. ASTM International, PA 22. Moini M, Sobolev K, Flores-Vivian I, Amirjanov A (2019) Modeling and experimental evaluation of aggregate packing for effective application in concrete. J Mater Civ Eng 31:1–10 23. ASTM C1170 (2014) Standard test method for determining consistency and density of rollercompacted concrete using a vibrating table. ASTM International, PA 24. ASTM C39 (2018) Standard test method for compressive strength of cylindrical concrete specimens. ASTM International, PA 25. Chhorn C, Lee SW (2017) Consistency control of roller-compacted concrete for pavement. KSCE J Civ Eng 21:1757–1763 26. Hazaree C, Ceylan H, Wang K (2011) Influences of mixture composition on properties and freeze-thaw resistance of RCC. Constr Build Mater 25:313–319. https://doi.org/10.1016/j.con buildmat.2010.06.023 27. Qasrawi HY, Asi IM, Wahhab HIAA (2005) Proportioning RCCP mixes under hot weather conditions for a specified tensile strength. Cem Concr Res 35:267–276. https://doi.org/10.1016/ j.cemconres.2004.04.030 28. Marchand J, Gagné R, Ouellet E, Lepage S (1997) Mixture proportioning of roller compacted concrete—a review. Concr Technol Spec Publ SP-171e22:457–487

Experimental Investigation on Aging Behavior of Bitumen Mastic with Hydrated Lime Using FTIR Spectroscopy K. L. A. V. Harnadh, M. R. Nivitha, and A. Padmarekha

Abstract To reduce moisture damage, anti-stripping agents are commonly used for the surface course of bituminous pavements. While a wide variety of anti-stripping agents are currently available, hydrated lime (HL) is widely used. The present study focused on studying the aging characteristics of bitumen and hydrated lime-treated mastic by using FTIR spectroscopy. HL was added to bitumen in contents of 20, 30, and 40% weight. The HL-treated mastics were oxidized at a constant temperature of 100 °C by varying the aging time. HL-treated mastics were evaluated for their aging compounds using FTIR spectroscopy. Results of obtained unaged and aged spectra were analyzed by calculating carbonyl and sulfoxide indices. Analysis of the spectra of aged mastic exhibited that the carbonyl indices increase with aging although at different rates depending on the aging duration and the dosage of lime. The sulfoxide compounds however exhibited very less sensitivity to duration of aging and dosage. Keywords Hydrated lime · HL-treated mastic · Carbonyl · Sulfoxide indices

1 Introduction Moisture damage (MD) is a primary distress in bituminous pavements, and it is influenced by the properties of individual constituents such as bitumen and aggregates and their interactions. Here, bitumen is used as the binder to bind aggregates of different gradations. As water ingresses into the bituminous mixture, it strips the film of binder coating the aggregates, thereby leading to de-bonding between the aggregates. The moisture damage is said to initiate a number of distresses in bituminous pavements such as potholes and raveling and increases the rate of crack propagation. To mitigate these issues and promote adhesion between the binder and aggregates, K. L. A. V. Harnadh (B) · A. Padmarekha Department of Civil Engineering, SRM University, Kattankulathur 603203, India e-mail: [email protected] M. R. Nivitha Department of Civil Engineering, PSG College of Technology, Coimbatore 641004, India © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_6

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anti-stripping agents are used. Few commonly used anti-stripping agents include hydrated lime (HL), amide-amines, polyamines, polyamides, and organo-metallics [1]. Among these, HL is generally preferred as it is readily available and contributes to the increase in stability of the bituminous mixture [2]. HL is mainly composed of calcium hydroxide, Ca(OH)2 . It is obtained by hydrating quicklime (calcium oxide, CaO) using specific equipment called hydrators. In addition, HL also contains magnesium oxide with silica, nitrites, and iron oxide in smaller proportions. HL is generally available in the market in the form of dry white powder with a relative density of 2.2 mg/m3 [3]. Lime is generally classified into four types namely CL90, CL80, and CL70 based on the percentage of calcium oxide present in the lime. The calcium oxide present in these cases is 90%, 80%, and 70%, respectively. Higher calcium oxide content is directly correlated to better anti-stripping properties [4]. The silica content present in lime is generally about 1–5%. The silica content is also seen to increase the stiffness of the mastic [2]. When HL added to bitumen does not act as inert filler but reacts with specific constituents in bitumen. The addition of HL creates a chemical reaction between the calcium oxide (CaO) present in lime and highly reactive polar molecules such as oxygen, nitrogen, and sulfur containing functionalities present in bitumen. The calcium ions present in HL react with the acidic moieties present in bitumen which leads to the swelling in the mastic. This phenomenon makes the mastic stiffer and improves its resistance to water absorption. This interaction between lime and bitumen is termed to be physical in this case [5]. Few other chemical interactions have also been proposed for the interaction of bitumen with lime [6]. The influence of HL at different proportions on the properties of bitumen has been studied using physical and chemical interactions between HL and bitumen [7]. HL when added to the bituminous mixture enhances the high temperature performance of the bituminous mixtures. An increase in softening point, ductility, and viscosity at 60 °C are observed on the addition of lime to bitumen. Addition of hydrated lime in bituminous mixtures helps to distribute and reduce the stresses and strain in the pavement structure created by traffic loads and reduces rutting [8]. The author’s concluded that the addition of HL to the bituminous mixture improved the permanent deformation characteristics and fatigue life of the material and decreased its moisture susceptibility [9]. The addition of hydrated lime to bitumen has also been observed to reduce the aging susceptibility of bitumen. The interaction between the magnesium and calcium oxide with bitumen neutralizes the reactivity of the polar molecules and thus reduces the aging properties of the mastic. Since the polar molecules are said to be the major points of oxidation in bitumen; neutralization of such compounds leads to overall slower aging kinetics [8]. HL also neutralizes the oxidative products such as ketones, anhydrides, and carboxylic acids that formed after aging, thus preventing the polar molecules from further reaction. This effect keeps bitumen from hardening excessively and from becoming highly susceptible to fatigue and low temperature cracking [8, 10]. At higher temperatures (85, 100, and 110 °C), during aging, the adsorption of HL particles into the acids of bitumen gets delayed, which in turn slows

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down the oxidation kinetics [2, 5]. 20 or 30% of HL was found to be effective to reduce the aging of bituminous mixture [11]. The chemical changes of bitumen and modified bitumen are commonly studied using Fourier transform infrared (FTIR) spectroscopy. Fourier transform infrared spectroscopy is used to identify and quantify the amounts of specific functional groups present in the bitumen [12]. The infrared spectrum sends infrared radiation through a bitumen film sample guided through an interferometer, and the transmitted radiation is detected by the detector after passing through the sample. Based on the percentage of transmission, absorbance is obtained as a spectrum. Carbonyls and sulfoxides are the major products formed in bitumen after aging [13]. FTIR has been increasingly used to investigate changes in the chemical composition due to oxidative aging. In most of the studies discussed, the influence of carbonyl and sulfoxide compounds on the aging properties of the unmodified and modified bitumen has been studied [14]. Bitumen spectra show two peaks vibrating at 3000–2800 cm−1 ; this region corresponds to asymmetric and symmetric stretches of C−H in CH3 and CH2 . The other significant aromatics peak at 1600 cm−1 corresponding to the C=C. Carbonyl (C=O) region is observed at 1700 cm−1 , sulfoxides (S=O) are observed at 1032 cm−1 . The prominent peaks in HL area sharp peak of hydroxyl group at 3641 cm−1 corresponding to unsaturated single bond stretch (O–H), peak at 1390 cm−1 (C–O) indicating the presence of calcium carbonate in lime, and another peak at 866 cm−1 due to the presence of thiols [15]. On the aging of HL mastic at a temperature of 100 °C, a reduction in the intensity of the hydroxyl group (O–H) at 3641 cm−1 was observed [2]. HL being added to the bituminous mixture varying from 1 to 20% of HL dosages, it is not known how theses dosages influence the aging of mastic. To understand how the stiffness changes with the aging of mastic with different dosages of HL, it is necessary to understand the aging behavior of mastic with different dosages of HL. This study aims to estimate the effect of the dosage of lime on the aging products observed in lime mastic. For this purpose, unaged bitumen and lime mastic prepared at different dosages of lime were tested using FTIR spectroscopy to quantify the aging products for different aging durations. Many works of literatures are available that have focused on interaction of bitumen with HL but very few have focused on the influence of HL on the rate of aging by tracking the variability of different functional groups in mastic. This research work aims to quantify the oxidation products namely carbonyl and sulfoxide that formed after aging.

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2 Experimental Investigation 2.1 Materials The base binder VG30 was obtained from Hindustan Petroleum Corporation Ltd, Mangalore refinery [16]. Hydrated lime was purchased from Lime industries, Chennai, and HL was added at 20, 30, and 40% by weight of bitumen. Mastic samples were prepared by manual mixing. Initially, VG30 bitumen was taken in a blending container and preheated in oven for 5 min at 90 °C. Then, the container was placed on the hot plate and the temperature was maintained at 160 °C. Once the bitumen melts, a stirrer was used to mix the bitumen and an accurate mass of hydrated lime was added slowly while the stirring was continued. The mixing temperature was maintained at 160 °C for 15–20 min. The stirring has to be continued till the hydrated lime particles are blended with the binder, which produces a homogenous mix that prevents the settling of HL at bottom of the mastic. This time was determined through various trials conducted at the laboratory, and a constant mixing time of 20 min was adopted for all dosages. Then, mastic was transferred to different vessels to be saved for later testing. To get the repeatability in experiments, HL-treated mastic was heated to 90 °C before starting the experiments to ensure the homogeneity of the samples [6].

2.2 Mastic Aging Aging mechanism purely depends on crude source and chemical composition of particular bitumen. It consists of various elements which vary from one another. Bitumen ages rapidly due to high temperatures and exposure to atmospheric oxygen; this oxidation process leads to change in physiochemical properties like surge in stiffens of bitumen [17]. The hydrated lime particles act as catalyst to develop the reaction in bitumen and enhance the bitumen properties which was discussed in section to simulate the aging characteristics on mastic by variating time and temperature [18]. Five aging conditions were chosen to age the HL-treated mastic. Aging was performed with an air-circulated oven for 10, 20, 30, 40, and 50 h at 100 °C with 20, 30, and 40% of hydrated lime to find the oxidation products. The mastics obtained from the aging were used to prepare the specimens to test in FTIR. Aging of mastic can be tracked by the formation of carbonyl and sulfoxide compounds, which are the major functional groups formed during aging.

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2.3 FTIR Characterization Hydrated lime-treated bitumen samples were prepared by dissolving in the THF (Tetrahydrofuran) at a concentration of 5% weight by volume. THF eliminated the interference from hydrogen bonding and determined the functional groups like carbonyls and sulfoxides. 10 µl of THF solution was placed on the KBr disk, and the solvent was allowed to evaporate before recording the spectra. To avoid the moisture peaks in the infrared spectrums, KBr disk should not contain any moisture and for that samples were kept under a sodium vapor lamp for 10–15 min before placing it in the FTIR spectrometer. The FTIR spectroscopy experiments were performed by SHIMADZU, IRTRACER 100. The spectrums were recorded from 4000 to 400 cm−1 at a resolution of 4 cm−1 averaging 32 scans for each measurement.

3 Result and Analysis 3.1 Prominent Peaks in Bitumen and Lime From the FTIR spectroscopy, the spectra of the VG30 binder and hydrated lime obtained are shown in Figs. 1 and 2, respectively. A baseline correction was carried out on the spectra with Origin 9 Pro software using the peak analyzer option. Here, the spectra were kept in manual mode and the number of points required for fitting the baseline correction was selected with the Savitzky–Golay function. Normalization of the absorption spectrum is carried out to remove the influence of the varied sample size. Peaks of the required functional indices were identified, and the area was calculated by using multiple fit options in the origin software [14]. Though multiple peaks are observed in the spectra as shown in Fig. 1, only carbonyl and sulfoxide peaks are used to analyze the aging characteristics of HL-treated mastic.

Fig. 1 Peaks in unaged VG30 binder spectrum

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Fig. 2 Spectra of hydrated lime

Table 1 Functional groups and peaks of VG30 binder and hydrated lime

Material

Wavenumber (cm−1 )

Functional group

Peak (cm−1 )

VG30

3080–2895

Methyl (CH3 )

2970

2891–2830

Methylene (CH2 )

2857

1648–1545

Aromatics

1597

1506–1411

Aliphatic

1453

1404–1231

Carbonyl group (C–H)

1373

1101–1026

Sulfoxides (S=O)

1067

742–675

Methylene (CH2 ), rocking

710

905–800

Thiols (C–S)

872

1567–1370

Aliphatic

1411

1835–1734

Carbonates

1804

3668–3600

Hydroxyl group

3641

HL

The spectra for hydrated lime are shown in Fig. 2. The hydroxyl group (O– H) is an unsaturated single bond, and it exhibits a broad peak 3641 cm−1 . This group interacts with the polar aromatics and asphaltene molecules and delays the oxidation process [17]. Carbonate exhibited a saturated weak peak that occurred at 1809 cm−1 [15]. Aliphatics exhibited a strong peak at 1411 cm−1 . Unsaturated singlebonded thiols exhibited a peak at 872 cm−1 , and methylene (CH2 ) was absorbed in 710 cm−1 corresponding to rocking vibration. Table 1 summarizes the peaks observed for bitumen and hydrated lime.

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Aging product

Carbonyl

Sulfoxide

Hydroxyl group

Aging index

A1732−1695  Ai

A1101−1026  Ai

A3668−3600  Ai

3.2 Calculation of Aging Indices To quantify the effect of aging for HL-treated mastic, aging indices were calculated from the FTIR spectra as shown in Table 2. The aging indices were calculated for each sample tested using Eq. 1. Ai Area(%) =  × 100 Ai

(1)

where Ai 

Ai

is the area of the particular oxidative product calculated from the spectra. is the sum of all the peaks area in the particular spectra.

Using the aging indices, one can understand how modifiers or additives can influence the treated mastic. Aging compounds like carbonyls, sulfoxides, and hydroxyl groups formed after the addition of HL to the bitumen and their influence on the treated mastic are discussed in the next section.

3.3 Aging Compounds in Lime Mastic The spectrum of unaged VG30 compared with that of unaged mastic with 20% of HL is shown in Fig. 3a. It can be observed from the figure that a reactive functional compound, hydroxyl group (O–H), has shown with low intensity peak at 3641 cm−1 in the mastic spectra indicating the presence of oxygen containing compounds. The absorption peaks of the carbonyl group (C=O) occurred at 1741 cm−1 . However, the rate of change of peaks was variated as the lime dosage increases. The peak corresponds to the sulfoxide (S=O) obtained a sharp peak at 1065 cm−1 . The absorption of this peak increases as the aging time variates. From Fig. 3b, 30 h aged bitumen is compared with the three dosages 20, 30, and 40% of HL aged at 30 h. Carbonyl formation change with the increase in aging time, and at 20% of HL, clear hike was observed. But in sulfoxides, as the aging time increases, the sulfoxides increased the characteristic absorption of peaks and functional groups have similar wavenumbers. However, as the dosage of HL increases, sample intensity and areas are quite different. By using the deconvolution method in Origin Pro 9 software, the peak positions were assigned based on the second derivative of the spectra.

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Fig. 3 a Spectra of unaged VG30 and unaged mastic with 20% of lime. b Spectra of VG30 and three dosages of mastic at 30 h of aging condition

4 Oxidation Compounds 4.1 Carbonyl Index Figure 4 shows the carbonyl formation from 0 to 50 h with bitumen and 3 dosages of HL. Aged spectra have shown the carbonyl region (C=O) with a low intensity peak

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Fig. 4 Carbonyl index for different aging conditions

at 1707 cm−1 . In the untreated bitumen (VG30), aging index values have increased from 0.080 to 0.261 (0–30 h) and then reduced. At 20% addition of HL, carbonyl indices at unaged conditions are high for lime mastic when compared to the untreated bitumen. This is due to the carbonates already present in lime. Upon aging, it is seen that the carbonates have reduced even for 10 h of aging, and then, it almost remained constant fluctuating over a small range for further aging conditions (10–50 h). With 30% of HL, the carbonyl index has reduced from unaged condition to 10H but then a sudden spike is seen at 20 h of aging. However, it gradually decreases from 30, 40, and 50 h. It is understood that high concentrations of HL neutralize the polar molecule’s reaction in bitumen and resulting in the increasing of the bitumen molecular weight leads to an increase in stiffness of the mastic [19, 20]. For the addition of HL with 40% dosage, a spike in carbonyl index is observed even at 10 h of aging but it then reduces gradually further on. It is evident that the formation of carbonyl compounds is significantly dependent on the dosage. There is scarcely any hypothesis available relating to the sudden spike in carbonyl index and its shift with aging duration depending on the dosage. Additional investigation has to be performed to verify the same.

4.2 Sulfoxide Index The formation of sulfoxides, the other dominant oxidation product, has been shown to result from the aging of organic sulfides that are part of complex asphalt molecules. These sulfides are highly reactive [21, 22]. Sulfoxides have exhibited sharp peaks at 1065 cm−1 (S=O) in the aged spectra. Figure 5 shows that the sulfoxide concentration in the untreated bitumen and mastic at three dosages is almost identical. There are no sulfur compounds present in lime, and the addition of lime to bitumen also does

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Fig. 5 Sulfoxide index for different aging conditions

not result in the formation of any new sulfoxide compound. This is the reason for identical sulfoxide concentration in lime mastic and bitumen. However, as the dosage increases, the sulfoxide concentration is much higher in mastic when compared to bitumen. Sulfoxide formation at 30% peak value achieved at 30 h (12.40) of oxidation. At 40% of HL, a maximum value was observed at 50 h of aging. Irrespective of the dosage and aging duration, these values are seen to remain constant. This could be because sulfoxides are being formed at a much greater rate than ketones, and most of the hydroperoxides formed during the initial spurt are scavenged by the naturally occurring bitumen sulfides to form sulfoxides [22]. Higher sulfur content in the bitumen produces more sulfoxides. The sensitivity of sulfoxides to thermal decomposition following their formation also significantly affects the sulfoxide content during the long-term aging [7, 18, 23].

5 Conclusions Hydrated lime is generally added to mitigate issues related to moisture damage. One consequence of the usage of this product with bitumen is its tendency to reduce the aging susceptibility of bitumen. The effect of the dosage of lime on the aging characteristics of lime mastic has been scarcely studied. In this study, the changes in the chemical composition of hydrated lime-treated mastic were studied by FTIR spectroscopy and the variability in oxidation compounds was studied at different durations of aging. The major conclusions drawn from this investigation are given below: • The aging of HL-treated bitumen mastics is different from the base bitumen. FTIR spectroscopy showed the carbonyl index for the lime mastic at any given dosage

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• •

•

85

was higher compared to the untreated bitumen. This could be attributed to the carbonates present in lime. As the lime mastic was subjected to aging, it was seen that depending on the dosage of lime, a sudden spike in carbonyl compounds was observed at different aging conditions. The spike was observed at 10H for 40% dosage while it was observed at 20H for 30% dosage of lime. For 20% lime however, no such spike was observed. The sulfoxide index for the lime-treated mastic and the untreated bitumen were observed to be identical. This could be due to the absence of sulfur compounds in lime and the absence of such compounds on the interaction of lime with bitumen. At 10H of aging, the sulfoxide compounds increased substantially. Beyond that, no substantial variation in sulfoxide compounds was observed for 20 and 30% HL at different aging conditions. Only for 40% dosage, a slight reduction in sulfoxide concentration was observed at all aging conditions. From the aged spectrum results, it is understood that as the addition of lime increases, a clear surge was observed on areas and intensities of peaks for carbonyl and sulfoxide compounds.

References 1. Bahia HU, Hanson DI, Zeng M, Zhai H, Khatri MA, Anderson RM (2001) Characterization of modified asphalt binders in super pave mix design (No. Project 9–10 FY’96) pp 1–53 2. Lesueur D, Petit J, Ritter HJ (2013) The mechanisms of hydrated lime modification of asphalt mixtures: a state-of-the-art review. Road Mater Pavement Des 14(1):1–16 3. ASTM (2016) Standard test methods for physical testing of quicklime, hydrated lime, and limestone. ASTM International, C110, West Conshohocken, PA 4. EN45-1 (2015) Building lime—part 1: definitions, specifications and conformity criteria. European committee for standardization, Technical Committee CEN/TC 51, pp 1–15 5. European lime association (2011) Hydrated lime a proven additive for durable asphalt pavements. Asphalt task force, pp 1–68 6. Little DN, Petersen JC (2005) Unique effects of hydrated lime filler on the performance-related properties of asphalt cements: physical and chemical interactions revisited. J Mater Civ Eng 17(2):207–218 7. Branthaver JF, Petersen JC, Robertson RE, Duvall JJ, Kim SS, Harnsberger PM, Mill T, Ensley EK, Barbour FA, Scharbron JF (1993) Binder characterization and evaluation. Volume 2: Chemistry (No. SHRP-A-368), pp 1–290 8. Sebaaly PE, Little DN, Epps JA (2006) The benefits of hydrated lime in hot mix asphalt. National Lime Association, pp 1–13 9. Neham SS (2017) Effect assessment the impact of filler types on the input design parameter of flexible pavements. J Univ Babylon 25.5:1–12 10. Little DN (1996) Hydrated lime as a multi-functional modifier for asphalt mixtures. Presented at the HMA in Europe Lhoist symposium, Brussels, Belgium, pp 1–18 11. Recasens RM et al (2005) Effect of filler on the aging potential of asphalt mixtures. Transp Res Rec 1901:10–17 12. Diab A, Mohassab-Ahmed MY, Prisbrey K, Dai Q, You Z, Wahaballa AM (2015) Do regularand nano-sized hydrated lime have different mechanisms in asphalt. Int J Pavement Res Technol 8(5):363–392

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13. Nivitha MR, Prasad E, Krishnan JM (2016) Ageing in modified bitumen using FTIR spectroscopy. Int J Pavement Eng 17(7):565–577 14. Hou X, Lv S, Chen Z, Xiao F (2018) Applications of Fourier transform infrared spectroscopy technologies on asphalt materials. Measurement 121:304–316 15. Arnold TS, Rozario-Ranasinghe M, Youtcheff J (2006) Determination of lime in hot-mix asphalt. Transp Res Rec 1962(1):113–120 16. IS73:2013 (2013) Indian standard for paving bitumen—fourth revision. Bureau of Indian Standards, New Delhi 17. Tauste R, Moreno-Navarro F, Sol-Sánchez M, Rubio-Gámez MC (2018) Understanding the bitumen ageing phenomenon: a review. Constr Build Mater 192:593–609 18. Petersen JC (2009) A review of the fundamentals of asphalt oxidation: chemical, physicochemical, physical property, and durability relationships. Transp Res Circ E-C140 19. Iwa´nski M, Mazurek G (2013) Hydrated lime as the anti-aging bitumen agent. Procedia Eng 57:424–432 20. Petersen JC, Plancher H, Harnsberger PM (1987) Lime treatment of asphalt to reduce age hardening and improve flow properties. In: Association of asphalt paving technologists proceedings technical sessions, Reno, Nevada, USA, vol 56, pp 1–38 21. Petersen JC (2000) Chemical composition of asphalt as related to asphalt durability. Dev Pet Sci 363–399 22. Alfaqawi RM, Airey GD, Presti DL, Grenfell J (2017) Effects of mineral fillers on bitumen mastic chemistry and rheology. In: Transport infrastructure and systems. Proceedings of the AIIT international congress on transport infrastructure and systems, pp 359–372 23. Plancher H, Green EL, Peterson JC (1976) Reduction of oxidative hardening of asphalt by treatment with hydrated lime—a mechanist study. In: Proceedings association of asphalt paving technologist, vol 45, no 1, pp 1–24

Numerical Analysis of Fiber-Reinforced Whitetopping Pavement Under Wheel Loading S. V. Jisha, M. Satyakumar, and Remya Valsalan

Abstract Ultra-thin Whitetopping (WT) is a technology in which a thickness of less than 100 mm of high early strength concrete pavement overlay is laid on a distressed bituminous pavement as a rehabilitation measure. WT modified by the application of Fiber-Reinforced Concrete (FRC) is adopted in this study. The three-dimensional finite element modeling developed in this study is for four-layered pavement with top fiber-reinforced WT layer, second bituminous layer, third sub-base, and bottom subgrade layer. Thickness and material properties of fiber-reinforced WT layer and bituminous layer are also varied for the parametric study. Loading is applied as per IRC:58-2015 and IRC:6-2010. Three different loading conditions such as interior, corner, and edge are considered, and maximum deflections at those loading positions are found out. The effect of temperature differentials is also studied. It is found that the deflection of fiber-reinforced WT pavement is maximum at slab corner, and it should be 5–10% times higher than edge and interior loading. Keywords Whitetopping · Pavement · Finite element method

1 Introduction Scientifically designed and constructed pavements have long service life. The increase in axile load spectrum has enhanced the need for longer serviceability characteristics. Distresses in bituminous pavements are common owing to varying traffic and environmental conditions. A short-term maintenance strategy to this is to overlay S. V. Jisha (B) · M. Satyakumar · R. Valsalan Department of Civil Engineering, Mar Baselios College of Engineering and Technology, Thiruvanathapuram, India e-mail: [email protected] M. Satyakumar e-mail: [email protected] R. Valsalan e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_7

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the bituminous layer with a concrete layer of normal, thin, and extra thin thicknesses. This technology is known as whitetopping (WT) and usually adapted for immediate maintenance strategies. This will help to enhance the pavement life.

2 Background of the Problem Many researches have been carried out to understand the bond strength between bitumen and concrete through different experimental investigations [1–5]. In these researches, the effect of base, sub-base, and subgrade layers of the pavement is not considered. Bituminous overlay exhibits a more rapid loss of serviceability as compared to concrete white topping, and it is found out from different field investigations [6–8] such as BBD test and FWD test Which are used for the evaluation of structural capacity of existing pavement. Many researchers conducted the Finite Element (FE) analysis of the white topped pavement under wheel loading, and the result shows stresses and deflection induced in the white topping within the safe limits. The subgrade is modeled as Winkler foundation that consists of a bed of closely spaced, independent, spring elements. In previous studies, two-dimensional or three-dimensional finite element analysis of flexible and rigid pavement are carried out [9–13]. The thermal properties of concrete pavement are studied by Hu et al. in 2012 [14]. Very few 3D finite element investigations are conducted to study the deflections of fiber-reinforced WT hybrid pavement.

3 Description of the Problem The objective of the study is to evaluate the deflection of fiber-reinforced ultra-thin WT pavement under wheel loading at different positions.

3.1 Geometry The FE method developed in this study was four-layered pavement with top fiberreinforced WT layer, second bituminous layer, third sub-base, and bottom subgrade layer, and geometry of pavement is 3700 × 3700 × 100 mm. Fig. 1 shows 3D view of fiber-reinforced WT pavement with four layers, and Table 1 represents geometry of pavement.

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Fig. 1 3D view of fiber-reinforced WT pavement

Table 1 Geometry of pavement

Pavement layers

Thickness of pavement layers (mm)

Panel dimension (mm)

WT

60, 75, 100

3700 × 3700

Bituminous layer

120, 150, 200

Sub-base

300

Subgrade

400

3.2 Material Properties Linear elastic material properties are assumed for the study. Fiber reinforced WT layer Two elastic constants, Young’s modulus (E) and Poisson’s ratio (µ), were used to represent the material characteristics of the slab. Table 2 shows the material properties of fiber-reinforced WT layer. Five different grades of concrete such as M25, Table 2 Properties of fiber-reinforced WT layer [7, 15] S. No

Grade of concrete (Concrete with 0.5% PPF)

Young’s modulus (N/mm2 )

Poisson’s ratio

Density (kg/m3 )

1

M25

27,500

0.15

2400

2

M30

32,500

0.15

2400

3

M40

34,100

0.15

2400

4

M50

37,000

0.15

2400

5

M60

41,600

0.15

2400

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Table 3 Properties of bituminous layer (IRC 37:2018) S. No

Material

Resilient modulus (MPa)

Poisson’s ratio

Density (kg/m3 )

1

VG10

1000

0.35

1020

2

VG20

1500

0.35

1030

3

VG30

2000

0.35

1040

4

VG40

3000

0.35

1050

M30, M40, M50, and M60 with 0.5% polypropylene fiber (PPF) are considered for designing fiber-reinforced WT layer. Bituminous layer Bituminous layer is the second layer, and it is below the fiber-reinforced WT layer. Table 3 shows the properties of bituminous layer. Four different viscosity grade bitumen are considered such as VG10, VG20, VG30, and VG40. As per IRC 37:2018, the resilient modulus of bituminous layer is taken. Sub-base layer Sub-base course is a layer of pavement provided between subgrade and bituminous layer. It is provided as an additional layer when the subgrade is of poor quality. It consists of broken stones, slag, broken burnt bricks, etc. At sub-base, it improves the bearing capacity of subgrade, it checks the capillary rise of sub-soil water, and it eliminates frost heave in frost-affected area. If the California Bearing Ratio (CBR) value of subgrade is known, then Young’s modulus of sub-base can be found out by using Eq. 1, Msub base = 0.2 h0.45 × Msubgrade (IRC 37 : 2018)

(1)

where Msub-base = Modulus of elasticity of sub-base Msubgrade = Modulus of elasticity of subgrade. Subgrade layer Subgrade is a layer of natural soil or filled soil prepared to receive the pavement materials over it. Functions of subgrade are to transfer the entire load coming to it to the earth mass and to provide a good support to the pavement structure. The CBR value considered is 7%. The resilient modulus of subgrade soil is calculated using the following equations. MRS = 10.0 × CBR for CBR ≤ 5%(IRC 37 : 2018)

(2)

MRS = 17.6 × (CBR)0.64 for CBR > 5%(IRC 37 : 2018)

(3)

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where MRS = Resilient modulus of subgrade soil (in MPa) CBR = California Bearing Ratio of subgrade soil (%). Boundary conditions The pavement structure is fixed at the bottom and free to move laterally at the top. Figures 2 and 3 show the boundary conditions of pavement along vertical cross section of pavement along the traffic and across the traffic, respectively. Wheel loading Load is applied as per IRC 58:2015 [16] and IRC 6:2010 [17] assuming 10.2 T equivalent single-axle load as shown in Fig. 4. This load is applied at edge, corner, Fig. 2 Vertical c/s of pavement along the traffic

Fig. 3 Vertical c/s of pavement across the traffic

(a)

(c)

(b)

Direction of traffic

Direction of traffic Direction of traffic

3.7 m

3.7 m

3.7 m

Fig. 4 Plan of single-axle wheel loading on pavement system at a corner, b edge, and c interior

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Fig. 5 Loading on wheel

Direction of traffic 51

kN

Wheel

0.6 m

and interior of the pavement slab. Figure 5 shows the loading on wheels. The stress combinations are taken as the sum of wheel load stresses and temperature stresses for the above three load positions. Temperature loading For temperature loading, four different temperature gradients are considered. Four temperature gradients considered are −15, −25, 15, and 25 °C. Thermal expansion is taken as 1 × 10−5 /°C, conductivity as 1.37 × 10−3 J/s/mm/°C, and specific heat of concrete as 880 J/kg/°C. Numerical investigation A 3D FE model for WT has been developed using ANSYS software. 3D brick elements SOLID185, having eight nodes with three degrees of freedom per nodetranslations in the nodal x, y, and z directions, are used to model all the four layers (fiber-reinforced WT, bituminous layer, sub-base, and subgrade). The linear elastic isotropic material property is assumed for the pavement system. Perfect bonding is assumed between WT and bituminous layer. Figure 6 shows SOLID185 element. The mesh size chosen was 100 × 100 × 25 mm on the surface fiber-reinforced WT layer, 100 × 100 × 35 mm on the bituminous layer, 100 × 100 × 37.5 mm on the sub-base layer, and 100 × 100 × 40 mm on subgrade layer. FE of fiber-reinforced WT pavement is shown in Fig. 7. Fig. 6 SOLID185 element

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WT layer Bituminous layer Sub base laye Subgrade layer

Fig. 7 FE of fiber-reinforced WT pavement

Parametric study For studying the performance of fiber-reinforced WT pavement, various parameters are considered. The thickness of fiber-reinforced WT layer (60, 75, and 100 mm) and bituminous layer (120, 150, and 200 mm) were varied. The thickness of sub-base and subgrade thicknesses are kept constant. The material properties of WT layer and bituminous layer are also varied. Three different loading conditions such as interior, corner, and edge are considered, and maximum deflections at those loading positions are found out. Three temperature differentials are considered for this study.

4 Results and Discussions Numerical investigation was done to find the maximum deflection on fiber-reinforced WT layer.

4.1 Maximum Deflection Three different loading conditions are considered for analysis such as edge loading, interior loading, and corner loading. Figures 8 and 9 show the contour of vertical deformation across the pavement and horizontal deformation along the pavement, respectively, for pavement due to edge loading, interior loading, and corner loading. From figure, it is clear that the critical loading position is at corner. The maximum deflection is under corner loading. Therefore, critical loading position is at corner. Deflections are less in interior loading compared to edge loading in pavement.

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Fig. 8 Vertical deformation contour for fiber-reinforced WT pavement due to a edge loading, b interior loading, and c corner loading

Fig. 9 Horizontal deformation contour for fiber-reinforced WT pavement due to a edge loading, b interior loading, and c corner loading

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Fig. 10 Variation of maximum deflection of WT layer with various elastic moduli and thickness of WT layer due to a edge loading, b interior loading, and c corner loading

Effect of thickness of fiber-reinforced WT layer Variation of maximum deflection of WT layer with various elastic moduli and thickness of WT layer due to (a) edge loading, (b) interior loading, and (c) corner loading is shown in Fig. 10. From Fig. 10, it is found that the corner loading is critical compared to edge loading and center loading in WT pavement. The deflections are less in interior loading compared to edge loading in WT pavement. The thickness of WT layer increases from 60 to 75 mm; the deflection of pavement is decreased by 4.5%. The thickness of WT layer increases from 60 to 100 mm; the deflection of pavement is decreased by 10%. For a 60 mm thickness of WT layer, it is found that the material property increases from M25 to M60; the deflection of pavement is decreased by 13%. Variation of maximum deflection of WT layer with various resilient modulus of bituminous layer and thickness of WT layer due to (a) edge loading, (b) interior loading, and (c) corner loading is shown in Fig. 11. The corner loading is critical compared to edge loading and center loading in WT pavement. The deflections are less in interior loading compared to edge loading in WT pavement. The thickness of WT layer increases from 60 to 75 mm; the deflection of pavement is decreased by 5%. The thickness of WT layer increases from 60 to 100 mm; the deflection of pavement is decreased by 12.5%. For a 60 mm thickness

Fig. 11 Variation of maximum deflection of WT layer with various resilient modulus of bituminous layer and thickness of WT layer due to a edge loading, b interior loading, and c corner loading

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Fig. 12 Variation of maximum stress of WT layer with various elastic modulus of fiber-reinforced WT layer and thickness of WT layer due to a edge loading, b interior loading, and c corner loading

of WT layer, it is found that the material property increases from VG10 to VG30; the deflection of pavement is decreased by 15%. Variation of maximum stress of WT layer with various elastic modulus of fiberreinforced WT layer and thickness of WT layer due to edge loading, interior loading, and corner loading is shown in Fig. 12. It is found that the corner loading is critical compared to edge loading and center loading in WT pavement. The stresses are less in interior loading compared to edge loading in WT pavement. The thickness of bituminous layer increases from 60 to 75 mm; the stress of pavement is decreased by 4.2%. The thickness of bituminous layer increases from 60 to 100 mm; the stress of pavement is decreased by 12.6%. For a 60 mm thickness of WT layer, it is found that the material property increases from M25 to M60; the stress of pavement is decreased by 21%. Effect of thickness of bituminous layer Figure 13 shows the variation of maximum deflection of WT layer with various elastic modulus of fiber-reinforced WT layer and thickness of bituminous layer due to (a) edge loading, (b) interior loading, and (c) corner loading. The thickness of bituminous layer increases from 120 mm to 150 mm; the deflection of pavement is decreased by 3%. The thickness of bituminous layer increases from 150 to 200

Fig. 13 Variation of maximum deflection of WT layer with various elastic modulus of fiberreinforced WT layer and thickness of bituminous layer due to a edge loading, b interior loading, and c corner loading

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Fig. 14 Variation of maximum deflection of WT layer with various resilient modulus of bituminous layer and thickness of bituminous layer due to a edge loading, b interior loading, and c corner loading

mm; the deflection of pavement is decreased by 7.2%. For a 120 mm thickness of WT layer, it is found that the material property increases from M25 to M60; the deflection of pavement is decreased by 11%. The variation of resilient modulus of bituminous layer with thickness of fiber-reinforced WT layer is shown in Fig. 14. It is seen that the deflections are less in interior loading compared to edge loading in WT pavement. The thickness of bituminous layer increases from 120 to 150 mm; the deflection of pavement is decreased by 4%. The thickness of bituminous layer increases from 150 to 200 mm; the deflection of pavement is decreased by 10%. For a 120 mm thickness of WT layer, it is found that the material property increases from VG10 to VG40; the deflection of pavement is decreased by 9%. Effect of temperature differentials Temperature differentials considered for analysis are −15, −25, 15, and 25 °C with thickness of fiber-reinforced WT layer and bituminous layer. Due to variation in temperature, the effect on fiber-reinforced WT layer and bituminous layer are shown in Fig. 15. Four different temperature gradients such as 25, −25, 15, and −15 °C were considered. Corner loading, edge loading, and interior loading show approximately

Fig. 15 Variation of maximum stress of WT layer due to temperature differentials under different loading with various thicknesses of a WT layer and b bituminous layer

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equal variation. The critical loading position is at corner. The temperature of fiberreinforced WT layer increases from 15 to 25 °C; the stress of pavement is increased by 4%. The temperature of fiber-reinforced WT layer decreases from −15 to − 25 °C; the stress of pavement is increased by 9%. The temperature of bituminous layer increases from 15 to 25 °C; the stress of pavement is increased by 21.5%. The temperature of bituminous layer decreases from −15 to −25 °C; the stress of pavement is increased by 13%. From the numerical investigation, it is found that the corner loading is critical compared to edge loading and interior loading of fiber-reinforced WT pavement. The deflections are less in interior loading compared to edge loading in fiber-reinforced WT pavement. For corner, edge, and interior loadings, temperature gradient was more active than axle loading. Most significant parameters are the thickness of fiberreinforced WT layer and the modulus of elasticity of fiber-reinforced WT layer. Less significant parameter is the effect due to thickness of bituminous layer.

5 Conclusions Numerical analysis was performed to investigate the deflections on WT layer using a three-dimensional finite element method. In numerical investigation, the thickness of fiber-reinforced WT layer and bituminous layer, modulus of elasticity of fiberreinforced WT layer, resilient modulus of bituminous layer, resilient modulus of sub-base, and resilient modulus of subgrade layer were varied. Major conclusions derived from the study are the following: • It is found that the corner loading is critical compared to edge loading and interior loading of fiber-reinforced WT pavement. • The deflections are less in interior loading compared to edge loading in fiberreinforced WT pavement. • Temperature gradient of bituminous layer is significantly enhancing the stresses in the WT pavement system under corner pavement loading. • The variation in thickness and modulus of elasticity of fiber-reinforced WT layer result in the highest variation in the deflection in WT pavement system. Acknowledgements The authors would like to thank Kerala State Council for Science, Technology and Environment (KSCSTE) for providing the financial support to carry out the study (Title of project: Performance Evaluation of Ultra-thin White Topping Overlays, sanctioned on 19-08-2020) described in this paper.

References 1. Abdulwahab R, Akinleye TM, Taiye H (2018) Effects of polypropylene fibre on the compressive and splitting tensile strength of concrete. J Mater Eng Struct 5:15–22

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2. Amsa M, Ariyannan P (2018) Experimental study on polypropylene fiber reinforced concrete. Int Res J Eng Technol 5(4):1128–1130 3. Jayakesh K, Suresha SN (2018) Experimental investigation of interface treatment technique on interface shear bond fatigue behavior of Ultra-Thin White topping. J Constr Build Mater 161:489–500 4. Lu Z, Yao H, Liu J, Hu Z (2014) Experimental evaluation and theoretical analysis of multilayered road cumulative deformation under dynamic loads. Road Mater Pavement Design 15:35–54 5. Rajan K, Sudha C (2019) Experimental investigation of high strength concrete beam with hybrid fibers. ARPN J Eng Appl Sci 14:2795–2800 6. Harle SM, Pajgade P (2018) Modelling of plain cement concrete pavement patch using ANSYS Workbench. Am J Civil Eng 6(3–1):1–8 7. Mohod MV (2015) Performance of polypropylene fibre reinforced concrete. IOSR J Mech Civil Eng 12:28–36 8. Kumar BNS, Suhas R, Bhavan V (2014) Performance evaluation on thin white topping. Int J Res Eng Technol 3:395–404 9. Jundhare DR, Khare KC, Jain RK (2012) Structural analysis of ultrathin whitetopping under wheel loading using the computer code ANSYS. Int J Struct Eng 3:208–228 10. Kumar SS, Ravindraraj JB (2018) Study of temperature differential in different concrete slabs of varying slab thickness in different region. Int J Civil Eng Technol 9(4):1008–1013 11. Pereira DDS, Balbo JT, Khazanovich L (2006) Theoretical and field evaluation of interaction between ultra-thin whitetopping and existing asphalt pavement. Int J Pavement Eng 7:251–260 12. Zhu M, Weng X, Zhang J (2018) Three-dimensional FEM loading stress analysis on a new airport concrete pavement UTW overlay structure. Struct Concr 1:1–10 13. Kim YK, Lee SW (2017) Numerical analysis of debonding mechanism in bonded concrete overlay according to horizontal traffic loading. J Constr Build Mater 131:327–333 14. Hu J, Wang K, Ge Z (2012) Study of concrete thermal properties for sustainable pavement design. J Sustain Cement Based Mater 1(3):126–137 15. Khan S, Khan RA, Khan AR, Islam M, Nayal S (2015) Mechanical properties of polypropylene fibre reinforced concrete for M25 and M30 mixes: a comparative study. Int J Sci Eng Appl Sci 1(6):327–340 16. IRC 58: 2018: Guidelines for design of plain jointed pavements for highways, Fifth revision, Indian Road Congress 17. IRC 6: 2016 Standard specifications and code of practice for road bridges, section II, Loads and stresses, Seventh revision, Indian Road Congress 18. IRC 37: 2018: Guidelines for design of flexible pavement, Indian Road Congress, Fourth revision, New Delhi

Rheological Characterization of Unmodified and Modified Bitumen in the Temperature Range of 40–70 °C T. Srikanth and A. Padmarekha

Abstract The multiple stress creep and recovery have been used to quantify the rutting behavior of the binder. The parameter such as non-recoverable creep compliance, percentage recovery, and retardation time depends on the temperature. The sensitivity of these parameters with the variation in the temperature is studied. This experimental investigation is carried out using one unmodified and one modified binder, and the test was carried out at unaged and short-term aged condition. The temperature regime of interest for this study is 40–70 °C. Multiple stress creep and recovery (MSCR) and steady shear tests were performed. MSCR tests were conducted using parallel plate geometry at 40, 50, 60, and 70 °C. The tests were conducted for two stress levels of 0.1 and 3.2 kPa. The steady shear viscosity at two different shear rates was also measured at these temperatures. The creep and recovery response of the bitumen at two different stress levels was found to be scalable, and Burgers’ model was used to predict the creep and recovery response of the bitumen. The Arrhenius constant for different binders and different aging conditions was determined using the viscosity data obtained from the steady shear test. The material functions such as creep compliance, percentage recovery, and retardation time were determined based on the experimental and model results. These parameters were observed to follow Arrhenius relationship in the temperature range of 40–70 °C. Keywords Temperature susceptibility · Rutting · Multiple stress creep and recovery · Retardation time · Arrhenius equation

1 Introduction The high-temperature superpave performance grading of bitumen is based on the failure criteria in which the binders are graded based on the extreme temperature at T. Srikanth · A. Padmarekha (B) Department of Civil Engineering, SRM IST, Kattankulathur 603203, India e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_8

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which the binder fails in rutting. The rutting distress even occurs at lower temperatures; however, the scale of distress depends on the temperature of interest. In a real life, the pavements are subjected to a critical temperature for few degree-days when compared to the degree-days for a temperature range in which the possibility of rutting tendency exists. Hence, grading a binder based on a single value of a critical temperature is erroneous and one needs to consider a range of temperatures over which such resistance to distress can be expected. To include a cumulative effect of distress over a range of temperatures in the grading system, one needs to first understand how the distress of the binder is sensitive to a different temperature. From the application of the time–temperature superposition principle in the construction of the dynamic modulus master curve for bitumen, it is understood that the temperature sensitivity of bitumen follows the Arrhenius equation. Williams–Landel–Ferry (WLF) equation also had been commonly used in predicting the shift factors for different temperatures for the construction of master curve for bituminous material. In the high service temperature range, WLF equation was found to approximately reduce to Arrhenius equation [9]. Taking into consideration the number of constants in WLF equation, in this study, the activation energy is preferred to study the temperature susceptible behavior of the binder. The behavior of asphalt binders from creep recovery and steady-shear experiments on selected modified and unmodified binders at 60, 70 and 80 ºC and the results were used to characterize the binders using a nonlinear viscoelastic model. The experimental and model parameters were obtained in such a way that all identified to obey the Arrhenius correlation with temperature [4]. However, the application of the Arrhenius equation for the distress prediction of bitumen needs to be verified. Also, |G*|/sin δ criteria (|G*| represents the dynamic modulus, and δ represents the phase angle) was proved to be inefficient in capturing the rutting characteristics of modified binders [1]. Multiple stress creep and recover test was used to quantify rut behavior of modified binders. Now, to address the issues in the current grading system for bitumen, one needs to understand how the creep and recovery functions such as creep compliance, percentage recovery, and retardation time vary with the temperature. The non-recoverable creep compliance (J nr ), percentage recovery (R), and retardation time at specific temperatures had been used in rutting studies. However, these parameters are expected to vary with the temperature of interest. Recently Saboo et al. [11] and Narayan et al. [9] observed that creep compliance measured at different temperatures followed the Arrhenius equation. However, it is not known whether the activation energy constant will depend on the material characteristic functions. For instance, it is not understood whether one can use activation energy determined from the viscosity to predict the creep compliance and percentage recovery of the binder. Viscosity determines the shearing strength to the fluid flow and is dependent on the liquid temperature. With increase in the temperature, the thermal energy of the particles increases and the flow resistance decreases; therefore, the fluid viscosity decreases. Henry Eyring patterned the idea of activation energy difficult to flow [6]. During fluid flows, a layer of fluid molecules slides on one another while inter-molecular forces withstand the movement and cause flow resistance.

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This phenomenon results in an activation energy (AE). The relationship between viscosity and temperature can be modeled by using an Arrhenius equation [13]. Ef

η = Ae RT (or) lnη =

Ef + lnA RT

(1)

where η is the viscosity of the bitumen binder in Pa s, T is the temperature in degrees K, A is a constant, E f is the activation energy for flow in kJ/mol, and R is the universal gas constant (8.314 Jmol−1 K−1 ). In this investigation, the evolution of rutting distress characteristics of the bitumen with temperature is studied using the creep and recovery response of the material. This investigation was conducted using one unmodified binder (VG30 grade) and one modified binder (PMB40 (E)). All the tests were conducted on the unaged and short-term aged bitumen. The steady shear viscosity at two different shear rates was also measured at these temperatures using parallel plate geometry. The Arrhenius constant for different binders and aging conditions was determined using the viscosity results achieved by the steady shear test. From the activation energy obtained from the steady shear test, the temperature susceptibility characteristics of binder during rutting are evaluated using MSCR data. By understating the evolution of material characteristics at different temperature, this study will further help in quantifying the rutting characteristics of binders at different temperature.

2 Materials and Investigation 2.1 Materials This experimental investigation is carried out using two binders: one is unmodified bitumen of VG30 viscosity grade as per IS 73, and the other is laboratory modified PMB40(E). The tests were carried out for unaged (UA) and short-term aged conditions. The short-term aging was performed according to ASTM D2872-19. The equivalent performance grade of the VG30 and PMB40(E) was observed to be 70 and 82 °C as high temperatures and 22 and 16 °C as intermediate temperatures, respectively.

2.2 Experimental Investigation The experimental investigation is carried out in two stages: In the first set of experimental investigations, the viscosity of bitumen was measured using steady shear test to establish the temperature dependency relation. In steady shear test, all the sample was perfectly sheared by applying a constant shear rate of 0.5 and 5 s−1 for 10 min.

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25 mm parallel plate geometry was used with a gap of 1 mm. Tests were performed at four temperatures 40, 50, 60, and 70 °C for both the binders and for two aging conditions. The apparent viscosity was recorded at the rate of one data point per second. Figure 1 indicates the viscosity variation as a function of time. The viscosity attained steady condition after few seconds of shearing. The difference between two consecutive viscosity measure was used to determine the steady-state condition. Here, it is considered as the steady-state condition if the difference between two consecutive measures is less than 5%. The point of reaching steady state was observed to vary with shear rate and temperature. At higher temperatures, the viscosity reached the steady state relatively earlier when compared with lower temperature conditions. For all the conditions tested, the 200th second data was found to fall in the steady-state condition. Hence, the 200th second data was considered for further analysis. In the second set of experimental investigations, the binder is tested using the MSCR protocol. In the MSCR test, all the samples were sheared by applying continuous shear stress for a certain time (creep) and then allowed to relax for a specified period (recovery). The creep and recovery tests were carried out at four temperatures 40, 50, 60, and 70 °C for both the binders at two aging conditions. The parallel plate of 25 mm diameter was used with 1 mm gap. The strain values were measured at two different stress levels of 0.1 and 3.2 kPa, and loading and unloading at each stress level were repeated for five cycles. Figure 2 shows the schematic test protocol of the MSCR test. The creep and the recovery times were fixed based on the strain values. These interval timings for both creep and recovery were fixed so that the strain value reached the steady state for both the stress levels. Figure 3 shows the rate of variation strain for short-term aged PMB40(E) binder during 5th cycle creep and recovery at 0.1 kPa. Here, the steady-state condition was determined from the derivative plot. The point where the derivative of the creep strain in relation to time reached constant is considered as the steady-state condition. Similarly, during recovery, the point where the time derivative of strain is zero is considered as steady state during recovery. From Fig. 3, one can state that both creep and recovery reaching the steady state are dependent on temperature. The lower temperature reaches the steady state at an early stage, whereas the higher Fig. 1 Variation of viscosity for PMB(E) RTFO at 40 °C

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Fig. 2 MSCR test protocol

Fig. 3 Rate of variation of strain in short-term aged PMB40(E). a Creep. b Recovery

temperature reaches the steady state slowly. For instance, for short-term aged PMB 40(E), the steady state during creep reached 6, 8.1, 9.5, and 11.2 s at 40, 50, 60, and 70 °C, respectively. For the same material, the condition reached steady state during recovery at 90.1, 99.8, 111.2, and 125.4 s at 40, 50, 60, and 70 °C, respectively. Hence, one can state that with increases in the test temperature, the steady-state time also increases. Considering the maximum time required to reach the steady-state condition, the creep time of 15 s and recovery time of 150 s were fixed for all the tests.

3 Results and Discussion Figures 4 and 5 show the creep and recovery of the binders for all the test temperatures and aging conditions for 0.1 and 3.2 kPa. From Figs. 4 and 5, one can say that the strain

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Fig. 4 Creep and recovery at 40 and 50 °C

Fig. 5 Creep and recovery at 60 and 70 °C

of unaged (UA) binder shows higher than the short-term aged binder. As expected, the strain increased with an increase in temperature. The creep in VG30 was found to be higher when compared with PMB40(E). Figure 6 shows the normalized strain corresponding to the 5th cycle. From Fig. 6, one can state that the creep and recovery data is scalable up to 95% accuracy with respect to the applied stress level. As the response of the material is scalable, one can say that the response of the material is linear. Further, the linear viscoelastic

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Fig. 6 Normalized strain at 40 °C

material parameters such as creep compliance and linear viscoelastic model can be used to characterize the response of the material. The material functions such as creep compliance and percentage recovery were determined from the experimental data and retardation time using a linear viscoelastic model. The material parameters non-recoverable creep compliance (J nr ) and percentage recovery (R) of the binders were calculated using Eqs. 3 and 5. Non - recoverable creep compliance, ε10 Jnr (Stress, N ) = Stress Average non - recoverable creep compliance, Sum (Jnr (Stress, N )) Jnr Stress = 5 ε1 − ε10 Percentage recoveryεr (Stress, N ) = × 100 ε1 Average percentage recovery, RStress =

Sum (εr (Stress, N )) 5

where, ε1 = εc − ε0 and ε10 = εr − ε0 εo , the strain value at the beginning of creep portion of each cycle, εc , the strain value at the end of creep portion of each cycle,

(2)

(3)

(4) (5)

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Fig. 7 Burgers model

εr , the strain value at the end of the recovery portion of each cycle and N = 1 to 5. The retardation time is determined using a linear viscoelastic model, and in this study, Burgers’ model is used. Figure 7 shows the spring and dashpot arrangement of Burgers’ model. The total strain in the model is given in Eq. 6, and the time-varying strain due to an applied constant stress σo is given in Eq. 7. ε(t) = ε1 + ε2 + ε3 ε(t) =

−E 2 t  σo σo σo  1 − e η2 + t+ E1 η1 E2

(6) (7)

where ε1 = strain caused by spring; ε2 = strain caused by dashpot; ε3 = strain caused by Kelvin model, respectively; and ε = total strain of the model. The time-varying strain due to an applied constant stress σ0 is as follows [13]. Retardation time is given by ï1 /E 1 or ï2 /E 2 . The model parameters ï1, ï2, E 1, and E 2 are estimated by nonlinear curve fitting. Figure 8 shows a sample experimental and model-predicted creep and recovery curves for PMB(E) UA at 40 ˚C. From this figure, one can state that the experimental and the model data was observed to be equal, and this model data was used to estimate the retardation time of the bitumen. Table 1 indicates the non-recoverable creep compliance (J nr ) and percentage recovery (R), which was calculated using Eqs. (2)–(5) and retardation time calculated using Burgers’ model parameter. Further, the variation of the non-recoverable creep compliance (J nr ), percentage recovery (R), and retardation time with temperature is studied. For this purpose, the Arrhenius relationship is used. The activation energy of the Arrhenius equation is obtained using the viscosity–temperature relation of the binder. Figure 9 indicates the difference of viscosity with temperature for short-term aged PMB40(E) binder. The slope of the plot is considered as activation energy, and it can be observed that the activation energy is independent of shear rates.

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Fig. 8 Experimental and model data at 40 °C of PMB(E) UA binder

Table 1 Binder parameters (non-recoverable creep compliance and percentage recovery) Binder VG30 PMB(E)

Condition UA

Parameter J nr

(Pa−1 )

Stress (kPa) 0.1

PMB(E) VG30

VG30

0.619

2.733

10.096

0.323

1.547

6.352

UA

0.021

0.112

0.408

1.872

0.007

0.031

0.171

0.595

0.084

0.650

2.855

10.701

RTFO

0.053

0.353

1.629

6.577

UA

0.023

0.157

0.734

3.008

RTFO

0.006

0.036

0.251

1.124

2.383

0.362

0.031

0.315

UA

UA

3.2

R (%)

0.1

4.538

1.843

0.284

0.271

UA

32.282

32.468

26.269

14.020

RTFO

57.276

56.388

37.368

29.865

1.611

0.129

0.021

0.009

3.368

0.368

0.046

0.011

29.688

15.087

2.304

0.247

60.614

50.845

20.647

3.905

0.162

0.034

0.012

0.002

RTFO

0.350

0.089

0.022

0.005

UA

3.249

1.233

0.474

0.211

RTFO

9.024

6.331

1.812

0.250

UA

3.2

UA RTFO

VG30 PMB(E)

70 °C

0.051

RTFO PMB(E)

60 °C

0.081

RTFO PMB(E)

50 °C

RTFO RTFO VG30

40 °C

UA

RT (s)

–

Figure 10 depicts the values of viscosity as a function of temperature for all the binders and aging conditions. These viscosities were fitted to Eq. 1, which can also be seen in the same figure. The activation energy for all the samples was tabulated in Table 2. These activation energies were used to observe whether the material parameters tabulated in Table 1 follow the Arrhenius relationship or not. The variation

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Fig. 9 Viscosity values at different shear rates of PMB(E) RTFO

Fig. 10 Viscosity values with Arrhenius fit

Table 2 Activation energy values of the binders

Binder

Condition

AE (kJ/mol)

VG30

UA

55.80

RTFO

60.36

UA

56.87

RTFO

58.17

PMB(E)

of the non-recoverable creep compliance (J nr ), percentage recovery (R), and the retardation time with temperature is expected to follow the Arrhenius equation, and the same is shown in Fig. 11. Figure 11 shows the variation of material parameters as a function of temperature.

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Fig. 11 Material parameters

4 Conclusions Analyzing the steady-state creep and recovery response of the material, it is understood that in the 40–70 °C temperature regime, the response of the material is viscoelastic fluid. The creep and recovery response of the bitumen at two different stress levels was found to be scalable, and the simple one-dimensional linear viscoelastic fluid model (Burgers’ model) could predict the creep and recovery response of the bitumen. The variation material functions such as creep compliance, percentage recovery, and retardation time with temperature were observed to follow the Arrhenius equation in the temperature range of 40–70 °C. Acknowledgements The authors would like to acknowledge the Science and Engineering Research Board (SERB) wing of the Department of Science and Technology, New Delhi, India, for financial support (Project no: SERB/F/6157/2018-2019).

References 1. Angelo DJ, Kluttz R, Dongre RN, Stephens K, Zanzotto L (2007) Revision of the superpave high temperature binder specification: the multiple stress creep recovery test (with discussion). J Assoc Asphalt Paving Technol 76 2. ASTM D2872—19: Standard test method for effect of heat and air on a moving film of asphalt (Rolling Thin-Film Oven Test). In: American society for testing and materials. West Conshohocken, PA, United States

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3. ASTM D7175—15: Standard test method for determining the rheological properties of asphalt binder using a dynamic Shear Rheometer. In: American society for testing and materials. West Conshohocken, PA, United States 4. Atul Narayan SP, Murali Krishnan J, Little DN, Rajagopal KR (2017) Mechanical behaviour of asphalt binders at high temperatures and specification for rutting. Int J Pavement Eng 18(10):916–927 5. Angelo D (2009) Development of a performance based binder specification for rutting using creep and recovery testing. Ph.D. Thesis, Calgary, Alberta 6. Eyring H (1936) Viscosity, plasticity, and diffusion as examples of absolute reaction rates. J Chem Phys 4:283–291 7. IS: 73:2018: Paving bitumen—specification (Fourth Revision). Bureau of Indian Standards, New Delhi, India 8. Kennedy T, Huber GA, Harrigan ET, Cominsky RJ, Hughes CS, Von Quintus HL, Moulthrop JS (1994) SHRP-A-410: superior performing asphalt pavements (superpave): the product of the SHRP asphalt research program. Strategic Highway Research Program, National Research Council, Washington DC 9. Narayan SP, Dallas A, Little N, Rajagopal KR (2019) Incorporating disparity in temperature sensitivity of asphalt binders into high-temperature specifications. J Mater Civ Eng 31(1):04018343 10. Mashaan NS, Karim MR (2013) Investigating the rheological properties of crumb rubber modified bitumen and its correlation with temperature susceptibility. Mater Res 116–127 11. Saboo N, Singh B, Kumar P (2019) Development of high-temperature ranking parameter for asphalt binders using Arrhenius model. J Mater Civ Eng 31(12):04019297 12. Ward IM, Hadley DW (1993) An introduction to the mechanical properties of solid polymers. John Wiley & Sons, New York 13. Wineman AS, Rajagopal R (2000) Mechanical response of polymers—an introduction. Cambridge University Press, Cambridge, UK

Fatigue Damage Criteria for Bitumen Based on the Evolution of Lissajous Plot M. Jayaraman and A. Padmarekha

Abstract Time sweep test (TS) and linear amplitude sweep (LAS) test are the two types of oscillatory shear testing that were commonly used fatigue tests. The damage progress from these both tests is expected to be completely different for different binders. In this study, the fatigue damage due to TS and LAS test is quantified using the evolution of distortion in the Lissajous plot. For this investigation, bitumen of grade VG30 and polymer-modified binder of grade PMB40 (Plastomer) were used. Both the binders were tested in long-term aged condition. The TS test was conducted at 20 °C for 1 and 1.5% strain amplitude at 5 and 10 Hz frequencies for 20,000 cycles. For the LAS test, strain amplitude varies from 0 to 30% at 20 °C and a frequency of 5 and 10 Hz. The Lissajous plot was fitted to the standard ellipse, and the distortion in the shape was measured using the R2 value. The fatigue life was identified as a point of slope change in the R2 plot. The fatigue life of binder obtained from TS test was found to be 10–40 times higher than fatigue life from LAS test. Also, the fatigue life ranking at different frequencies from LAS test was observed to be different from the ranking obtained from TS test. Keywords Fatigue damage · Time sweep test · Linear amplitude sweep

1 Introduction In the binder selection process for bituminous mixture, two types of binder distress are considered. One is the rutting distress, and the other is the fatigue damage. In the performance grading of binder, the rutting and fatigue damage are quantified using dynamic modulus and phase angle, and this approach is proved to be effective for unmodified binder. Many works of literature have proved the ineffectiveness of performance grade parameters in quantifying the binder distress for modified binders [1, 2]. Following this multiple creep and recovery test, it is proved to be effective in characterizing the rutting behavior of the asphalt binder [3]. Many recent studies M. Jayaraman · A. Padmarekha (B) Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, India e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_9

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focus on the fatigue damage characterization of unmodified and modified binders [4]. Understanding the fatigue damage accumulation of the binder especially the modified binder to the required region is under process, and study is one such attempt. Two types of test protocols are widely used to quantify fatigue damage of binder. In one test protocol, the binder is subjected to repeated oscillatory shearing using constant strain amplitude and frequency. The test protocol is termed as time sweep test (TS) [5]. In the second test protocol, the binder is subjected to oscillatory shearing with linearly increasing strain amplitude under constant frequency. This test is termed as linear amplitude sweep (LAS) test [6]. In continuous loading, the binder initiates micro-cracks, and coalescence of micro-cracks leads to the formation of microcracks. The progression of the damage depends on the strain amplitude. It is well known that at a higher strain level, the damage progresses quickly when compared to lower strain amplitude. In the LAS test, the damage in the binder is induced at a faster rate when compared to the TS test. This is expected to affect the fatigue life of the binder. In case if one is interested in ranking unmodified and modified binder based on the fatigue life across different strain amplitude and frequencies, it is necessary to understand how the rate of progress in the damage influences the fatigue life of unmodified and modified binder. Different post-processing techniques exist for the determination of fatigue life. The number of cycles corresponding to 50% of dynamic modulus is one such approach that is used in processing the TS test data [7]. The evolution of energy dissipation and phase angle is also used in the estimation of the fatigue life of the binder. For the LAS test, pseudo stiffness-based criteria [8, 9] were widely used as fatigue criteria. The peak of the pseudo stiffness curve (CxN with N) gives the number of cycles corresponding to the fatigue life. Where C- is the pseudo stiffness, N- is the number of cycles. When the binder is subjected to sinusoidal strain in the linear regime, the stress waveform of the undamaged binder is also expected to be in the sinusoidal shape, and thus, the Lissajous plot of the undamaged binder will be elliptical. On continuous loading, as the damage progresses in the material, the Lissajous plot is expected to distort from the ellipse. The existing literature [10, 11] captured this deviation of the Lissajous plot from the ellipse for the bituminous mixture using the R2 coefficient. During the initial loading, the material experience a negligible amount of damage, Hence there is no distortion in the Lissajous plot was not observed. As a result, the R square value is expected to be near 1. As the damage progresses, the distortion of the Lissajous plot from the ellipse increases, and this will result in a lower R2 value. The evolution of R2 with the number of cycles is used in the determination of the fatigue life of the material. The deviation of the Lissajous plot on continuous shear loading for bitumen is not known. As a common method of post-processing of TS and LAS test data, here, the deviation of the Lissajous plot from the ellipse was measured and the evolution of deviation was measured in the fatigue life determination of the binder. In the experimental investigation, the polymer-modified bitumen PMB 40 (Plastomer) was subjected to TS and LAS test and fatigue life was determined using different post-processing techniques and the results were benchmarked with the

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same unmodified binder that was used in the production of PMB 40 (Plastomer). The fatigue life magnitude and its sensitivity to the frequency in LAS and TS for unmodified and modified are further analyzed.

2 Experimental Investigation For this experimental investigation, VG30 and PMB40(P) binders were used. Before conducting a fatigue test on the binders, both the binders were tested for their basic properties following IS 73 [12] and IS 15462 [13] and the test results were found to satisfy the corresponding grade requirements of the standards. For the fatigue characterization of the binder, the long-term aged binders were tested. Dynamic shear rheometer was used for the repeated oscillatory shear in strain-controlled mode. The geometry of the 8 mm diameter sample with a 2 mm shear gap thickness was used to carry out the test at a constant temperature of 20 ºC. Two sets of fatigue tests were conducted. The first set of tests is the time sweep (TS) test in which the binder was sheared at a constant strain amplitude of 1 and 1.5%. To check the influence of frequency, the test was conducted for two different frequencies of 5 and 10 Hz, and the sample was sheared continuously for 20,000 cycles. In the second set of tests, the binder is subjected to linear amplitude sweep (LAS) test with linear increment in amplitude from 0 to 30%. LAS test was also conducted at two different frequencies of 5 and 10 Hz. The strain waveform corresponding to 5 Hz for both TS and LAS test is shown in Fig. 1. Both the tests were conducted in Large Amplitude Oscillatory Shear mode, and complete waveform data with each waveform consisting of 512 data points were collected. In addition to dynamic modulus and phase angle as a function of the number of cycles, the strain and stress waveform data were collected for every 10th cycle of shearing. Two replicates were conducted to ensure the repeatability. Figure 1c and d shows the repeatability check with a 10% error bar.

3 Result and Discussion 3.1 Fatigue Life Based on Dynamic Modulus The evolution of dynamic modulus obtained from the TS test was used in the estimation of the fatigue life of bitumen. The evolution of the dynamic modulus of PMB40(P) at 1.5% strain amplitude and 5 Hz frequency is shown in Fig. 2. As expected, the dynamic modulus decreased on continuous loading. The number of loading cycles corresponding to 50% of the dynamic modulus of the initial load cycle is considered as the fatigue life of the binder. The fatigue life obtained based on dynamic modulus criteria is tabulated in Table 1. The dynamic modulus remained constant up to 20,000 cycles of loading for VG30 and PMB40(P) while testing at

116

Fig. 1 Test protocols and repeatability check Fig. 2 Evolution of dynamic modulus of PMB40(P) for 5 Hz at 1.5% strain and its fatigue life

M. Jayaraman and A. Padmarekha

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Table 1 Time sweep test results—fatigue life based on dynamic modulus 5 Hz

10 Hz

VG 30

PMB40(P)

VG 30

PMB40(P)

1%

1.5%

1%

1.5%

1%

1.5%

1%

1.5%

–

4445

–

4825

13,500

3300

17,900

3450

1% strain and 5 Hz frequency, and the extrapolation of the results was not attempted. PMB40(P) exhibited better fatigue resistance at both frequencies of testing when compared to the VG30 binder.

3.2 Fatigue Life Based on Pseudo Stiffness The pseudo stiffness was calculated for the LAS test data, and the evolution of the product of the pseudo stiffness (C) and the number of cycles (N) with loading cycle for PMB40(P) at 5 Hz frequency is shown in Fig. 3. The methodology adopted for the calculation of pseudo stiffness was adopted from [8, 9]. The peak of the C × N curve gives the fatigue life. The fatigue life based on pseudo stiffness for both the binders and all the conditions tested were tabulated in Table 2. From Table 2, the fatigue life of both unmodified and modified bitumen at 5 Hz frequency was found to be higher when compared to 10 Hz frequency, whereas, TS test results exhibited better fatigue life at 10 Hz frequency when compared to 5 Hz frequency (Please refer to Table 2). Also, when comparing the results of the TS and LAS test, the magnitude Fig. 3 C × N curve for PMB40(P) for 5 Hz

Table 2 Linear amplitude sweep test results—fatigue life based on pseudo stiffness

5 Hz

10 Hz

VG 30

PMB40(P)

VG 30

PMB40(P)

423

440

388

432

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of fatigue life based on TS is found to be approximately 10 to 40 times higher when compared to the LAS test. This difference was found to depend on the binder type, strain amplitude, and the frequency of testing.

3.3 Evolution of Distortion in the Lissajous Plot The stress–strain curve for the undamaged binder is the perfect ellipse. When the material is subjected to repeated loading, the distortion in the shape of the Lissajous plot was observed. Figure 4 shows that at the 1000th cycle, there is a slight distortion in the shape of the Lissajous plot and the complete distortion was observed at the 20,000th cycle indicating the progressive damage accumulation in the binder. To identify the distortion of the Lissajous plot, R2 -based analysis was carried out. R2 square is defined as a deviation between the predicted model (ellipse) and actual response (Lissajous plot obtained experimentally). For the prediction model, as suggested by [14], general conic fitting ellipse using the second-order polynomial equation (Eq. 1) was used. F(a, x) = a.x = ax 2 + bx y + cy 2 + d x + ey + f = 0

(1)

where a = (a, b, c, d, e, f )T , x = (x 2 , xy, y2 , x, y, 1)T and F(a, x i ) is the algebraic distance of point a (x, y) to the conic F(a, x) = 0. Figure 5 compares the experimental data and the model prediction of VG30 binder at 1% strain amplitude and 10 Hz frequency. The evolution of R2 was observed for each cycle. From Fig. 6a, it was observed that the evolution of R2 is steady till the test duration indicating that the material does not experience any damage. From Fig. 6b–d for initial cycles, the evolution of R2 was steady, and when the number of loading cycles increases, slope changes were observed. It indicates the initiation of distortion in the Lissajous curve as a result of damage accumulation in the material. The evolution of R2 from the LAS test as shown in Fig. 7 also shows a similar trend. The point of slope change in R2 Fig. 4 Lissajous Plot—PMB40(P) at 1.5% and 5 Hz

Fatigue Damage Criteria for Bitumen Based on the Evolution …

Fig. 5 Lissajous plot of VG30 at 1% for 20 °C for 10 Hz and its comparison with ellipse

Fig. 6 R2 Evolution for time sweep test data

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Fig. 7 R2 Evolution for linear amplitude sweep test data

Table 3 Fatigue life based on the shape of Lissajous plot Test protocol

5 Hz

10 Hz

VG 30

PMB40(P)

VG 30

PMB40(P)

1%

1.5%

1%

1.5%

1%

1.5%

1%

1.5%

TS

–

4134

–

4814

7205

3556

–

4169

LAS

472

520

390

454

is considered as fatigue life. The point of slope changes was determined by drawing the two tangent lines as shown in Fig. 6b–d and Fig. 7. For LAS test data, the slope changes in the R2 evolution were observed earlier than the TS test. Table 3 shows the fatigue life computed from the evolution of the R2 value of VG30. As in the case of fatigue life based on dynamic modulus, TS showed higher fatigue life at 10 Hz frequency when compared to 5 Hz, whereas this is not the case for the LAS test. The fatigue life obtained from the Lissajous plot is in a close match with the fatigue life based on 50% dynamic modulus.

4 Conclusion • PMB40(P) exhibited better fatigue resistance at both frequencies of testing when compared to the VG30 binder. • When comparing the fatigue life obtained from the TS test and LAS test, the TS test is approximately 10–40 times higher than the LAS test which is due to the influence of testing frequencies and strain amplitude. • The point of slope changes in R2 is taken as fatigue life. The fatigue life obtained from the Lissajous plot is observed to be closer to the fatigue life based on 50% dynamic modulus.

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• The fatigue life ranking at different frequencies from LAS test was observed to be different from the ranking obtained from TS test.

References 1. Bahia HU, Zhai H, Onnetti K, Kose S (1999) Non-linear viscoelastic and fatigue properties of asphalt binders. J Assoc Asphalt Paving Technol 68 2. Shenoy A (2002) Fatigue testing and evaluation of asphalt binders using the dynamic shear rheometer. J Test Eval 30(4):303–312 3. D’Angelo JA (2009) The relationship of the MSCR test to rutting. Road Mater Pavement Design 10(sup1):61–80 4. Bonnetti KS, Nam K, Bahia HU (2002) Measuring and defining fatigue behaviour of asphalt binders. Transp Res Rec 1810(1):33–43 5. Bahia HU, Hanson DI, Zeng M, Zhai H, Khatri MA, Anderson RM (2009) Characterization of modified asphalt binders in superpave mix design, NCHRP Report 459. National Academy Press, Washington, D.C 6. AASHTO TP101 (2014) Standard method of test for estimating damage tolerance of asphalt binders using the linear amplitude sweep. Washington, DC 7. Mannan UA, Tarefder RA (2018) Investigating different fatigue failure criteria of asphalt binder with the consideration of healing. Int J Fatigue 114:198–205 8. Cao W, Wang C (2018) A new comprehensive analysis framework for fatigue characterization of asphalt binder using the linear amplitude sweep test. Constr Build Mater 171:1–12 9. Kommidi SR, Kim YR, de Rezende LR (2020) Fatigue characterization of binder with aging in two length scales: sand asphalt mortar and parallel plate binder film. Construct Build Mater 237:117588 10. Al-Khateeb G, Shenoy A (2004) A distinctive fatigue failure criterion. J Assoc Asphalt Paving Technol 73:585–622 11. Al-Khateeb G, Shenoy A (2011) A simple quantitative method for identification of failure due to fatigue damage. Int J Damage Mech 20(1):3–21 12. IS 73, 2018 (Indian Standards): paving bitumen–specifications. IS 73. New Delhi, India: IS (2018) 13. IS 15462, 2019 (Indian Standards). Polymer and rubber modified bitumen–Specification. IS 15462. New Delhi, India: IS (2019) 14. Fitzgibbon AW, Pilu M, Fisher R (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):477–480

Moisture Damage Prediction of Hot Mix Asphalt Using Artificial Neural Network A. Jegan Bharath Kumar, Mohit Singh Parihar, P. Murshida, V. Sunitha, and Samson Mathew

Abstract Tensile Strength Ratio (TSR) is a moisture-defining characteristic of hot mix asphalt (HMA) used to describe the susceptibility of mix against moisture damage. The goal was to create an artificial neural network (ANN) model that could forecast TSR. Compaction blows, bulk density expressed in gram per cubic centimeter, bulk specific gravity, stability, flow, percentages of bitumen content, air voids, voids in mineral aggregate, and voids filled with bitumen were employed as the predictor variables. Modeling involved these properties of the mix as input parameters and TSR as an output parameter. Feedforward backward type architecture with Levenberg-Marquardt training algorithm opted for the ANN model. The significance of input parameters was based on Pearson’s test (R-value). The results demonstrate that the model is able to forecast the TSR values of a bituminous mixture with high accuracy. Values of performance parameters were observed to be satisfactory (RMSE 0.75) on validation data. A set of calculating equations have been derived using the best ANN model to measure TSR. The relevance of parameters in forecasting TSR has been determined using Garson’s method. The sensitivity analysis result shows that out of all input parameters, Stabilitydry is more effective in explaining TSR (predicted) variations. This study is limited to volumetric properties of the HMA mix and assessment of TSR using ANN techniques. Keywords Hot mix asphalt · Moisture damage · Artificial neural network

A. Jegan Bharath Kumar (B) · M. S. Parihar · V. Sunitha National Institute of Technology, Tiruchirappalli, Tamil Nadu, India e-mail: [email protected] Present Address: A. Jegan Bharath Kumar · P. Murshida · S. Mathew KSCSTE-National Transportation Planning and Research Centre (NATPAC), Thiruvananthapuram, Kerala, India © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_10

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1 Introduction Flexible pavements are having a significant part of the transportation infrastructure system in India. Moisture damage is the widely occurring mode of pavement failure. It is referring to the loss of durability and strength. The extent and level of moisture damage depend on the environment, volumetric properties, and the quality and composition of the materials used in the bituminous mixture [1]. Moisture susceptibility is generally called stripping. Debonding of bitumen from the aggregate exterior by moisture result defines stripping. ANN is quickly becoming the most popular advanced approach for generating data-driven prediction models, owing to the ease with which it can be deployed in practical applications utilizing equations or spreadsheets. Many previous works of literature support the use of ANN models in the prediction of pavement performance [2–6]. The ANN is used to develop correlations between several factors involved. The human brain neural structure inspired the ANN, and the ANN model is a more advanced computational tool for applications involving unique data processing features [6]. The supervised methods, like back propagation, were the most general method for training. The TSR of the asphalt samples exposed to varying compaction levels was examined experimentally and using ANN.

2 Objectives of Study The construction of an ANN model for forecasting the TSR values of a bituminous mixture is the aim of this study. The specific objectives are as follows. • To develop a computational framework for Tensile Strength Ratio forecasting of asphalt mix using ANN • To establish the relationship between factors such as volumetric properties, stability, indirect tensile strength, and Tensile Strength Ratio • To check performance of ANN model in predicting TSR • To conduct a sensitivity analysis and establish the significance of various factors in the prediction of moisture damage.

3 Theoretical Background A previous study conducted on ANN was utilized to investigate the potential of artificial intelligence in assessing the soil strength [7]. Six distinct kinds of ANN models with varying parameter groups were explored in order to obtain the optimum model for shear strength prediction. Their analysis indicated that virtually all ANN could accurately predict observed shear strength. Research on application of ANN approach to the prediction of Resilient Modulus was conducted. Their study results showed that ANN models show higher prediction accuracy when compared to conventional

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regression models [5]. In a study, they developed models to forecast the ITS and TSR of various mixtures. They investigated just five input variables: supplier of asphalt binder and aggregate, bitumen modifier, and conditioning period. The findings revealed that ANN models are very effective predictors. They concluded that these ANN models could simply be implemented in a spreadsheet, making their application easier [2]. The estimate of the susceptibility features of the HMA to water damage served as the basis for this investigation. The samples were prepared with a void ratio of 7±1.0%. An investigation of the influence of relevant factors on HMA characteristics was carried out using a statistical approach. An investigation of two components at three levels of abstraction was carried out using a central composite design in this research. The significance of the parameter with the polynomial regression model and RSM was discussed [8].

4 Methodology 4.1 Experimental Investigation of Material Properties The following are the tests that required in order to obtain volumetric and physical attributes of materials: The characteristics of course aggregate were investigated using the following: • Water absorption and specific gravity test was followed to measure material strength and quality. Generally, aggregates with a high specific gravity are stronger than those with a low specific gravity. The amount of water absorbed provides an indication of aggregate strength [9]. • To determine the toughness of the aggregate, the aggregate impact test was performed. The Los Angeles abrasion test (IS 2386, Part 4-1963) was employed to calculate the percentage wear caused by rubbing between the aggregates and the steel balls [10]. • Combined flakiness and elongation test (shape test) and soundness test were followed to measure the resistance of coarse aggregates and fine aggregates to weathering action [11, 12] (Table 1). The characteristics of asphalt were investigated using the following: Table 1 Physical properties of selected aggregates

Properties (%)

Result (%)

Particle shape

10.58

Strength

21.00

Water absorption Abrasion test Specific gravity

0.29 25.00 2.73

126 Table 2 Properties of asphalt used in HMA mixture

A. Jegan Bharath Kumar et al. Properties

Typical quality

Penetration at 25 °C, mm

53.3

Softening point in °C

49

Ductility at 25 °C, cm

92

Specific gravity

1

• Penetration test was followed to measure the hardness or softness of bitumen. It measured the vertical penetration depth of a normally loaded needle in 5 s, measured in tenths of a millimeter. • Ductility is distance, to which a taster of the bitumen will be extended without breaking was find. • Softening point test was followed to define the temperature at which the bitumen softens to the specified degree in the test standards. Table 2 shows the parameters of VG-30 bitumen, which was employed in the HMA mixture, after these testing [13].

4.2 Preparation of Samples The HMA samples were prepared at the research laboratory. Compaction temperature of 150 °C and the different number of blows (75, 60, 50, 40, and 35 blows on each face) were considered for the preparation of samples. Using mix design methodologies, the bitumen percent was used to calculate the optimum binder percentage, which came out to be 4.5% of the final mixture. The characteristics of HMA were investigated using bulk specific gravity or density as per AASHTO T 331 and maximum specific gravity (AASHTO D6857), percentage air voids, VMA, and VFA in the compacted mixtures. Percentage passing through different sieve sizes as per MORTH clause 507.2.5 table was followed for pavement sample preparation [14]. Aggregate characteristics for the mix were adopted as single gradation for all the sample. The percentage passing different sieve sizes was discussed as defined in Table 3. The performance of HMA has to be found out for moisture susceptibility detection. Studies related to the moisture susceptibility of HMA suggested that the method can be used for defining moisture susceptibility [15]. The moisture-induced damage of the asphalt mixture was calculated using the TSR [20]. TSR was obtained from the ratio (100 × ITSwet /ITSdry ) of the mean ITSwet conditioned and ITSdry unconditioned after two freeze-thaw cycles. ITS values in kPa and specimen were calculated using the length and diameter of the specimen and the peak load at which specimen splits diametrically. The minimum specified value of TSR is 80% as per MORTH specification. A value above 80% means that the mix is not susceptible to moisture-induced damage.

Moisture Damage Prediction of Hot Mix Asphalt … Table 3 Cumulative % by weight of total aggregate passing

127

IS sieve (mm)

Gradation range

Percentage passing through the sieve (%)

19

90–100

95

13.2

59–79

69

9.5

52–72

62

4.75

35–55

45

2.36

28–44

36

1.18

20–34

27

0.6

15–27

21

0.3

10–20

15

0.15

5–13

9

0.075

2–8

5

5 ANN ANN has arrived from the simulation of the biological system a nervosum. The ANN technique was developed in 1940. ANN will be used to solve problems without algorithmic issues or solutions that are very complex. Multilayer perceptron (MLP) system is well-known class of ANN. MLP has feedforward architectures. They are inherently able to estimate any function with an unbounded level of accuracy. These networks are often used to execute controlled learning, which is accomplished via the use of iterative training techniques. They used a back propagation algorithm. The MLP network involves the input layer, a minimum of one hidden layer neurons, and output layer. There are numerous processing units in all of those layers, and each unit is associated with units in the subsequent layer through weight constants. Each layer has number of different nodes. Each input is advanced by connecting weight constant of nodes. Finally, the output was achieved, with the total of merchandise being bypassed due to the activation function [3, 4, 8]. The equation below was used to explain the use of weight and bias in the prediction y = b0 +

h  k=1





wk × f sig bhk +

m 

 wik X i

i=1

where, • b0 —bias at the output layer; • wk —connection weight between the kth of the hidden layer and the single output neuron; • bhk —bias at the kth neuron of the hidden layer; • h—number of neurons in the hidden layer; • m—number of neurons in the input layer; • wik —connection weight between the ith input variable and the hidden layer;

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• X i —normalized input variable i; • y—normalized output variable.

6 Model Development The database has values of compaction level number, i.e., blows on each face of the mix, air void (%), voids in mineral aggregate (%), void filled with bitumen (%), bulk specific gravity of mix referred to dry conditioning, bulk density of mix referred to dry conditioning expressed in g/cc, stability of mix soaked in water for 40 min referred as dry conditioning expressed in kN, stability of mix soaked in water for 24 h referred as wet conditioning expressed in kN [21], and flow corresponding to dry and wet conditioning expressed in mm were considered as the input parameters for the model development. TSR expressed in percentages is considered as the output parameter for the respective model. To accurately measure the TSR of an asphalt mixture using ANN techniques, the findings from these concerns should be integrated into the model construction process. Because of these considerations, the ANN-based TSR formulation was examined. The R-value was used to choose the input parameters. Tables 4 and 5 demonstrate it. R with a value equal to 1 indicates a greater linear correlation, and close to 0 shows the poor correlation between input and output. Low the significance value better the correlation in Pearson’s correlation test.   TSR(%) = f G n , Va , G mb , BD, flwet , fldry , VMA, VFA, Swet , Sdry where, Gn Va G mb BD

Number of blow on each face Air Voids (%) Bulk specific gravity of mix referred to dry conditioning Bulk Density of Mix referred to dry conditioning (g/cc)

Table 4 Correlation between inputs and output TSR

Gn

BD

Va

VMA

VFA

R

0.563

0.496

−0.656

−0.656

0.658

Sig

0.029

0.060

0.008

0.008

0.008

Table 5 Correlation between inputs and outputs TSR

Sdry

Swet

f ldry

f lwet

G mb

R

0.444

0.541

0.235

−0.716

0.657

Sig

0.097

0.037

0.398

0.003

0.008

(1)

Moisture Damage Prediction of Hot Mix Asphalt …

f lwet f ldry VMA VFA Swet Sdry

129

Flow values of mix for dry conditioning (mm) Flow values of mix for wet conditioning (mm) Voids in mineral aggregate (%) Voids filled with bitumen (%) Stability of mix referred to wet conditioning(kN) Stability of mix referred to dry conditioning soak (kN)

6.1 Data Analysis The experimental test data will be preprocessed, and descriptive analysis will be carried out to understand the existing trend. Data will be prepared for statistical analysis. The experimental data will be divided into training, testing, and validation sets to reduce overfitting and underfitting of proposed model [15]. Pearson’s correlation test is commonly used for research in various fields. This is carried out by utilizing statistical techniques for bivariate analysis. It is based on the covariance approach and provides information on the magnitude of the correlation as well as the direction of the connection [16]. When it comes to machine learning modeling, one of the biggest issues is overfitting. The most common cause of overfitting is overtraining an algorithm. Even though the training error decreases in this situation, the testing error quickly rises. The term “overfit” is used to describe a learning system that performs well on the training data, but fails to accurately forecast fresh data that has never been seen before. Scientists proposed a scientific technique to reduce overfitting and to increase model generalization. The available data is categorized into three groups: learning, testing, and validation testing subsets for the purpose of the study [5]. The ANN algorithm’s educational process was aided by the training data. The testing data sets were used to assess the models’ capacity to handle data that they had not before learned. Because of the results of the runs, the models with the simplest performance on all data sets were eventually chosen. It was necessary to utilize the validation data to show the performance of final model on data that had no relevance to its creation. A number of combinations of training (70%), testing (15%), and validation (15%) data sets are taken into consideration.

6.2 Validation of Model Measuring Performance To assess the constructed model performances, the current research employs several statistical error measure criteria such as overfitting ratio (OFR), coefficient of correlation (R), mean absolute percentage error (MAPE), root mean squared Gmb (RMSE), and coefficient of determination (R2 ). A successful model should have minimal

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RMSE and MAPE value (imply a high degree of confidence in the model’s anticipated values), as well as a close R-value (which shows the degree of comparability between predicted and measured values). The RMSE is used to calculate the square error of the forecast in comparison to the actual values. To express RMSE, following equation is used:

n

1  2 yp − y RMSE =  n i=1

(2)

The degree to which forecasts are close to actual values is measured by the MAPE index. The mean absolute error is calculated using the following equation: MAPE =

1  y p − y y × 100 n

(3)

In statistics, the R-value measures the degree to which there is a linear connection between predicted and actual values. The R-value is computed with the help of equation given below:



n( y.y p ) − ( y)( y p ) R =   

n y 2 − ( y)2 n y 2p − ( y p )2

(4)

where, y and y p denote the real and forecasted values; n denotes the number of observations. In order to assess a model’s performance, following criteria should be used in conjunction with a rational hypothesis [17]. • |R| > 0.8: substantial correlation exists between the predicted and measured values. • 0.2 < |R| < 0.8: significant correlation exists between the predicted and measured values. • |R| < 0.2: modest correlation exists between the predicted and measured values. R2 is another metric used to evaluate the performance of the model. It is used to compare the developed model with the baseline so as to check which would perform better. The baseline is considered the mean of the data and sketches the line at the mean. The value will always come to be below or equal to 1. R2 = where,

F1 − F2 F1

(5)

Moisture Damage Prediction of Hot Mix Asphalt …

F1 =

n 

131

(y − y)2

(6)

i=1

F2 =

n  

yp − y

2

(7)

i=1

where y is the averages of the actual TSR. The overfitting ratio indicates whether a model is under-fitted or overfitted, and it is expressed as a percentage. It is possible to justify the model as a better model when its value is closer to one in comparison to the other models in the set. For calculating the OFR, equation below has been used: OFR =

RMSEin testing RMSEin training

(8)

6.3 ANN Model for TSR of HMA The structure and parameter of an ANN model have the greatest impact on its performance. Several scholars demonstrated simultaneously that a single hidden layer network is sufficient to uniformly estimate any continuous and nonlinear function. This is known as the universal approximation theorem. The Levenberg-Marquardt algorithms were used to build a single hidden layer feedforward neural network. The transfer function for input-hidden layer is chosen to be a log sigmoid of form 1/(1+e-x ) and purelin transfer function was adopted for hidden-output layer; 10 inputs and 1 output (TSR) were considered to carry out the performance of the neural network. Stopping criteria for the network were defined to achieve one of the following: 1000 number of training epochs (iteration), minimum gradient magnitude (1.00E + 10), or minimum performance value. The model that has the best performance is shown in Fig. 1. Input layer n = 10(G n , Va , G mb , BD, f lwet , f ldry , VMA, VFA, Swet , Sdry ) Output layer is having only 1 neuron providing the values of TSR. Single hidden layer is having 2 neuron (m = 2) nodes.

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Input Layer

Hidden Layer

Output Layer

Gn BD flwet fldry VMA TSR VFA Swet Sdry BD Va

Fig. 1 The 10-2-1 architecture of the ANN model for predicting TSR

6.4 Results and Discussion The ANN model for the prediction of TSR (%) was presented in Table 6. The performance of ANN model has an excellent result for R-value and OFR value. The result obtained from the 10-2-1 ANN model has shown in Table 6. The RMSE values obtained for the 10-2-1ANN model were less than 1 for training (0.95), testing (0.94), and validation (0.74), thus considered to be satisfactory for the prediction of TSR (%). The MAPE for the ANN model was found to be 60

15

Married

79

Unmarried

21

1

2

2

12

3

19

4

37

5

18

6

9

7

1

8

2

0.09) are removed from the model. The log-likelihood value for the overall model is determined. The log-likelihood value is determined by removing each individual significant attribute one by one. The PLL values for individual attributes is calculated by subtracting the loglikelihood value obtained after removal of a given attribute from log-likelihood value of overall model using Eq. (6). PLL = LLOM − LLIA

(6)

where PLL = Partial Log-likelihood; LLOM = Log-likelihood of the overall model; LLIA = Log-likelihood of the model after removal of an attribute.

4 Results 4.1 Stated Priority of Paratransit Attributes Table 2 shows the relative importance of paratransit attributes for Kolkata estimated using RIDIT analysis. Results reveal that the attributes with a high RIDIT score should be prioritised over the attributes with low RIDIT scores [43]. The results

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Table 2 Stated relative importance of fixed paratransit service quality attributes Service quality attributes

Score

Rank

Auto-rickshaw stop proximity

0.511

6

Hours of operation

0.572

3

Delay in total journey time

0.434

16

Crowding level inside the vehicle

0.481

10

Maintenance and cleaning frequency of stops

0.468

13

Headway of service

0.512

5

Waiting time at terminal auto-rickshaw stops

0.511

6

Waiting time at intermediate auto-rickshaw stops

0.460

15

Terminal auto-rickshaw stop design

0.473

11

Intermediate auto-rickshaw stop design

0.469

12

Auto-rickshaw design

0.508

8

Amenities within vehicle

0.468

13

Customer service

0.574

2

Auto-rickshaw fare

0.492

9

Safety and security

0.585

1

Environmental sustainability

0.521

4

show that the paratransit users of the city have stated ‘safety and security’, ‘customer service’, ‘hours of operation’, ‘environmental sustainability’, ‘headway of service’, ‘waiting time at terminal auto-rickshaw stops’ and ‘auto-rickshaw stop proximity’ as more important which are characteristics of a typical transit service except for ‘environmental sustainability’ which is due to the semi-open design of auto-rickshaws where passengers are exposed to air pollution, noise, etc. Table 2 shows that the importance of qualitative attributes is more than quantitative attributes. Therefore, this section further elaborates on these qualitative service attributes and discusses the relative weights of sub-attributes used to define these qualitative attributes. The results in Table 3 show the stated priority of the sub-attributes of the qualitative attributes. In the case of terminal auto-rickshaw stop design, the users gave more importance to the availability of adequate lighting, dustbins, signage, fire extinguishers, CCTV surveillance and drop-off bays. On the other hand, at intermediate auto-rickshaw stop design, seating, signage, drop-off bays and availability of time and fare information was prioritised. The user preferences, in the case of auto-rickshaw design, are in line with the type of auto-rickshaws. Users claim the availability of side rails and lighting to be important for the sake of safety. For customer service, users gave more importance to professionalism in providing paratransit services, follow-up and coordination of complaints and driver’s training. The users prioritised the suitability of fare structure and ease of payment over the integration of paratransit and public transit fare structure. Similarly, in the case of ‘safety and security’ users gave more importance to display of women and child helpline/emergency contact numbers,

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183

Table 3 Stated priority of sub-attributes of qualitative attributes Attributes and sub-attributes

Score Rank

Terminal auto-rickshaw stop design Parking bays

0.360

15

Controlled entry and exit for channelised movement

0.423

14

Pedestrian infrastructure to access the auto-rickshaw stops

0.459

12

Routes and stops’ location information at the stop and through other media

0.459

13

Integrated transit mode (bus) schedules at the stop and via other media

0.473

11

Journey time and fare information for routes at stops and via other media

0.483

10

Auto-rickshaw frequency information for routes at stops and via other media

0.515

8

Drop-off bays to allow other vehicles to pass

0.543

4

Shade from sun and rain

0.526

7

Seating infrastructure

0.545

3

Adequate lighting

0.583

1

Availability of dust bins

0.551

2

Availability of signage

0.538

6

Fire extinguishers

0.539

5

CCTV surveillance

0.503

9

Seating infrastructure

0.623

1

Shade from sun and rain

0.483

6

Journey time and fare information for various routes at the stop and through other media

0.541

4

Drop-off bays to allow other vehicles to pass

0.600

3

Availability of signage

0.604

2

Auto-rickshaw frequency information at for various routes at stops and through 0.398 other media

9

CCTV surveillance

0.404

8

Availability of dust bins

0.541

5

Adequate lighting

0.374

10

Fire extinguishers

0.427

7

Appropriate seating arrangement and leg space

0.477

4

Luggage space

0.491

3

Adequate lighting inside the vehicle

0.499

2

Side rails for protection

0.533

1

Music system

0.340

3

First aid kit

0.600

1

Intermediate auto-rickshaw stop design

Auto-rickshaw design

Amenities within vehicle

(continued)

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Table 3 (continued) Attributes and sub-attributes

Score Rank

GPS

0.560

2

Ease to submit complaints, opinions and requests

0.482

4

Follow-up and coordination regarding complaints and requests

0.524

2

Professionalism in service provision

0.542

1

Driver’s training

0.516

3

Help provided by drivers in loading/unloading of luggage and in case of emergency

0.478

5

Trip refusal

0.457

6

Integration with public transit fare structure

0.453

3

Ease of payment

0.519

2

Suitability of fare structure

0.528

1

Safety from road accidents whilst travelling inside vehicle

0.401

6

Safety from theft/robbery (at stop/vehicle)

0.429

5

Safety from assault/harassment (at stop/vehicle)

0.479

4

Availability of pink auto-rickshaw service

0.555

2

Availability of information about the driver

0.555

2

Display of women and child helpline/emergency contact numbers within stop/vehicle

0.582

1

Suffocation caused whilst travelling

0.512

1

Obstruction and other issues during parking

0.488

2

Customer service

Auto-rickshaw fare

Safety and security

Environmental sustainability

availability of driver information and pink auto-rickshaw service over other attributes. These basic requisites for ensuring security are lacking in Kolkata. Surveys also reveal that users in Kolkata are more sensitive to the perception of safety and security. In the case of ‘environmental sustainability’, users gave more importance to suffocation whilst travelling.

4.2 Derived Priority of Paratransit Attributes The results of the OLR model are given in Table 4. Table 5 presents the ranks of attributes derived using the PLL technique. The results show that ‘Auto-rickshaw fare’ is perceived as the most important determinant of users’ satisfaction of overall service quality of paratransit. Besides, ‘delay in total journey time’, ‘proximity of

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Table 4 Ordinal logistic regression model for Kolkata Variables Estimate

Std. error

Wald

Sig.

95% Confidence interval LB

Exp. B

Lower Upper

UB

Auto-rickshaw stop proximity Poor

−2.241

0.877

6.534

0.011

−3.959 −0.523 0.106 0.019

0.593

Average

−2.119

0.712

8.849

0.003

−3.515 −0.723 0.12

0.03

0.485

Good

Reference 0.247 0.02

3.027

Hours of operation Poor

−1.400

1.279

1.197

0.274

−3.908 1.108

Average

−1.795

0.607

8.747

0.003

−2.984 −0.605 0.166 0.051

0.546

Good

Reference

Delay in total journey time Poor

−2.250

0.702

10.275

0.001

−3.626 −0.874 0.105 0.027

0.417

Average

−2.172

0.697

9.720

0.002

−3.538 −0.807 0.114 0.029

0.446

Good

Reference

Crowding level inside the vehicle Poor

−2.233

0.927

5.803

0.016

−4.051 −0.416 0.107 0.017

0.659

Average

−1.028

0.725

2.013

0.156

−2.449 0.392

1.48

Good

Reference

0.358 0.086

Maintenance and cleaning frequency of stops Poor

−2.800

0.900

9.672

0.002

−4.565 −1.036 0.061 0.01

0.355

Average

−1.515

0.630

5.780

0.016

−2.749 −0.280 0.22

0.064

0.756

Good

Reference

Headway of service Poor

−2.854

0.947

9.083

0.003

−4.710 −0.998 0.058 0.009

0.369

Average

0.059

0.782

0.006

0.940

−1.473 1.591

4.91

Good

Reference

1.061 0.229

Waiting time at intermediate auto-rickshaw stops Poor

−2.288

0.709

10.399

0.001

−3.679 −0.897 0.101 0.025

0.408

Average

−0.599

0.621

0.932

0.334

−1.815 0.617

0.549 0.163

1.854

Good

Reference

Terminal auto-rickshaw stop design Poor

−1.525

0.778

3.845

0.050

−3.050 −0.001 0.218 0.047

0.999

Average

−0.903

0.691

1.710

0.191

−2.257 0.451

0.405 0.105

1.569

Good

Reference 0.013

−7.180 −0.827 0.018 0.001

0.437

Intermediate auto-rickshaw stop design Poor

−4.004

1.621

6.103

(continued)

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Table 4 (continued) Variables Estimate

Std. error

Wald

Sig.

95% Confidence interval LB

Average

−2.890

Good

Reference

Exp. B

Lower Upper

UB

1.546

3.493

0.062

−5.920 0.141

0.056 0.003

1.151

Auto-rickshaw design Poor

−1.210

0.809

2.239

0.135

−2.795 0.375

0.298 0.061

1.455

Average

−1.456

0.595

5.985

0.014

−2.623 −0.290 0.233 0.073

0.749

Good

Reference

1

Customer service Poor

−1.721

0.728

5.589

0.018

−3.149 −0.294 0.179 0.043

0.745

Average

−0.558

0.689

0.656

0.418

−1.907 0.792

2.208

Good

Reference

0.573 0.149

Auto-rickshaw fare Poor

−1.985

0.773

6.601

0.010

−3.500 −0.471 0.137 0.03

0.625

Average

−3.299

0.884

13.928

0.000

−5.032 −1.567 0.037 0.007

0.209

Good

Reference

Safety and security Poor

−2.292

0.713

10.331

0.001

−3.690 −0.894 0.101 0.025

0.409

Average

−1.131

0.649

3.037

0.081

−2.402 0.141

0.323 0.091

1.151

Good

Reference 0.303 0.077

1.198

Environmental sustainability Poor

−1.192

0.701

2.897

0.089

−2.566 0.181

Average

−1.428

0.693

4.246

0.039

−2.786 −0.070 0.24

Good

Reference

Model fitting information

−2 Log-likelihood Chi-square

Intercept only

369.493

Final

135.634

Values of pseudo R-square

Cox and Snell

0.627

Nagelkerke

0.794

McFadden

0.633

233.859

0.062

df

Sig.

30

0.000

0.933

auto-rickshaw stop’, ‘maintenance and cleaning frequency of stops’ and others are other major determinants of users’ perception of paratransit service quality. The paratransit users are not much concerned about ‘amenities within vehicle’ and ‘waiting time at terminal auto-rickshaw stops’ as these attributes are found to be insignificant determinants of users’ satisfaction of overall service quality. Though the provision of amenities like GPS is important in the vehicle, the paratransit users did not report

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Table 5 Derived relative importance of paratransit service quality attributes for fixed route services Attributes

Log-likelihood value

Full model

135.63

Partial effect change in LL

Auto-rickshaw fare

156.53

21.90

1

Delay in total journey time

152.17

16.54

2

Auto-rickshaw stop proximity

150.16

14.53

3

Maintenance and cleaning frequency of stops

149.75

14.12

4

Waiting time at intermediate auto-rickshaw stops

148.25

12.62

5

Safety and security

147.32

11.69

6

Headway of service

146.43

10.80

7

Intermediate auto-rickshaw stop design

144.83

9.20

8

Hours of operation

143.12

7.49

9

Crowding level inside the vehicle

142.87

7.24

10

Auto-rickshaw design

142.14

6.51

11

Customer service

141.84

6.21

12

Environmental sustainability

140.63

5.00

13

Terminal auto-rickshaw stop design

139.33

3.70

14

–

Rank –

any security issues concerned with the absence of GPS. The GPS provision enhances the user experience by enabling live tracking of the vehicles which is an important safety and performance tracking parameter. It helps in providing vehicle arrival and delay information in the case of fully regulated and controlled services. Provision of GPS is also beneficial for both fleet operators and regulators to check vehicle speed, idling behaviour, for rerouting vehicles (fleet management), to track stolen vehicles and to check whether the trips are being done in assigned routes and zone. Whilst the results mostly match our earlier rankings derived from stated priority, there are also certain differences which are mostly due to the present service conditions where people are satisfied with certain aspects of the service.

5 Discussion The result of the analysis is summarised in Table 6 which shows that the relative importance of attributes varies across prioritisation techniques. Whilst the derived importance of some of the attributes such as ‘auto-rickshaw fare’ is more than stated

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Table 6 Comparison of ranks of attributes for derived and stated importance Attributes

Derived importance

Stated importance

Auto-rickshaw stop proximity

3

6

Hours of operation

9

3

Delay in total journey time

2

16

Crowding level inside the vehicle

10

10

Maintenance and cleaning frequency of stops

4

13

Headway of service

7

5

Waiting time at terminal auto-rickshaw stops

–

6

Waiting time at intermediate auto-rickshaw stops

5

15

Terminal auto-rickshaw stop design

14

11

Intermediate auto-rickshaw stop design

8

12

Auto-rickshaw design

12

8

Amenities within vehicle

–

13

Customer service

12

2

Auto-rickshaw fare

1

9

Safety and security

6

1

Environmental sustainability

13

4

importance, the stated importance of few attributes is more than the derived importance. Therefore, both stated and derived importance methods are important for analysing users’ preferences. Stated importance is required to identify the preferences of potential users who have not experienced the service or existing users who have not experienced certain aspects of the service, whereas derived importance could be used to identify attributes which need to be immediately addressed. A combination of these methods will help authorities to adopt both short term (attributes which are more preferred over other attributes) and long term (attributes which are less preferred but are important) strategies towards the improvement of service quality of paratransit attributes according to user preferences. The results reveal that the regulations could be introduced to improve the ‘safety and security’ by making it mandatory to display driver details and emergency contact numbers inside auto-rickshaws in Indian cities as other studies conducted in different cities of India have also found that safety is an important concern of paratransit users [1, 4]. The other service attributes that passengers have identified to be important for fixed route services are ‘customer service’, ‘hours of operation’ and ‘environmental sustainability’. However, none of these is of immediate concern as established in Table 6. Results show that the passengers in Kolkata may be presently content with the major aspects of ‘customer service’ and ‘hours of operation’. Similarly, people may realise that even though ‘environmental sustainability’ is important, the improvement of this aspect may be difficult in the short term. This research also proposes that the government should immediately frame policies and regulations for the improvement of the operation of these feeder services as

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per users’ preferences and expectations. For example, ‘auto-rickshaw fare’, ‘waiting time at intermediate auto-rickshaw stops’ and ‘delay in total journey time’ are all operational aspects controlled by the union and require immediate improvement. Following a hybrid dispatch mode incorporating both the fixed frequency and queue approach, the threshold number of passengers within the vehicle can be kept lower than capacity whilst following a fixed frequency, particularly during peak hours. This will ensure higher seat availability at intermediate stops which will lower waiting time and improve headway without compromising operator income. ‘Delay in total journey time’ is primarily due to hawking for customers at intermediate stops during off-peak hours in case the vehicle is not filled. This could be improved by enforcing strict arrival and departure policies including maintaining a queue at busy autorickshaw pick-up and drop-off points/stops by police personnel. A major issue of concern amongst users is the unregulated fare demanded by auto-rickshaw operators during late hours. Incentives in the form of fare surcharge can be legally allowed during extended service hours to encourage private operators to provide service during these lean periods and users the knowledge and assurance of a fixed fare structure. Regular maintenance and cleaning of auto-rickshaw stops also need to be taken up by the city authorities to improve paratransit service quality. This also highlights the fact that the improvement of paratransit services can be only achieved through the integrated effort of operators, union authorities, government regulatory authorities and city officials. The relative importance of some of the factors influencing the perception of paratransit users is similar to the previous studies carried out in the Indian cities and the other cities of developing countries, but some new attributes are also determined to be more important in this research in comparison to previous studies [1, 4, 40]. Unlike earlier studies, this research also helps in prioritisation paratransit service attributes based on which phase-wise development of services can be adopted in case of limited availability of funds.

6 Conclusion The current research prioritises and compares the major and minor determinants of service quality of fixed route paratransit modes using stated and derived importance methods. The findings of this study would help in the adoption and implementation of attribute-specific policies for the improvement of service quality of fixed route paratransit in the cities of India and other developing countries to achieve sustainable transportation goals. The findings of the current research also indicate that the priority of paratransit attributes vary across methods. Therefore, the users’ preferences should be analysed using both derived and stated importance methods. There is an immediate requirement of route and fleet size rationalisation which could help in improving stop proximity and headway of service, respectively. Similarly, the government could suggest guidelines for setting service headway during different time periods for different routes similar to transit services and make provisions for improvement

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of safety and security of users. However, the sample size of the current research may have an impact on the findings and should be taken care of in future works. Future studies could also consider developing models for determining fleet size for paratransit services since many of the service attributes of fixed route paratransit are similar to urban bus service. Forthcoming research on service quality assessment can also focus on integration between transit and paratransit services and provide service assessment for an integrated system. Acknowledgements The authors are grateful to the Ministry of Housing and Urban Affairs (MoHUA), India, for funding this research program under project No. K-14011/18/2011-UT-IV. The authors are also thankful to the Indian Institute of Technology Kharagpur for providing the required facilities to prepare this research paper.

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40. Rahman F, Haque MF, Ehsan MT, Rahman SM, Hadiuzzaman M (2017) Determination of users’ perception of paratransit service quality in Dhaka city based on users perception. Int J Educ Appl Res 7(1):19–24 41. Rashid M, Pandit D (2019) Analysis of service quality of household toilets expected by households practicing open defecation: a study in rural settlements of Bihar, India. Environ Dev Sustain 21(5):2487–2506. https://doi.org/10.1007/s10668-018-0145-8 42. Rashid M, Pandit D (2007) Service quality of household toilets in rural settlements of India: an assessment from users’ perspective. J Water Sanitation Hyg Dev 7(4):589–600. https://doi. org/10.2166/washdev.2017.054 43. Rashid M, Pandit D (2017) Determination of appropriate service quality attributes for household toilets in rural settlements of India based on user perception. Environ Dev Sustain 19(4):1381– 1406. https://doi.org/10.1007/s10668-016-9807-6 44. Roman C, Martín JC, Espino R (2014) Using stated preferences to analyze the service quality of public transport. Int J Sustain Transp 8(1):28–46. https://doi.org/10.1080/15568318.2012. 758460 45. Roos D, Alschuler D (1975) Paratransit—existing issues and future directions. Transportation 4(4):335–350 46. Sadhukhan S, Banerjee UK, Maitra B (2015) Commuters’ perception towards transfer facility attributes in and around metro stations: experience in Kolkata. J Urban Plann Dev 141(4):1–8 47. Sharma D, Pandit D (2021) Determining the level of service measures to evaluate service quality of fixed-route shared motorized para-transit services. Transp Policy 100:176–186. https://doi. org/10.1016/j.tranpol.2020.11.002 48. Sharma D, Pandit D, Bose T (2020) Determination of service quality attributes based on user perception for paratransit services in developing country like India. Transp Res Procedia 48:3577–3594 49. Shen W, Xiao W, Wang X (2016) Passenger satisfaction evaluation model for urban rail transit: a structural equation modeling based on partial least squares. Transp Policy 46:20–31 50. Tyrinopoulos Y, Aifadopoulou G (2008) A complete methodology for the quality control of passenger services in the public transport business. Eur Transp/Trasporti Europei 38(38):1–16 51. Woolf SE, Joubert JW (2013) A people-centred view on paratransit in South Africa. Cities 35:284–293. https://doi.org/10.1016/j.cities.2013.04.005 52. Wu C-H (2007) On the application of grey relational analysis and RIDIT analysis to likert scale surveys. Int Math Forum 2(14):675–687 53. Zhang PG (2010) Data mining and knowledge discovery handbook, 2nd edn. Springer, New York

Road Network Analysis of Major Destinations in Guwahati City Using GIS Mayurakshi Hazarika and Amit Kumar Yadav

Abstract The transportation system plays a prominent role in the urban spatial structure. It is the main social-economy operation of the city. Transport planning is a complex process requiring meticulous forecasting of potential needs and review of current urban travel trends. Sustainable development is enabled by successful route planning and accessibility. The GIS-based Network Analyst allows users to dynamically model realistic network conditions at various times of the day, including turn restrictions, speed limits, and traffic conditions. A GIS can be used to monitor the transport system, network conditions, the shortest or best route to the destination, and the closest services. The main purpose of this paper is to provide an enhanced road network analysis that uses the capabilities of GIS to identify the fastest as well as the shortest route between the two busiest hubs in Guwahati city. A basemap of the city is downloaded using the QuickMapServices plugin. The network analyses such as routing and nearest facility are carried out in this project using various plugins available in QGIS. The points of origin and destination were chosen to address the network in order to decide the shortest path as well as the fastest path and to serve the purpose of the analysis. The study would raise awareness of the potential for data collection, management, and analysis of geographical information technology. It will allow them to access low-cost technology and freeware solutions to operate in GIS for decision-making purposes. It will highlight the gaps and restrictions impacting the use of GIS in the transport planning field for the responsible offices to deal with. Keywords Transport planning · Urban travel trends · Sustainable development · GIS · Network analyst · QGIS

M. Hazarika (B) · A. K. Yadav Department of Transport Science and Technology, Central University of Jharkhand, Brambe, Ranchi 835205, India e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_14

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1 Introduction To promote sustainable development in today’s world, it is very important for a country to maintain immaculate infrastructural development, in which a wellorganized transportation system plays a pivotal role. From an economic point of view, in almost all the countries, developed or developing, cities are undeniably a substantial source of their national economic growth and transport sector takes on a dominant role in the livelihood of these cities [1]. In other words, mobility and connectivity has a direct bearing on the economic health of a region. Transportation contributes a significant portion of a nation’s Gross Domestic Product (GDP) and is thus crucial to its development. Physical infrastructure, as well as the associated social services required to support it, must be built, operated and maintained with prior knowledge, skilled precision, and expertise in geo-information. Data was collected from geospatial sources form the basis for planning, engineering, asset management, and operations associated with every type of transportation mode, across all levels of government and administration, Transport Research Board [2]. Using geographic information systems (GIS), data, hardware, and software are integrated together to capture, manage, analyse, and visualize all information related to a particular territory. In the form of maps, globes, reports, and charts, GIS unfolds associations, figures, and trends which allows users to view, understand, interrogate, interpret and analyse data in many ways. GIS technology can be used in conjunction with any enterprise information system framework, and the information provided by it can be easily understood and just as quickly shared which makes it simple enough for users to solve any problems. Planning new routes is easier and effective with GIS-based systems to develop, analyse and deliver essential economic, demographic, and cost estimates at the point of decision. The software assists with the analysis of existing routes, the collection of data, and the notification of riders of route modifications to improve services in future. Location planning also includes route planning, such as analysing catchment areas for different sites, calculating the amount of time people spend driving to and from the site, maximizing potential customer inflow, and ensuring optimal accessibility. Many cities around the world have been looking at new ways to improve public transit systems due to growing congestion in traffic, the need to preserve the environment, and concerns associated with road safety. Various aspects such as transportation planning, evaluation of the retail market, assessment of accessibility, allocation of service, and more are highly influenced by models of transportation and road network system. A clear understanding of the human mobility behaviour can be deducted from a detailed study of road network system. In addition to routing, travel directions, nearest facility, and service area analysis, GIS-based Network Analyst offers comprehensive spatial analysis using the network. The data from geographic information system (GIS) can be comfortably constructed into networks by using a sophisticated network data model. A GIS operation called network analysis examines a dataset that represents a geographic network or a real-life network. An analysis of the properties of networked

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systems, both natural and man-made, can be conducted to understand flow patterns within and around these systems and to provide the locational analysis. This tool simulates real-life information networks with edge-node topology. Graph theory and topology are mathematical sub-disciplines underlying its function. Network analysis is a set of interlinked lines. These interlinked lines represent railways, streams, roads, waterlines, pipelines, telecommunication lines, etc., which are typically analysed as a network. It analyses the movement of people, connectivity and accessibility of railway, roads, pipelines and telecommunications, and movement of goods and services. Any type of network is connected by vertices and edges. This network is measured and compared by plotting graphs and linkages of objects or features. Network depends on topological properties, i.e. connectivity, adjacency, and incidence. Some of the types of operation that can be done are shortest path analysis, best route, closest facility, service area analysis, location-allocation, OD cost matrix, network partitioning, and route optimization. The objectives of the project are stated as follows: i. ii. iii.

Identification of different routes in Guwahati city with a prepared road network map. Determination of the shortest as well as fastest route between two busiest hubs in the city by proper digitization and analysis. Locating the nearest facility to the incident point based on shortest distance.

2 Literature Review Silalahi et al. (2020) used the GIS-based network analysis and a spatial distribution model of the COVID-19 pandemic to assess the accessibility of referral hospitals in Jakarta, Indonesia. They developed a Standard Deviational Ellipse (SDE) model to evaluate the geographical distribution of the pandemic as well as generate service areas and OD cost matrix to aid the pre-existing referral healthcare facilities. Based on distance-based coverage area, they found more than 12.4 million people (86.7%) living in the referral hospital’s well-served area. A total of 2637 positive-infected cases were discovered, with the majority of them concentrated in West Jakarta (1096 cases). The OD cost matrix indicated a total of 908 unassigned cases from patients’ centroid within a 10 km radius, with a significant concentration in West Jakarta [3]. To eliminate traffic issues to a larger extent, Das et al. (2019) evaluated the shortest between two places in Guwahati city by generating a road network map with detailed analysis and digitization of its current road network system. The analysis is done using network characteristics such as journey time and distance, travel cost and restrictions, vehicle restraints, and so on. They found out that the network analysis will also save time and cost and prevent traffic congestion leading to lower emissions of pollutants and assist tourists and business entrepreneurs access the famous landmarks or trade centres. They concluded that the traffic congestion in the city can be dealt by segregating the traffic flow consisting various road transport into alternative and

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less frequented routes on the basis of operational characteristics and vehicle mileage performance and amending the transport policy for the city [4]. By using network analysis features of ArcGIS software, Kharel et al. (2019) mainly focussed on boosting the traffic conditions and making it easier for the users to traverse around. The location-allocation function in ArcGIS is used to relocate the existing bus stops, considering a variety of elements such as traffic lights, traffic restrictions, and so forth. Information concerning bus routes with a bus stop between the starting and the destination places is generated by transport network analysis which is used to schedule a route from one person to another. The model is designed in such a manner that the users can plan their travel depending on the specifics of the bus route, such as when and how to utilize the public and private modes of transportation. The study was carried out in order to generate service area layers, assign bus stops locations, and track buses by sorting their route numbers. The existing petrol stations in the region were located using the service area function. This facilitates consumers’ knowledge of neighbouring gas pumps and refuelling needs, as well as identifying a feasible place for establishing a new petrol station in the city. Location-allocation models aided in assessing the requirements for the bus stops at various demand areas, while traffic signal buffers assisted in the relocation of the problematic bus stops from their previous positions to a nearby location to alleviate congestion problems [5]. Khaing et al. (2018) calculated the best route using the GIS application and the Dijkstra’s algorithm. The framework is built by using free and open-source software incorporating the open-source GIS tools. A prototype application based on short bus routes in Yangon city has been designed and implemented. It includes a planning system for determining the best route and mode of transportation as well as the fare associated with it. By accessing the Android application of the proposed approach, the users may acquire the information on the best route from their current location to their chosen destination. They displayed the bus line or the bus route and the bus stops information that the bus passengers will need to travel without any hassle or delay. They also tried to incorporate it into a mobile application that would provide information over the Internet using the GIS [6]. Kharel et al. (2018) evaluated the best routes and the nearest facility on a road network using the network analysis function in ArcGIS. The research area chosen is the central region of Bengaluru of the Karnataka state. The findings are used to determine the most efficient path to a facility at any specific point of time. The optimal path is determined by identifying the least distance between two location points. The model was tested after two pickup points were used on the layers. Different parameters such as travel time, distance, and cost were evaluated by the model for optimum path. The nearest facility such as petrol station is identified by the least distance between the actual location and the surrounding establishments. The shortest distance between the desired position on the road network and the nearest petrol station was established, and driving directions were displayed [7]. Kerekes (2018) aimed to analyse the road network of the municipality of ClujNapoca and develop an ideal route model for more dependable and faster access to the medical services. The main purpose of this study was to create an elaborate

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and a more complex methodology in order to identify the optimal routes for emergency situations. Using analyses, the tendency of the vehicle flux in different time periods is observed. The differences in the tendency are significant and are taken into consideration by the emergency units to improve the response time. Minor differences between the theoretical and empirical results are observed, and hence, the applied methodology can be used to identify the optimal routes for each district. The estimation of the ambulance response time for different regions in the city is done by considering traffic as the most important speed restricting element [8]. Farooq et al. (2018) developed a transportation model to link Beijing and XiongAn, minimizing the journey time between the two destination and boosting the transfer capacity. The present transportation network between the two cities is evaluated, and a new network that can accommodate the future demand is proposed. They presented a transport scheme based on various criteria for developing the neighbouring settlements around the city. An analytical hierarchy methodology was used to analyse several criteria from a range of various possibilities, from which the best outcome was deduced for the research area. The cost of travel, trip duration, type of infrastructure, connectivity, safety, dependability, accessibility, type of terminal connection, and environmentally friendly transportation were all considered utilizing an analytical hierarchy process tool. Based on the findings, a high speed railway line is proposed for the research area. Journey time, travel expenses, safety, dependability, accessibility, and the environment were deemed to be the most important factors in determining the best alternative. The decision of a high speed rail line beside the metro line to meet the transportation demand of the study area was also supported by the sensitivity analysis [9]. Kumar et al. (2017) investigated the potential for network analysis in the Kumaon region’s Nainital. The research used a simplified vector data format to present an ArcGIS transport data model for the city of Nainital, with GIS offering an effective means of integrating fundamental networks. It gives a framework for understanding a transportation system and aids in the creation of a geodatabase. The transport data model in ArcGIS leverages the flexibility of object orientation to specify entities and their connections. The goal of the project was to determine the best route. The road network of the research area was poorly designed; most of the roads are too small to allow large vehicles to drive about freely, and the majority of them are only suitable for pedestrians. The town’s traffic flow is congested due to the small amount of road space available. With a few exceptions, the majority of the internal roads are single lane. The lack of footpaths in the town has an effect on pedestrian movement. A Google earth imagery was used in this study, and the digitization was done with the use of a shape file generated for various analyses. The ArcGIS-based Network Analyst offers a number of solutions. The efficiency of services was evaluated based on distance and time by using the network analysis tool. It also aids in analysing the gaps in the existing facilities and generating service areas around it based on distance covered and time taken to access the area. The research area has been subjected to a network review for tourism destinations using remote sensing and GIS. It is found that the route with the shortest distance is optimal for tourism. Acquiring visual and precise understanding of the tourist destinations, as well as using network analysis

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software to optimize sightseeing planning, increases tourism’s capacity. Furthermore, users can save time by using GIS [10]. Ahmed et al. (2017) provided an extended network analysis that uses GIS capabilities to determine the optimum path from the incident scene to any healthcare providers in the Greater Cairo metropolitan area on the basis of journey time. Furthermore, the proposed approach incorporates historical traffic data into the study, resulting in more accurate conclusions that are appropriate for practical road networks. In congested cities like Cairo, the Dijkstra routing algorithms included in the ArcGIS software are the most ideal tool for network analysis. According to the findings of this paper, the travel time for the optimum route is 22% shorter than travel time for the shortest distance route. Therefore, based on the findings collected, this study proposed that the GIS’s best routing algorithm is better than the shortest routing algorithm particularly in circumstances of urgency in congested metropolis such as GCR [11]. Gitau and Mundia (2017) performed spatial modelling to establish site selection for optimal route by using GIS analyses and weights criteria utilizing the Analytic Hierarchical Process. Satellite images of the study area were collected, analysed, and reviewed in order to gather preliminary data for the area along with the land use land cover chart to determine the best road route location. To allow integration with GIS analysis, a Geodatabase was developed. Sound and dust studies, as well as shapefiles of soils, pre-existing highways, villages, rivers, and vegetations were included in the GIS database. Questionnaires were given to various experts and weights were calculated using the Analytical Hierarchical Process (AHP). In each subclass, the weights from various criteria resulted in unique normalized Eigen vectors. The weights were used to create a model to determine the optimal way. Topographical and physical elements such as slope, vegetation, and existing rivers were given a lot of consideration, as per the findings. Infrastructure was next, followed by the atmosphere, and finally soils, based on the weights calculated. Two optimal routes were discovered as a result of the study, which varied from the project’s initial proposed path [12]. The aim of Jalegar and Begum (2017) was to find the shortest and most alternative routes from various villages to growth centres. The study has made a sustained effort to create rural road databases based on Geographic Information Systems (GIS) such that the final result will help planners, policymakers, academics, and other stakeholders in the rural road sector. The research area is in the Sangareddy District of Telangana, India. Growth centres were chosen from 39 settlements depending on the availability of rural infrastructure and the number of trough routes in the research region. The village of Mirzapur was identified as the mandal’s development centre. In addition, connectivity indexes were used to conduct structural analyses of the road in the sample, and a maintenance approach was developed based on the population serviced by the link and the average PCI value of the link [13]. Balasubramani et al. (2016) used the Network Analyst tool in ArcGIS to generate the service areas for three fire service stations of Madurai city with specified impedance i.e. travel time. Ten zones are formed around each fire station based on travel time by road network, with each zone representing one minute of travel time coverage. These concentrated service areas demonstrate how accessibility is

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influenced by the impedances selected, and all of these zones are merged to assess the non-serviced area, where ten minutes of travel is insufficient to reach a destination. Non-serviced areas are to be regarded as critical zone. High risk zones, on the other hand, are derived from the departmental records of fire stations. Both critical zone and risk prone area are superimposed to identify optimal locations for new fire service stations with the help of network analyst tool [14]. Elsheikh et al. (2016) proposed creating digital route driven maps and, as a result, deploying digital spatially enabled location-based computer programmes that can be downloaded to a laptop or mobile device as a tool to enhance services in the event of an emergency, such as an accident. This was accomplished by using GIS’s network analysis and visualization capabilities by mapping the facilities based on journey time to determine the best route to the nearest healthcare facility [15]. The applications of the GIS technology were reviewed by Patil et al. (2015) by performing an analysis of the transportation network of the Nanded Taluka. They displayed all the spatial and attribute data such as roads and its types, bus stops, infrastructural support and land use, administrative or constitutional boundaries, etc., using the GIS software. It conducts network analysis on the data to verify the reliability and suitability for further processing. Data in network analysis is used in various applications such as shortest route analysis, tour analysis, and index computation. The road generalization process is executed for more accurate network analysis, after the development of road network using the GIS application. The findings demonstrate that the Nanded has adequate transportation facilities as well as the usefulness of GIS in administration for the Local Governments, particularly Municipal Corporations and Taluka level government organizations [16]. Dabhade et al. (2015) attempted to address this issue of unfamiliar parts of the metropolis by representing the shortest path facility for locating hospitals closest to the user’s location. They used ArcGIS software and the Dijkstra’s algorithm to calculate the route with the shortest distance between two location points. They generate the optimal path between Mangal Medi Center, Osmanpura, Aurangabad, to Dr. Rajguru Hair Clinic, Bajrang Chowk, Aurangabad, by calculating the distance based on road length. It was followed by a closest facility analysis where it picks the nearest route based on shortest distance to that particular hospital facility. In this project, the service area is created around the hospital point to find out how many other facilities are present within the 500 m area. In the event that one of the hospitals is closed, the user will quickly locate another within the accessible region [17]. Kumar and Kumar (2014) investigated the use of network analysis in determining the best service area for various public services in Chandigarh, including healthcare facilities, schools, and fire departments. A Google Earth image of Chandigarh city was used in this analysis, which was subsequently geo-referenced. Digitization was done with the help of a shapefile generated for various analyses. The shortest route which is more efficient in terms of less time-consuming and saves travel cost subsequently is determined using the network analysis tool in ArcGIS. The efficiency of services based on distance and time was calculated using the network analysis approach. Services such as hospitals, schools, and fire stations were selected to conduct the service area analysis based on distance and time. The actual service area

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surrounding various facilities is analysed and whether these facilities are sufficient for that region [18]. Arora and Pandey (2013) studied the allocation of ATMs of different banks as well as healthcare facilities of South-west Delhi using network analysis. The data in this paper is divided into four categories: Google Earth data (Satellite Imagery), data collected from field surveys, data collected from open source, and GIS Datasets (which include data derived from remote sensing as well as field measurements and surveys). The objectives of their study were to develop a coherently formulated alternative transport planning methodology, route analysis based on shortest distance from the accident site to the nearest healthcare facility and to evaluate the accessibility of ATM services from four banks: Axis Bank, ICICI Bank, Punjab National Bank, and State Bank of India. The field survey shows the good distribution of the SBI (State Bank of India) and Axis Bank ATMs whereas ATMs of Punjab National Bank are inadequate. The findings of the study also reveal that the region is divided into five healthcare facilities within 3–5 min of accessibility. The study area’s road network and accessibility are of a high quality [19]. Kakade (2013) aimed to use modern RS and GIS tools and methodologies to validate a development plan targeted to limit urban dominance and its related issues. The relevant land use structures and transport networks using remote sensing data are developed within the GIS platform. He highlighted the ways of locating the nearest hospitals to the accident site, police cars to a crime scene, and stores to a customer’s addresses and hence finding the best route using the network analysis tool of ArcGIS. ArcGIS software was used to carry out the research. Using ArcGIS programme and its beneficial tools, we were able to perform digitization, attribution, topological error elimination, modelling of digitized data, network dataset construction, and data network analysis [20]. For route optimization of tourist attractions in Delhi, Gill and Bharath (2013) employed the GIS-based Network Analysis. Their research revealed the best path for tourists from their starting point to their final destination, taking into the amount of time spent at each site. The analysis of route is dependent on the impedance, a cost property of the road network (time or length). The analysis is based on the approximate travel time and distance in terms of time and length impedance. If length is set as the impedance, the shortest distance path is proposed. However, if time is selected as the impedance, the fastest route in terms of anticipated speed based on the road with is determined [21]. In order to monitor and provide online real-time information of pilgrim movement in a real time, Koshak and Nour (2013) proposed an integrated advanced application of Radio Frequency Identification (RFID) technology along with Geographic Information Systems (GIS). The technology also captures and retains data that will be used in future transportation planning. RFID tags embedded in pilgrim transport vehicles and RFID readers mounted at strategic locations make up the RFID scheme. The data received by the RFID readers is transmitted wirelessly to a workstation for analysis and then presented on the interactive maps using GIS application. The Hajj has successfully implemented the prototype of this project. The technology facilitated the management of the movement of pilgrims from Arafat to Muzdalifah and

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also supplied traffic information such as average speed and travel time for the traffic network. The system also allows Hajj authorities to monitor and regulate pilgrim movements, as well as store vital data for future urban transportation planning and management [22]. Ilayaraja (2013) carried out the study with the goal of connecting various road networks in Neyveli Township and determining the shortest distance route. Quantum GIS was used to digitize the road network, and Road graph plugin analysis was used to analyse it. The tool shortest path in Quantum GIS is used to find the shortest distance route connecting any two point locations on the road network. The research can also be implemented for better information by recognizing the route between the two known addresses, by linking the entire database of addresses [23]. Ejiagha et al. (2012) presented the study with a GIS-based analysis of the accessibility of healthcare services in Enugu metropolitan regions. This was accomplished by acquiring an Enugu State basemap depicting the geospatial extent of Enugu Metropolitan, using handheld GPS to determine the spatial locations of the medical services in the Enugu Metropolitan area, generating a spatial database of existing health centres, as well as illustrating the potentials of GIS in evaluating accessibility to medical services in Enugu Metropolitan through various analyses. The research involves network analysis to determine the proximity between the healthcare services and the shortest distance route to them. The main software used was ARCGIS 9.3. Geocal and Microsoft Office products were useful for coordinate conversions. The findings of the research revealed the spatial distribution of health institutions within Enugu’s metropolitan area, as well as the nearest emergency facilities and routes to those health institutions. The majority of hospitals have been found in the Enugu North local government area (LGA). There were fewer healthcare facilities in other settlements and LGAs [24].

3 Study Area Guwahati is the largest city in the Indian state of Assam, as well as north-eastern India’s largest metropolis. The latitude of Guwahati is 26.148043, and the longitude is 91.731377. Guwahati is in the cities category of the India region, with GPS coordinates of 26° 8 52.9548 N and 91° 43 52.9572 E. Guwahati is located between the Brahmaputra River and the Shillong plateau’s foothills, with LGB International Airport to the west and Narengi to the east. A map of Guwahati city is shown in Fig. 1. Guwahati is one of 98 Indian cities to be designated as Smart Cities as part of a project spearheaded by India’s Ministry of Urban Development. Road traffic problems like congestion, insufficient parking space, exorbitant bus, auto rickshaw, rickshaw and taxi fares, time-consuming and uncomfortable travel, unpredictable travel time delays, and road accidents are taking a serious shape in the city. During peak hours, almost the entire city is clogged with traffic, but the R.G.

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Fig. 1 Map of Guwahati City. Source Guwahati-Google Maps

Baruah Road, sections of the G.S. Road, Maligaon, and Paltan Bazar are particularly congested as shown in Fig. 2. Google maps picked up the NH 27 highway (shown in Fig. 3) to travel from Khanapara to Jalukbari based on historical traffic patterns for the roads of Guwahati city over time as well as live traffic conditions and combine both the databases to generate predictions.

Fig. 2 Traffic congestion in Guwahati City. Source Unsafe Guwahati roads-Sentinelassam

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Fig. 3 Preferred route from Khanapara to Jalukbari as shown in Google maps. Source Khanapara, Assam, India to Jalukbari, Assam, India—Google maps

4 Methodology A flowchart depicting the different steps of methodology of the road network analysis of a city is presented in Fig. 4. In QGIS, the first step is to bring the map canvas to the OpenStreetMap Basemap and then zoom it into our area of interest. QGIS has plugins with the help of which we can add free online basemaps such as QuickMapServices plugin, OpenLayers plugin, and XYZ Tiles layer. In this project, a basemap of the Guwahati city is downloaded from OpenStreetMap which is an open source using QuickMapServices plugin. The OpenStreetMap database consists of various spatial data such as roads, buildings, waterways, railways, and point data of public services such as hospitals, ATM, bank, schools, colleges, and fire stations. As part of its basic data structures (its nodes, ways, and relations), OpenStreetMap represents real-world physical features (such as roads and buildings) using tags. Node, way, or relation tags refer to specific geographic attributes of the feature being displayed. As a result of OpenStreetMap’s free tagging system, the map can include an unlimited number of attributes, each describing a particular feature. Several key-value pairs have been agreed upon by the community for the most commonly used tags, and these serve as informal standards. The QGIS plugin QuickOSM and the OSMDownloader are the only two that are designed to download the OSM data. The road network of Guwahati city is visualized, managed, and analysed in the QGIS software using QuickOSM plugin. With QuickOSM, one has access to Overpass API, an API designed specifically for serving up custom parts of the OSM map data. The API has been designed for data consumers and a search criterion can be specified (location, object type, tags, proximity, or combinations thereof) in the API to allow the access of millions of elements in minutes.

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Data Collection

Spatial Data

Attribute Data

Identification of Road Networks

Analysis using GIS techniques

Routing Analysis

Nearest Facility Analysis

Results and Discussions

Conclusion

Fig. 4 Flowchart of the methodology followed in this project

QGIS provides a wide range of network analysis tools which can be used according to the data present at hand. Some of the network analysis tools used in QGIS are: i.

Based on local network data (a) (b)

ii.

Default QGIS Processing network analysis tools QNEAT3 plugin

Based on web services (a) (b) (c)

Hqgis plugin ORS Tools plugin TravelTime platform plugin

Network Analysis problems studied in this paper: i.

Routing: Using the Network Analyst, one can access several locations at once or travel between them in the most efficient manner possible. Place points on the screen, type an address, or use points from an existing feature class or feature layer to specify the locations interactively. Depending on the impedance selected, the most favourable route can be the fastest, the one with the least

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distance, or the most aesthetically pleasing route. Though when time is considered as the impedance, the quickest route is considered as the best route. As a result, the best route is the one with the lowest impedance, where the impedance is set by the consumer. When deciding the best path, any valid network cost attribute can be used as the impedance. Nearest Facility: Locating the hospital with the least distance from an accident site or any point location, the closest police cars to a crime scene, and the closest store to a customer’s address are all examples of closest facility problems. When looking for nearby facilities, we can decide how many we want to find and whether we want to go towards or away from them. We can present the best route to or from the closest facilities, return the travel cost for each route, and display directions to each facility once the closest facilities have been identified. We may also define an impedance cut-off above which Network Analyst should stop looking for a facility. For example, we can set up the closest facility problem to look for hospitals within 15 min of an accident’s location. The results would exclude all hospitals that require more than 15 min to enter.

5 Analysis QGIS plugins extend the capabilities of the QGIS application. There are numerous built-in plugins in the QGIS application, and it can also be downloaded additionally. These plugins can also be directly installed into the QGIS application using the QGIS Plugin Manager. In this project, we have imported a OpenStreetMap basemap into QGIS using QuickMapServices plugin as a background layer. OSM data can be accessed dynamically with the help of Overpass, an API of OpenStreetMap. QuickOSM is another such plugin in the QGIS application that presents an effective graphical interface for querying and downloading OpenStreetMap data. We can use this plugin for either searching OSM data by key/value pairs, defining geographical extent (either map canvas or a layer extent), or saving queries for running them later. Extracting layers from OSM might take a while since they depend on the Internet speed and the amount of data that we want to load. Once this is done, new temporary layers, referred to as “scratch” layers in QGIS, will be added in the layer panel on the left-hand side. A well-connected road network map is drawn in QGIS software using its various plugins. With the help of QuickOSM plugin, network lines based on different classes of road are laid. The roads in Guwahati city are classified into six classes such as trunk roads, primary roads, secondary roads, tertiary roads, unclassified roads, and residential roads. The ID for each class is given and stored in the attribute table. A map depicting all the functioning road networks around the Guwahati city drawn using QGIS software is shown in Fig. 5 Deciding the optimum path from point A to point B across a road network is critical for emergency services, corporate trips, or even planning routes for tourists exploring a region. It is necessary to build an adequate network in order to carry out

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Fig. 5 Road network map of Guwahati city drawn in QGIS software

such operations. Routing is done using the ORS plugin in QGIS software to find the shortest route as well as the fastest route between Khanapara and Jalukbari. The travelling mode chosen for this analysis is driving by car. For finding the shortest path, distance is taken as impedance, and for finding the fastest path between the two locations, time is taken as impedance. ORS tools offer accessibility to the majority of the functionalities of openrouteservice.org, which are based on Open Street Map. Routing, isochrones, and matrix computations are available in the ORS tools set and can be accessed either directly in the map canvas or from point files within the processing toolbox. The output files are generated with the configuration of various attributes including distance, time duration, and starting and ending location points. The ORS tools calculate the shortest as well as fastest route between the two locations and plots the path over the road network dataset. Figure 6 depicts the shortest route (shown by red line) and fastest route (shown by blue line) from Khanapara to Jalukbari determined by using ORS tools in QGIS. To obtain all road data, we add the highway layer. Since roads are essentially lines, we remove the point and polygon data and are left with only the road network. The points of origin and destination are chosen to address the network to decide the shortest path as well as the fastest path and hence serve the purpose of the analysis. We used graphs (represented by the road network) in this case study to obtain analyses that are closer to reality. We also considered time as impedance here since the shortest route in terms of distance does not always imply the shortest route in terms of time. An attribute table can be used to display all of the details gathered during the study. The attribute table will provide useful geographic details such as how to get to the points, the type of road they are on, and the length of the line they are on. Here in this routing analysis, the attribute table for shortest route and fastest route contains the distance in kms and the duration in which that distance is covered given in hours. Figures 7 and 8 show the attribute table for the shortest route and fastest route from Khanapara to Jalukbari obtained in QGIS software, respectively.

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Fig. 6 Shortest route (shown by red line) and fastest route (shown by blue line) from Khanapara to Jalukbari determined by using ORS tools in QGIS

Fig. 7 Attribute table for shortest route from Khanapara to Jalukbari obtained in QGIS software

The nearest facility analysis decides which accidents and facilities are closest to one another by calculating the cost of travel between them. When looking for nearby facilities, we can decide how many we want to find and whether we want to go towards or away from them. The analysis of the nearest facility reveals the optimum path between accidents and facilities, as well as the cost of travel and directions of routes. We can define limitations while looking for the nearest facilities, such as a

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Fig. 8 Attribute table for fastest route from Khanapara to Jalukbari obtained in QGIS software

cost cut-off beyond which Network Analyst will stop looking for facilities further. We can, for example, create a nearest facility problem to look for hospitals within a 15 min drive from the accident scene. The analysis will exclude ant hospitals that will take more than 15 min to reach from the site of the accident. Network Analyst helps us to analyse several nearby facilities simultaneously. This ensures that we can have several incidents and locate the nearest facility (or facilities) for each of the incident. The data regarding all the major places as well as hospitals in the Guwahati city are downloaded that are required in order to conduct the analysis, using QuickOSM plugin, and are added as layers to the given basemap. The QNEAT3 (QGIS Network Analysis Toolbox 3) Plugin is designed to stimulate advanced network analysis algorithms for the QGIS Processing Toolbox. QNEAT3 is a component of the QGIS3 Processing Framework. It provides algorithms ranging from simple shortest path calculations to more complicated tasks such as Shortest Path, Iso-Area (also known as service areas, accessibility polygons), and OD Matrix (Origin–Destination Matrix) calculations. (a)

(b)

Shortest Path Algorithms: Network and graph research is mostly about pathfinding algorithms. This algorithm calculates the geometry and the costs along the shortest distance between the two location points with the QGIS3 adaptation of the Dijkstra algorithm. Iso-Area Algorithms: Iso-Areas are the equivalent of buffers depending on the network. They have answers to queries such as, “How far can I travel on a network while travelling 2500 m from a given start point on a street network?”. There are several methodologies for computing Iso-Areas.

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OD Matrix Algorithms: The OD matrix algorithms calculates the travel cost on the network (either routed distance or routed time) between all the combination of origin and destination points and represents them in the output layer. These algorithms estimate the network travel cost only without having to calculate the shortest path geometry.

Here, the Distance Matrix algorithm from QNEAT3 plugin is used to find the nearest facility to each of point locations in the city. Here, the hospitals are taken as facilities, and the places are taken as the incident. This algorithm aids in the finding of distances as well as the network between two layers of origin and destination. This algorithm computes the network travel cost (either routed distance or routed time) between all combinations of origin and destination points and represents the final output in a layer. The network route-based cost of origin–destination relationship between the twopoint layers is calculated by using the OD Matrix from Layers as Lines (m: n) algorithm. The cost components are divided into Entry cost, Network cost, Exit cost, and Total cost (sum of all other cost components). The relationship is then represented as a straight line between the origin and destination points with the costs given as attributes in the attribute table. The output OD matrix obtained by connecting all place points to all hospital points is shown in Fig. 9. Since the aim is to find the nearest healthcare facility to all the major destinations in the city, we select place and hospital as the two-point layers and highway as the network layer for the OD matrix analysis. The place and hospital layers are added to the map using the QuickOSM plugin in QGIS. A default speed of 30 kmph is set for the analysis.

Fig. 9 Output OD matrix connecting all place points to all hospital points

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Fig. 10 Attribute table for the output OD matrix obtained using QNEAT3 plugin

Taking centroid of location points as origins points and hospital points as destination points, we have 103 origin points (location points) and 44 destination points (hospital points), resulting in an output that contains 103 × 44 = 4532 pairs of origins and destinations. The output of the OD matrix connects every location point to each of the hospital points with straight lines. The total_cost column keeps a record of the distance in metres between each origin point and destination point as shown in Fig. 10. Only the shortest distance between the two points is of concern to us. We may use SQL to find the destination with the lowest total cost out of all the possibilities. The Layers panel will gain a new virtual layer called SQL Output as shown in Fig. 11. This layer has the result of our analysis. Nearest hospitals for each of the 103 origin points are traced out over the existing road network map. Figure 12 shows the attribute table for SQL output showing origin points (place) connected to the nearest hospitals.

6 Results and Discussions The objective of the research is to assess the possible applications of different network analysis techniques such as routing, nearest facility, and service area analysis. Network Analysis tools or plugins used in this project are ORS tools or plugin for finding the optimal path between two busiest hubs of Guwahati city, i.e. from Khanapara to Jalukbari. We found that the fastest route (considering time as impedance) between Khanapara and Jalukbari is via Guwahati Bypass Road, NH 27 stretching 20.683 kms covering in 0.302 h or 18.12 min. But when distance is considered as

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Fig. 11 Nearest hospitals for each of 103 origin points (place) obtained by nearest facility network analysis using QNEAT3 plugin in QGIS software

Fig. 12 Attribute table for SQL output showing origin points (place) connected to the nearest hospitals

impedance, the shortest route found is the route via GS Road/Mahapurush Srimanta Sankardeva Path and Assam Trunk Road stretching 18.032 kms covering in 0.497 h or 29.82 min. The route analysis depends upon the cost attribute (time or length) of the road network. The analysis is based on the approximate travel time and distance when the impedances selected are time and length. The study indicates that the

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shortest distance route is proposed if length is selected as the impedance; but if time is used as the impedance, the fastest route in terms of estimated speed based on the road width is generated. Information on the shortest and the fastest routes would save drivers and road users time as well as travel cost and avert traffic congestion in commuter routes by redirecting the surplus traffic to shorter routes that are less frequented or busy. Another analysis that is conducted in this project is the nearest facility analysis using the QNEAT3 plugin. This analysis not only searches the nearest facility available in a network of digitized interconnecting lines but also enables adaption routing in critical situations. We used two datasets mainly, places layer and hospital layer, and found out which points from one layer are closest to which point from the other layer. An origin–destination (OD) cost matrix analysis is conducted using the road network dataset to generate a matrix indicating distances and to allocate the nearest referral healthcare facility to the specified locations based on shortest distance. By using distance matrix algorithm, an OD matrix is created connecting all important origin points (places) to all the hospitals available in the city and finally using the Execute SQL tool from the processing toolbox to pick the destinations (hospitals) with the shortest distance from the origin points (places). The matrix output displays the shortest route along the network between each centroid of major places (origins) and each referral hospital as a demand point (destinations) and also tabulate the total impedance that could be distance, time, or cost attribute. The distance recorded in the origin–destination cost matrix is measured across the street network. A closest facility is assigned to each demand point. Figure 5.12 shows the attribute table for the SQL query containing the shortest distances between the origin and destination points.

7 Conclusion Traffic patterns throughout the world have changed considerably since the onset of the COVID-19 pandemic. When the lockdowns began in the early 2020, we witnessed a 50% reduction in global traffic. Since then, certain parts of the world have progressively reopened, while others have imposed limitations. To adapt for this abrupt shift, we will have to adjust our models to make them flexible by dynamically prioritizing historical traffic patterns from previous two to four weeks only and deprioritizing patterns from earlier period. The map was created using Geographic Information Systems (GIS)-based application. It not only has a strong connection to the development of a transportation model, but it also provides capable platforms for spatial data management, analysis, and visualization, specifically for the amalgamation of several datasets. An approach towards determining the most effective travel journey or roads is the Network Analysis. This helps determine the quickest or least expensive route to a given location or collection of locations in a network. We found that the QGIS Network Analyst assists in the resolution of typical network issues including the selection of the most

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favourable path across a region, locating the nearest ambulance or hospital, determining a service area surrounding a venue, and delivering a number of shipments with the help of a range of vehicles. The aim was to establish the shortest distance between two location points by adopting the optimal path, measured in length and time. It is the most efficient method of deciding on a travel route from one place to another or visiting multiple destinations and the order of location specified by the user. It can be the quickest, shortest, or the most picturesque path based on the impediment (cost attribute) that we want to consider for the analysis. The number of secondary roads (as long as they are the shortest) in concerned wards should be increased in response to transportation demand to minimize current traffic congestion in the single-lane roadways and enhance rural–urban connections in order to prevent excessive traffic entering directly into the main city. The OD cost matrix solver helps us categorize and classify facilities in terms of emergencies and where they are located by reducing the computation time from each starting place to all destinations. This will assist the user in determining the shortest route from their location to the healthcare facility. Moreover, the user can dynamically locate all the nearest facilities present within their location, which is beneficial in terms of saving travel time and selecting a suitable hospital immediately. Predicting traffic and determining routes is incredibly complex, which is why developers are always building tools and technologies to keep consumers out of gridlock and on the safest and most efficient route possible. While determining the most favourable route between two locations, it is important to single out all of the possible road segments in between the two points. The aim will be to generate a list to those road segments based on parameters such as the shortest distance, the length of adjacent existing roads, and the current traffic conditions which will then give us the highest-scoring path as well as some alternatives based on various analyses. For future studies, the routing network analysis can be done considering factors other than distance and time such as traffic patterns during various time of the day, less cost consumption, direction, accessibility, etc. We can also evaluate other amenities such as banks, ATMs, schools, etc., in and around the study area.

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6. Khaing O, Wai H, Myat E (2018) Using Dijkstra’s algorithm for public transportation system in Yangon based on GIS. Int J Sci Eng Appl 7(11):442–447 7. Kharel S, Shivananda P, Ramesh KS, Naga Jothi K, Ganesha Raj K (2018) Transportation network model for route and closest facility analysis in Central Bengaluru. Int J Appl Innov Eng Manage (IJAIEM) 7(4):58–62 8. Kerekes AH (2018) Road network analysis using GIS techniques in the interest of finding the optimal routes for emergency situations. Case study: Cluj-Napoca (Romania). Geographia Napocensis Anul XII nr. 1:57–70 9. Farooq A, Xie M, Stoilova S, Ahmad F, Guo M, Williams EJ, … Issa AM (2018) Transportation planning through GIS and multicriteria analysis: case study of Beijing and XiongAn. J Adv Transp 2018(Article ID 2696037):1–16 (2018) 10. Kumar S, Kumar A, Hooda RS (2017) Analysis of road network using remote sensing and GIS data Nainital District (Uttarakhand). Int Res J Eng Technol (IRJET) 4(7):1608–1613 11. Ahmed S, Ibrahim RF, Hefny HA (2017) GIS-based network analysis for the roads network of the Greater Cairo Area. In: Proceedings of 2nd international conference on applied research in computer science and engineering ICAR’17, Lebanon, 22 June 2017. Published at http://ceurws.org 12. Gitau IK, Mundia CN (2017) GIS modeling for an optimal road route location: case study of Moiben-Kapcherop-Kitale Road. Am J Geogr Inf Syst 6(1):26–39 13. Jalegar JM, Begum CS (2017) Rural Road Network Planning by using GIS Methodology. Int J Eng Res Technol (IJERT) 6(4):468–471 14. Balasubramani K, Gomathi M, Prasad S (2016) GIS-based service area analysis for optimal planning strategies: a case study of fire service stations in Madurai city. Geographic Analysis of Union Geographic Information Technologists, Department of Geography, Bangalore University, Bangalore, India. 2319-5371. 5(2):11–18 15. Elsheikh RFA, Elhag A, Sideeg SEK, Mohammed AE, Gism NA, Abd Allah MS (2017) Route network analysis in Khartoum City. SUST J Eng Comput Sci (JECS) 17(1):50–57 (2017) 16. Patil PR, Dhore MP, Thorat SB (2015) Transportation network analysis of Nanded Taluka by using geographic information system. Int J Adv Res Comput Sci Softw Eng 5(4):155–158 17. Dabhade A, Kale KV, Gedam Y (2015) Network analysis for finding shortest path in hospital information system. Int J Adv Res Comput Sci Softw Eng (IJARCSSE) 5(7):618–623 18. Kumar P, Kumar D (2014) Network analysis using GIS techniques: a case of Chandigarh city. Int J Sci Res (IJSR) 5(2):409–411 19. Arora A, Pandey MK (2011) Transportation network model and network analysis of road networks. In: 12th ESRI India user conference, Gurgaon (Haryana, India), 7–8 Dec 2011, pp 1–9 20. Kakade RR (2013) Road network analysis using geoinformatic technique for Akola City, Maharashtra State, India. Int J Eng Res Technol (IJERT) 2(8):1884–1886 21. Gill N, Bharath BD (2013) Identification of optimum path for tourist places using GIS based network analysis: a case study of New Delhi. Int J Adv Remote Sens GIS Geogr (IJARSGG) 1(8):34–38 22. Koshak N, Nour A (2013) Integrating RFID and GIS to support urban transportation management and planning of Hajj. In: The 13th international conference on computers in urban planning and urban management. Utrecht, The Netherlands 23. Ilayaraja K (2013) Road network analysis in Neyveli Township, Cuddalore district by using Quantum GIS. Indian J Comput Sci Eng (IJCSE) 4(1):56–61 24. Ejiagha IR, Ojiako JC, Eze CG (2012) Accessibility analysis of healthcare delivery system within Enugu urban area using geographic information system. J Geogr Inf Syst 4(4):312–321 25. Guwahati-Google Maps. https://www.google.com/maps/place/Guwahati,+Assam/@26.142 9809,91.5627973,11z/data=!3m1!4b1!4m5!3m4!1s0x375a5a287f9133ff:0x2bbd1332436bd e32!8m2!3d26.1445169!4d91.7362365. Accessed on 10 Oct 2020 26. Unsafe Guwahati roads-Sentinelassam. https://www.sentinelassam.com/editorial/unsafe-guw ahati-roads/. Accessed on 10 Oct 2020

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Probabilistic Approach for the Evaluation of Two-Lane Two-Way Rural Highways P. Muhammed Swalih, M. Sangeetha, M. Harikrishna, and M. V. L. R. Anjaneyulu

Abstract Two-lane two-way highways constitute a significant portion of road networks in developing countries. Highway Capacity Manuals developed by various agencies suggest the use of performance measures based on platooning of vehicles for evaluating quality of service provided by two-lane highways. Current practices use a single critical headway for identifying the following vehicles but this has its drawbacks. This paper checks the suitability of headway and speed-based probabilistic approach for Indian condition where traffic is heterogeneous and exhibits weak lane discipline. The new method under consideration is compared with current practices by carrying out correlation analysis with 5 min directional traffic flow rate. Analysis showed significantly higher correlation coefficients for the new method compared to that of currently used methods. The method can identify a greater number of vehicles as followers and is also able to account for “happy to follow” vehicles and “safe drivers.” Keywords Two-lane highways · Following vehicle · Level of service · Performance measures · Platooning

1 Introduction Two-lane two-way highways play a vital role in the development of countries. These are the major facilities that connect rural areas to suburban and urban areas. The primary function of this facility will vary from mobility to accessibility depending on its environment. Two-lane two-way highways differ from other uninterrupted P. M. Swalih (B) · M. Sangeetha · M. Harikrishna · M. V. L. R. Anjaneyulu National Institute of Technology Calicut, NITC, Calicut 673601, India e-mail: [email protected] M. Harikrishna e-mail: [email protected] M. V. L. R. Anjaneyulu e-mail: [email protected] © Transportation Research Group of India 2023 L. Devi et al. (eds.), Proceedings of the Sixth International Conference of Transportation Research Group of India, Lecture Notes in Civil Engineering 271, https://doi.org/10.1007/978-981-19-3505-3_15

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flow facilities as the vehicles in these facilities are in constant interaction with the opposing flow. The facility is characterized by the overtaking maneuver through the opposite lane. Level of service analysis of two-lane two-way highways is not done based on density due to this reason. Commonly adopted performance measures are based on the proportion of platooning or following vehicles present in the stream. It is very important to identify whether a vehicle is following or freely moving for the performance evaluation of two-lane two-way highways. Current practices use a single headway criterion as suggested by Highway Capacity Manual (HCM) [1] or site-specific critical headway as suggested by the Indian Highway Capacity Manual [2]. The use of such a simple criterion makes it very convenient for calculation but possesses certain deficiencies. However, assigning a single value to classify following vehicles assumes every vehicle acts similarly irrespective of the conditions and does not account for the random behavior of drivers. Using a single headway fails to address two types of vehicles that are present in traffic flow. Some vehicles may seem to be following because of their low headway maintained but in reality, they may be traveling at the desired speed which can be called “happy to follow” vehicles. Some drivers who chose to drive more safely may maintain higher headway even though they are traveling at a speed less than desired speed which can be termed as “safe driver.” In this way, a single headway is inadequate to identify following and free vehicles.

2 Literature Review There were several studies on the Indian condition related to the performance evaluation of two-lane two-way highways. Penmetsa et al. [3] carried out statistical and graphical analysis on six prominent performance measures and were evaluated for suitability in the Indian condition. But none of the commonly used measures were found to be giving good statistical results. A new measure called number of followers per capacity was evaluated and was found out to be a good measure. This is the measure currently suggested by Indo-HCM [2]. Similarly, Boora and Ghosh [4] also evaluated some performance measures along with platooning variables for the examination of two-way highways. A method based on relative speed was used to identify the following vehicles. It was found that speed-related variables were performing very poorly. NFPC and FD were found to be the better performing measures. Miller [5] observed that after a specific value, headways can be considered as exponentially distributed. After this headway, vehicles are not affected by other vehicles in the stream, which means they are free flowing. On close analysis of relative speeds of vehicles, it was found that the speed of second vehicles was slightly higher than the first after this headway, or in other words, vehicles begin to slow down as headway reduces below a certain value. Buckley [6], based on the observations by Miller [5], proposed a mixed headway distribution model named semi-Poisson distribution. In this model, vehicles are considered as either constrained (i.e., forced

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to move slow) or unconstrained. Wasielewski [7] modified this model into a nonparametric model. Hoogendoorn [8] stated that for the identification of the following vehicles one must consider both speed and headway. Assuming free-flowing speed as desired speed, a method for calculating the probability of a vehicle in the constrained condition is proposed. Hoogendoorn [8] modified the Kaplan–Meier [9] equation by incorporating the probability of the vehicle being constrained from headway condition. Both of these equations predict the probability of the vehicle being in a constrained condition. Catbagan and Nakamura [10] proposed a speed- and headway-based probabilistic method based on these equations to identify the following vehicles. In order to consider both speed and headway, the probability of a vehicle being constrained is taken as the product of probabilities of vehicle constrained by headway as well as speed. Vehicles having a probability higher than a threshold value (say 0.5 or 0.6) are taken as the following vehicle. HCM [1] uses a service measure known as percent time spent following (PTSF), which is the average percent of travel time that vehicles have to spend in the platoon following slower vehicles due to the inability to pass. PTSF was assumed to better represent the traffic conditions than density, as density is very less evenly distributed in two-lane highways compared to multi-lane highways. PTSF is a measure for the freedom to maneuver and comfort and convenience associated with the movement on the road. As it is difficult to measure PTSF in the field, HCM [1] suggested a surrogate measure namely “percent followers” (PF) which is the percent of following vehicles identified by the 3 s critical headway criteria. The Indo-HCM [2] adopted a new technique for recognizing the following vehicle based on the study of Miller [3]. It used the speed difference between two vehicles to find whether the vehicles are in the following condition or not. To identify the followers on a two-lane highway, a histogram of speed difference (SD) between two consecutive vehicles is plotted. Thereafter, a normal distribution curve of SD for free-flowing vehicles (vehicles that are traveling at their desired speed with a gap value equal to or greater than 8 s) is plotted on the secondary axis. Later, this normal distribution curve is superimposed on the SD histogram of two consecutive vehicles. The intersecting points between the normal curve and the histogram designate the maximum observed SD range for the highway. This SD limit is used to describe the following and non-following vehicles. Vehicles traveling within this SD limit are identified as followers, and those traveling outside this SD limit are recognized as non-followers or free vehicles. For ease of analysis, a single critical headway is further derived based on the above procedure by the acceptance curve method.

3 Need and Objective of the Study A review of the related literature revealed that single headway criteria will not be able to account for the randomness of drivers. Most of the research fails to appreciate the

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importance of speed for recognizing vehicles as following. Most of the studies evaluated the performance measures based on follower vehicle identification by single threshold headway criteria. None of the studies used a probabilistic approach to identify the following vehicles in Indian traffic condition, to the best of the author’s knowledge. The objectives of the study are to assess the suitability of a speed- and headway-based probabilistic method for Indian traffic conditions and compare it with current practices. Performance measures used for the analysis of two-lane two-way highways were also evaluated, and the one suitable for the specific traffic condition is also identified.

4 Data Collection and Extraction Eight sites across Kerala were selected for data collection. The sites include National Highways (NH), State Highways (SH), and Major District Roads (MDR). All the sites considered are straight and level two-lane two-way highways and at a minimum distance of 500 m from the nearest junction. Details of study sites are given in Table 1. Data was collected using an automatic traffic data logger (TIRTL). To account for the weak lane discipline exhibited by Indian traffic, the carriageway was considered as four lanes instead of two lanes for measurement by the instrument. Headway and relative speeds were then extracted by considering these lanes using timestamp and speed registered by the vehicle. Table 1 Details of study sites selected Location Areekkode

Carriageway width Road type Shoulder type Length of section (m) (m) 6.4

SH

Paved

410

Chevarambalam

10.0

NH

Paved

1000

Karanthoor

11.0

NH

Paved

180

Korapuzha

7.2

NH

Earthen

393

Manissery

7.2

SH

Paved

312

Mundikkal Thazham

6.0

MDR

No shoulder

400

10.0

NH

Paved

1000

9.8

NH

Earthen

200

Ramanattukara Vavad

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5 Speed- and Headway-Based Probabilistic Method Catbagan and Nakamura [10] proposed a method for the identification of following vehicles considering headway and speed. For ease of usage, this method is referred to as the C&N method in the following sections of this paper.

5.1 Headway-Based Following Probability Headway-based following probability is calculated using the semi-Poisson model as suggested by Buckley [6] which was later modified by Wasielewsky [7]. In theory, the probability that a vehicle is in the following state increases as its headway reduces with the leader. In other words, headway-based following probability of a vehicle decreases as headway increases. So, it can also be stated that there will be a headway after which the probability of following based on headway alone will be insignificant. Miller [5] found that after a specific headway, headway distribution will follow an exponential distribution. On closer analysis, it was found that this exponentially distributed headway is related to free flow vehicles. Based on this, Buckley [6] proposed a composite headway distribution model which is widely known as the semi-Poisson model, in which it divides vehicles as constrained and unconstrained. The general form of this model is given by the probability density function f (t) = φg(t) + (1 − φ)h(t).

(1)

where g(t) and h(t), respectively, denote the headway probability density functions of the constrained vehicles and the free vehicles; φ denotes the fraction of followers. Let g1 (t) = φ*g(t) and h 1 (t) = (1 − φ)*h(t). As stated by Miller [5], for sufficiently larger headways (say, t > T ), headway distribution can be taken as exponentially distributed. Then, the probability density function can be written as f (t) = h 1 (t) = Aλe(−λt) , for t > T.

(2)

where T > 0 is the headway value beyond which there is no significant probability of vehicles being constrained, i.e., free flow headway; λ is the arrival rate for free vehicles; and A is the normalization constant. For headway values t < T, the exponential form is no longer valid as the probability of vehicles being following will be significant. This is done by removing a fraction of constrained vehicles having preferred headway greater than “t” from the exponential distribution. This is done based on the basic assumption that no vehicle will travel below the preferred headway. This fraction p(t) is given by

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∞ p(t) =

g(s)ds.

(3)

t

Hence, h 1 (t) = Aλe

−λt

[1 − p(t)] = Aλe

−λt

∞ (1 −

g(s)ds.

(4)

t

But, g(t) = [ f (t) − h 1 (t)]/φ.

(5)

Using Eq. (5), one can easily obtain for h 1 (t) from the following integral equation Aλ −λt e h 1 (t) = φ

t [ f (s) − h 1 (s)ds].

(6)

0

The parameters A and λ can be evaluated from the observed headways in the range t > T. Then, the integral equation can be solved subject to the constraint Eq. 7 by iteration. ∞ φ=

g1 (s)ds.

(7)

0

Let θ (t) denote the conditional probability that a vehicle driving at headway “t” is constrained. Then, probability can be calculated as θ (t) =

g1 (t) φg(t) = f (t) f (t)

(8)

Speed-Based Following Probability. The other component for follower identification by this method is desired speed. Similar to that of headways, it is difficult to ascertain following status of the vehicle as desired speed will vary across users. In fact, the desired speed of the same driver will change with respect to change in the environment. For ease of analysis, it is assumed that desired speed across drivers is constant. This assumption also means that the desired speed is the maximum speed. For the estimation of desired speed, unified free speed distribution developed by Hoogendoorn [8] was used. In this study, free speed distributions are assumed to be equivalent to desired speed distributions. Based on the given speed vi alone, the probability of not following can be expressed as

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vi P(Free/vi ) =

f d (v)dv.

(9)

0

Therefore, the probability of following can be simply expressed as vi P(Foll/vi ) = 1 −

∞ f d (v)dv =

0

f d (v)dv.

(10)

vi

Then, the survival function of desired speed distribution which is a generalization of Kaplan and Meier [7] is given by  n v0    0   n− j −1 = P Follspeed . S∞ v = n − j − θj j=1

(11)

where v 0 is the free speed, n v0 denotes the number of samples vi that are smaller than or equal to v 0 (i.e., v j