Current Trends in Geotechnical Engineering and Construction: Proceedings of 3ICGE-Iraq 2022 9811973571, 9789811973574

Citation preview

Mahdi Karkush Deepankar Choudhury Jie Han   Editors

Current Trends in Geotechnical Engineering and Construction Proceedings of 3ICGE-Iraq 2022

Current Trends in Geotechnical Engineering and Construction

Mahdi Karkush Deepankar Choudhury Jie Han •

•

Editors

Current Trends in Geotechnical Engineering and Construction Proceedings of 3ICGE-Iraq 2022

123

Editors Mahdi Karkush Civil Engineering University of Baghdad Baghdad, Iraq

Deepankar Choudhury Civil Engineering Indian Institute of Technology Bombay Mumbai, Maharashtra, India

Jie Han Civil, Environmental and Architectural Engineering University of Kansas Kansas, KS, USA

ISBN 978-981-19-7357-4 ISBN 978-981-19-7358-1 https://doi.org/10.1007/978-981-19-7358-1

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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

This book contains selected articles from the third International Conference on Geotechnical Engineering-Iraq (3ICGE-Iraq) held at University of Baghdad/Baghdad/Iraq on May 29–31, 2022, to discuss the challenges, opportunities, and problems of application of geotechnical engineering in projects. Also, the conference includes recent applications in structural engineering, materials of construction, construction management, engineering of water resources, and environmental engineering. The 3ICGE-Iraq was organized by the Iraqi Scientific Society of Soil Mechanics and Foundation Engineering (ISSSMFE) with the cooperation of the College of Engineering/University of Baghdad, College of Engineering/University of Kerbala, and Karbala Center for Studies and Research/Imam Hussain Holy Shrine. The articles cover a wide spectrum of themes in civil engineering, including but not limited to sustainability and environmentally friend applications. The contributing authors are researchers in their respective fields from several universities. This book will prove a valuable resource for practicing engineers and researchers in the field of geotechnical engineering, structural engineering, and construction and management of projects. Mahdi Karkush Deepankar Choudhury Jie Han

v

Contents

Calculating the Real Need for Fire Brigade Stations in Al-Samawah City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waleed A. Rzaij, Basim H. K. Al-Obaidi, and Mohammed R. Abbas

1

Drinking Water Assessment Using Statistical Analyses of AL-Muthana Water Treatment Plant . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Abed Naser and Khalid Adel Abdulrazzaq

13

Simulation Design and Performance of a Residential Complex Using Liquefied Petroleum Gas Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amna A. Farouq and Basim H. K. Al-Obaidi

21

Disinfection Performance of Polyvinyl Chloride (PVC) Membrane Incorporating with AgNPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmaa N. Al-Himeiri and Alaa H. Al-Fatlawi

31

Optimal Bedding Selection with the Specific Soil Type According to the Thrust Forces Generated in the Water Distribution Networks Using the Restraining Joint System . . . . . . . . . . . . . . . . . . . . . . . . . . . . Murtadha H. Dawood, Amer F. Izzet, and Basim H. Khudair Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System Using WaterGEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdulrahman A. Abdulsamad and Khalid Adel Abdulrazzaq Optimization and Modelling of Electrochemical Removal of Nitrate from Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muhammed A. Shallal, Sarah A. Ali, Haneen H. Hamzaa, Salam M. Naser, Maliheh Arab, and Raad Hashim Determination of Potential Sites for Landfill Using Geographic Information Systems Technology in Shatra City . . . . . . . . . . . . . . . . . . Mukhalad N. Mohammed and Faisel G. Mohammed

38

52

62

73

vii

viii

Contents

Selection of Optimal Location for Wind Turbines in Diyala Governorate Using the Analytic Hierarchy Process (AHP) with GIS Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qater AL- Nada Rasim Rejap and Yousif H. Khalaf Automatic Co-registration of UAV-Based Photogrammetry and Terrestrial Laser Scanning in Urban Areas . . . . . . . . . . . . . . . . . . . . . . Mohammed G. Ahmed and Fanar M. Abed

86

99

Identify the Critical Risk Factors at the Tendering Phase in Iraq . . . . . 113 Marwa Makki Dishar and Meervat Razzaq Altaie Utilizing Delphi Technique and Bootstrap to Determine the Maximum Cost Reduction in Serial Tendering for School Construction Works . . . 124 Arshed A. Mohammed and Kadhim R. Erzaij Geotechnical Risk Management. A Case Study of Nablus City, Palestine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Fares Sami Hijjawi Analyzing the Quantity and Cost of the Waste Generated in Construction Projects in Iraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Kadhum L. Atabi, L. Reig-Cerdá, and F. J. Colomer-Mendoza Proposing Risk Responses for Iraqi Petroleum Sector Using Analytic Network Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Noor Abdulsattar Abduljabbar and Hatem Khaleefah Breesam Investigation of the Deformation of Sandy Soil Near a Laterally Loaded Single Pile Using the Particle Image Velocimetry Technique . . . 178 Balqees A. Ahmed and Dhergham A. R. Al-Hamdani Experimental and Numerical Evaluation for Bearing Capacity of a Square Footing on Geotextile Reinforced Sandy Soil . . . . . . . . . . . . . . . 195 Hussein Shaia and Lubna Thamer Effect of Embedment Depth on the Load-Settlement Behavior of Precast Pile in Al-Gharraf Oil Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Taha Y. Abdulnabi, Mahdi O. Karkush, Yaser Safa, and Ali H. Mahdi Observations on the Behavior of Continuous Flight Auger Piles in Iraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 Mohammed Y. Fattah, Mahdi O. Karkush, Mohammed A. Al-Neami, Taha Y. Al-Kaabi, Mudhafar K. Hameedi, Maher M. Jebur, Shaimaa H. Fadhil, and Mohammed H. Al-Dahlaki Numerical Assessment of Ring Foundation Settlement Under Seismic Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Evan E. Al-Khalidi, Nabeel K. Lwti, Mahdi O. Karkush, and Wisam A. Aljuboori

Contents

ix

Experimental Assessment of Bearing Capacity on Pile Foundations of the New Monument in Nur-Sultan City . . . . . . . . . . . . . . . . . . . . . . . 243 Askar Zhussupbekov, Dmitry Chunyuk, Victor Kaliakin, and Abdulla Omarov Finite Element Analysis of the Load-Settlement Behavior of LargeScale Shallow Foundations on Fine-Grained Soil Utilizing Plaxis 3D . . . 249 Asmaa G. Salih, Ahmad S. A. Rashid, and Nihad B. Salih Energy Piles, Applications and Research Aspects: An Investigation on the Behavior of a Single Energy Pile in Dry Condition . . . . . . . . . . . . . 261 Fardin Jafarzadeh and Sina Afzalsoltani Wetting and Drying Cycles Influences on Geotechnical Properties of Lime-Stabilized Clayey Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 Tavga Aram Abdalla and Nihad Bahaaldeen Salih Inversion Analysis of Slope Engineering Parameters Using Back Propagation Neural Network Based on Strength Reduction Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Arif Raouf and Kunyong Zhang Agricultural Nano Fertilizers: Macronutrient Types and Applications Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Mohammad R. Alrbaihat Investigation of Shear Strength Parameters for Gypseous Soils Using a Modified Apparatus of Triaxial Test . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Mustafa M. Abdalhusein, Ali Akhtarpour, Rusul Almahmodi, and Mohammed Sh. Mahmood Analytical Solution to the Consolidation Problem of PVD Improved Soil with Nonlinear Variation of Vacuum Pressure . . . . . . . . . . . . . . . . 331 Fatema S. Noori and Ala N. Aljorany Structural Problems of a Multi-storey Building During Construction Due to the Absence of a Specialized Geotechnical Role—A Field Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 Taha Y. Al-Kaabi, Mohammed Y. Fattah, and Ahmed S. A. Al-Gharbawi Assessment of Bearing Capacity and Settlement Characteristics of Compacted Clay Soil Reinforced by Sand Dune and Sodium Silicate Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Evan E. Al-Khalidi, Mahmood D. Ahmed, Ammar A. Sheikha, and Ali A. J. Alshamoosi Finite Element Based Pseudo-Static Stability Analysis of Soil Slope Under Combined Effects of Horizontal and Vertical Seismic Accelerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Tanmoy Das and Deepankar Choudhury

x

Contents

Elastic Modulus Determination Based on Pressuremeter Tests and Standard Penetration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Ali Tabatabaei, M. R. Kamali, and Saba Abedi Anaraki Slope Stability and Seepage Analysis of Dikes Strengthened with Sheet Pile Wall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Aneed Husam Sameer, István Kádár, and Zsombor Illés The Behavior of Reinforced Reactive Powder Concrete Two-Way Slabs Under Drop-Weight Impact Loads . . . . . . . . . . . . . . . . . . . . . . . . 397 Sajjad H. Majeed, Eyad K. Sayhood, and Nisreen S. Mohammed Damage Assessment of Tympanic Membrane in Sheep Subjected to Blast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Assal Hussein Estimation of Deep S-Wave Velocity Profile Using Seismic Records Case of Lima, Peru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Jorge Soto and Jorge E. Alva A Life Cycle Assessment Comparison of External Cavity Walls Using Different Types of Concrete Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 D. Todd, Joseph Amoako-Attah, and Khalid Hashim Integration of 3D Concrete Printing in the Construction Industry: A Short Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Ravekumar Chandrasekar, Michaela Gkantou, Georgios Nikitas, Khalid Hashim, Hampannaver Rajanna Pradeep, and Arun Ahuja Impact of Vehicle Speed and Loading Time on Permanent Deformations of Asphalt Pavement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 Zainab Ahmed Alkaissi and Qais Sahib Banyhussan Study of Pedestrian Crossing Behavior at A Number of Unmarked Crossings in Kuala Lumpur, Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Zainab Hacham and Hussain Hamid Non-destructive Tests of Reactive Powder Concrete Using Sustainable Materials as a Partial Replacement of Fine Aggregate . . . . . . . . . . . . . . 476 Mena A. Gawad and Nada M. Fawzi Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

Calculating the Real Need for Fire Brigade Stations in Al-Samawah City Waleed A. Rzaij1(B) , Basim H. K. Al-Obaidi1 , and Mohammed R. Abbas2 1 Civil Engineering Department, University of Baghdad, Baghdad, Iraq

[email protected], [email protected] 2 Directorate of Civil Defense, Samawah, Al-Muthanna Governorate, Iraq

Abstract. The location of fire brigade stations and equipment has a significant impact on the efficacy and efficiency of fire brigade department services. The challenge addressed by this study was that the fire brigade department required a consistent and repeatable technique to assess the response capabilities and safeguarding levels offered as the city of Samawah/Iraq grew and changed. Evaluating the locations of the current fire brigade stations in the city of Samawah is the aspect addressed by the research to determine the accuracy and validity of the locations of these stations by the competent authorities and their suitability to the area of the city’s neighborhoods and its residents. The Iraqi Ministry of Housing, Construction, Municipalities and Public Works has set standards for fire brigade stations in the year 2018. These standards were used in this research because they are the standards adopted in Iraq. The first criterion represents the population size criterion. This criterion specified that each fire brigade station must provide a service for (48000 people), and the second criterion represented the distance traveled, which defined its field of service by (2 km) for each fire brigade station, as for the third criterion represented by the response time, which was set at (10 min) for the local standard and this criterion is considered large compared to the global standard of (4 min). The result using geographic information system (GIS) showed that needs four additional fire brigade stations to be added to the already existing four stations so that the total number of fire brigade stations in the city becomes eight stations, and this number of stations will provide service to all residents of the city and reduce the risk of fires on the city. Keywords: Fire brigade station · Evaluating · Response time · Location · Distribution · Population size

1 Introduction The location of fire brigade stations has grown increasingly essential in order to serve the largest number of residents [1]. The proper placement of fire brigade stations is a key aspect in providing effective and cost-effective fire protection. In the past, fire brigade stations were built on or near major thoroughfares and in regions where existing stations were too far away to respond. The locations of the sites were chosen based on personal © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 1–12, 2023. https://doi.org/10.1007/978-981-19-7358-1_1

2

W. A. Rzaij et al.

preferences. Even though cities may appear to be the same on a map, determining deployment requires a deeper investigation [2]. Therefore, fire brigade stations services are one of the security services that must be available in every city and distributed over all its residential neighborhoods in a manner commensurate with the continuous increase in the size of its population and with the area of that city and the future expansion of each of them, and this, in turn, depends on several things, the most important of which are the speed of arrival of fire brigade station vehicles to the places of accidents, which is one of the important indicators of the efficiency of this service, which comes from the efficient geographical distribution of fire brigade stations within cities, and thus this provides security and safety for residents inside and outside the city [3]. Where Iraqi cities suffer from a weakness in the spatial distribution of fire brigade stations in general, as there are areas that are not covered by emergency response services, not taking into account planning standards in the spatial distribution of fire brigade stations, and the difficulty of accurately determining the locations of fires in the city and the shortest distance and time to reach. Among the characteristics of fire brigade station efficiency is the speed of reaching the location of the accident in the shortest possible time, the skill of rescuers or fire brigade station men in dealing with accidents, what are the procedures followed to deal with the accident, and what are the modern techniques using civil defense men to reduce time and speed in rescue [4]. To evaluate the locations and performance of fire brigade stations, many studies have been conducted around the world. These studies included tests of these stations with different levels of performance, in terms of the speed of response to extinguish the fire, the speed of access to the site of the accident, and other criteria. Mufeed [5] presented a study to evaluate the locations of fire brigade stations in the city of Baghdad, and the results showed that the lack of response speed in some cases is not due to the inaccurate distribution of these stations, but rather there is a lack of consideration for the distribution of road network centers, traffic, closed roads and intersections in the streets to facilitate the arrival of firefighters and shorten the time. Whereas Jerald [6] presented a procedure for evaluating the locations of fire brigade stations in West Covina and concluded that a standard operating procedure should be adopted for the regular evaluation of the station’s location and the effectiveness of deployment. It is also recommended to purchase software that uses advanced GIS features to improve the delivery of firefighting services. Also, Maher [1] presented a study to evaluate the fire brigade station locations in the city of Samawa based on the standards of the Ministry of Construction and Housing for the year 1983 and found that these standards are old and do not fit the housing growth in the city. The research aims to evaluate the distribution of fire brigade stations and to indicate the accuracy of the selection of these places by the competent authorities, through the use of local and international standards in addition to geographic information systems (GIS) in order to ensure a fair distribution of this service to all neighborhoods of the city and to ensure the life and property of its residents through a service efficiency offered by these stations. The local standards of the Iraqi Ministry of Housing, Construction, Municipalities and Public Works for the year 2018, which were approved in the Republic of Iraq, as well as the use of geographic information systems (GIS), were used in the

Calculating the Real Need for Fire Brigade Stations

3

analysis and identification of serviced and unserved areas by fire stations in the city of Samawah.

2 Material and Methods 2.1 Case Study Description The study case that was adopted in this research is the city of Samawah, an Iraqi city located in southern Iraq on the banks of the Euphrates River. It is the center of AlMuthanna Governorate, 280 km southeast of Baghdad, and its area is (941 km2 ). Figure 1 shows the location of the city.

Fig. 1. The location of the city of Samawah on the map of Iraq [7].

2.2 Data Collection and Analysis Data was collected about fire brigade stations in terms of their locations and the neighborhoods they serve. This information was taken from the Directorate of Civil Defense in the city of Samawah. Also, information was collected about the number of residents of the neighborhoods served by the fire brigade stations, and this information was taken from the Directorate of Statistics of the city of Samawah. As for the local standards adopted in locating fire brigade stations, they were taken from the Department of Construction and Housing in the city of Samawah. Figure 2 shows the locations of the fire brigade station in the city of Samawah.

4

W. A. Rzaij et al.

Fig. 2. Fire brigade station locations in the city.

2.3 Fire brigade Stations Details The city of Samawah includes four fire brigade stations, distributed over the city to provide service and assistance to the residents in the event of a fire or other emergency. Some of the locations of these stations are located in densely populated streets, such as the Samawah station, while others are located in the outskirts of the city, such as Al Soub Al Sagheer station. Table 1 shows the details of each fire brigade station in terms of serviced neighborhoods, their area, number of residents.

Calculating the Real Need for Fire Brigade Stations

5

Table 1. Details of fire brigade stations in the city of Samawah [8, 9]. Fire brigade station location

Serviced area (km2 )

AL-Soub AL-Saghir

15.10

76781

Thawrat AL-Ashrin

Residents of serviced neighborhoods

13.55

36404

AL-Samawah

5.21

37628

AL-Jumhori

7.67

83164

41.53

233977

Total

3 Fire Brigade Station Concepts and Considerations Fire brigade is the process of putting out a fire with the use of specialist equipment and techniques. Fire science ideas and procedures are used to save people trapped in fires and prevent material and human losses [10]. The location of fire brigade stations has a substantial impact on the efficacy and efficiency of the fire department’s services [11]. Evaluation of fire brigade station locations is done by using many criteria and considerations, including (population size, distance traveled, and response time). The Iraqi Ministry of Construction, Housing, Municipalities, and Public Works has set parameters for these standards, and according to the instructions of the housing standards for the year 2018, which are shown in Table 2. Table 2. Size of approved criteria [12]. Criteria name

Criteria measurement

Population size

(38400–57600) capita

The traveled distance

2000 m

Response time

10 min

4 Results and Discussion 4.1 Serviced Area and Population It is noticed that there is a random and illogical distribution of fire brigade stations, and this is appears in Figs. 3 and 4, where there are stations responsible for protecting the population and neighborhoods of the spaces and numbers of citizens, the service provided by other fire brigdae stations is weak, and this certainly affects the ability of these stations to provide a full service to the areas for which these stations are responsible. This indicates that the city needs additional fire stations to assist other fire stations in providing full services to the city’s residents and protecting their properties.

6

W. A. Rzaij et al.

AL-Jumhori 18%

AL-Samawah 13%

AL-Soub ALSaghir 36%

Thawrat ALAshrin 33%

Fig. 3. Percentage of the area served by each fire brigade station.

AL-Jumhori 35%

AL-Samawah 16%

AL-Soub ALSaghir 33%

Thawrat ALAshrin 16%

Fig. 4. Percentage of population served by each fire brigade station.

4.2 Assessment of Annual Fire The population expansion in the city of Samawah and random construction in informal neighborhoods, led to an increase in the number of fires in the city. In the past five years, an increase in the number of fires has been observed from year to year. In 2017, the number of fires reached 1491, to nearly double this number in 2021, and this indicates the large population increase during this period and the expansion of residential neighborhoods, as well as the weakness of preventive measures against fires, knowing that the main cause of fires is the use of electrical appliances, Therefore, residents must be made aware of the danger of fires. Figure 5 shows the annual growth of the number of fires in Samawah over the years (2017–2021).

Calculating the Real Need for Fire Brigade Stations

7

3000

Number of fire

2500 2000 1500 1000 500 The number of fires in the province

The number of fires in the city

0

2017

2018

2019

2020

2021

Years

Fig. 5. The increase in the number of fires annually.

4.3 Classification of Fire The Directorate of Civil Defense in Al-Muthanna Governorate classifies fires into three categories (government sector fires, private sector fires, mixed sector fires). Table 3 shows the rate of fires that occurred during the past five years in each sector. Table 3. Fire rate in sectors for the past five years. Classification of fire

Fire rate for the period from (2017–2021)

Government sector

286

Residential sector

1758

Mixed sector

0

The data in the above table showed that there is a big difference between the number of fires in the governmental and residential sectors in Al-Muthanna Governorate, and this indicated that the most crowded areas have the largest number of fires and this is observed in residential areas. As for the mixed sector, the number of fires is equal to zero because the number of mixed buildings between the residential and government sectors in the governorate is almost non-existent. In the city of Samawah (70%) of these fires occur because it is the center of the governorate and the most densely populated and built.

8

W. A. Rzaij et al.

4.4 Evaluation of the Efficiency of the Fire brigade Stations Distribution 4.4.1 Population Size Criterion The Directorate of Statistics in the city of Samawa estimated the population of the city of Samawa to reach 233977 people for the year 2020. Since the urban and rural housing standards in Iraq for the year 2018 indicate that each fire brigade station must provide a service for (38400–57600) capita (average = 48000 capita), this indicates a shortage in the number of fire brigade stations in the city according to this standard. There are four fire brigade station available in the city, meaning that there is a need to open two additional fire brigade stations according to this standard. 4.4.2 Distance Traveled Criterion The Ministry of Housing standards for 2018 indicates that the coverage distance of civil defense centers is 2 km. By applying the criterion of the distance traveled within the study area and based on the area of residential neighborhoods represented by the

Fig. 6. Coverage of existing fire brigade stations for city areas.

Calculating the Real Need for Fire Brigade Stations

9

GIS program, the proportion of areas served and not served by fire brigade stations in the city of Samawah. Figure 6 shows the parts of the city that are not covered by fire brigade stations services, which constitute large areas of the city, and this makes many of the city’s residents and their properties exposed to risks due to the lack of fire stations covering their areas. This means that the city needs to open additional fire brigade stations to reduce the shortfall in coverage of all areas of the city and achieve the standard traveled distance of 2 km, as shown in Fig. 7.

Fig. 7. Additional fire brigade station locations.

4.4.3 Response Time Standard Time is the critical element when reporting any emergency. For example, a fire can expand and double its size significantly in a short time. Time is the most important

10

W. A. Rzaij et al.

factor in saving building occupants and reducing human and material losses [13]. The Iraqi local standard, according to the Ministry of Housing, set the response time at 10 min, which is a very long time if it is compared with the international standard, which is 4 min, so reducing the time required requires finding an ideal distribution for fire brigade stations and this depends on good planning for that service, which in turn is reflected on providing good service as quickly as possible and for the largest number of residents. To achieve this ideal service, the number of fire brigade stations must be increased and distributed in an ideal manner, taking into account the area of the city. The response time of 4 min was adopted for research purposes because it represents a global and realistic standard in practice and is approved in many countries [1]. Table 4 shows the response rate of fire brigade stations in the city of Samawah for the past five years. Table 4. Response time to fire brigade stations in the past five years. Response time (minutes) Year

60 min.

2017

460

137

48

6

2

2

0

2018

540

213

51

12

4

1

0

2019 2020

569

230

59

16

6

1

0

798

296

85

21

11

0

0

2021

1091

421

113

31

17

2

0

Ave.

692

260

72

18

8

2

0

65

24

7

1.7

0.79

0.018

0

%

(4–5) min.

(6–10) min.

It is clear from the above that the fire brigade stations in the study area have applied the international standard in response time, which was estimated at four minutes, 65% of the total during the study period. As for the national standard, which estimated the response time at ten minutes, the rate of arrival of fire brigade teams during this period was It reached 7% of the total number of fires in urban centers in the city during the study period, and there are percentages of the arrival of fire brigade teams outside the local and international classification, so these percentages are not appropriate for rescue operations from fire accidents that are quick to destroy everything within minutes. Suggesting fire brigade stations through which an appropriate and standard response time is achieved to reduce the proportion of human and material losses as much as possible.

5 Conclusions From this study the following was concluded: • There is an illogical distribution of fire brigade stations in terms of the area served, as there are fire brigade stations that serve areas twice the area served by other stations.

Calculating the Real Need for Fire Brigade Stations

11

• There is a significant increase in the number of annual fires and these fires are gradually increasing with the years as a result of the population increase in the city. • Evaluate the locations of fire brigade stations, according to: a. Population size criterion: By applying this criterion, it was found that the study area needs additional fire brigade stations. b. Distance traveled criterion: Through the application of this criterion, it was observed that there is a large deficit in all fire brigade stations in delivering their services to residential neighborhoods in the city, where the percentage of the deficit reached approximately (40%) of the total area of the city. c. Response time criterion: There is a delay in the response time for a number of fires, and this is unacceptable because the fire spreads very quickly and causes a heavy loss of life and property. • According to the above criteria, the study area needs eight fire brigade stations, meaning that it needs to add four fire brigade stations to the actual existing stations, which are four fire brigade stations. This additional number of stations will serve the expansion areas around the study area.

Acknowledgment. The author would like to extend his thanks to the Civil Defense Directorate in AL-Muthanna Governorate, the Department of Statistics in the city of Samawah for allowing him to obtain data and information and to communicate with them. The author would like to thank the staff from the Sanitary Engineering Laboratory and the Civil Engineering Department-College of Engineering, the University of Baghdad for their invaluable support in completing this work.

References 1. Maher, N.A.: Evaluation of the services civil defense centers. J. College Basic Educ. Educ. Hum. Sci. (27), 489 (2016) 2. Granito, J.A.: Evaluation and planning of public fire protection. In: Cote, A.E., Linville, J.L. (eds.) Fire Protection Handbook, 16th edn., pp. 15–90–15–101. National Fire Protection Association, Quincy (1986) 3. Muna, A.D., Abd-aljalel, D.A.: Evaluation of the efficiency of the distribution of civil defense centers in the urban centers of Wasit Governorate. J. Fac. Educ. (37), 467–492 (2019) 4. Osama, J.M., Sadiq, T.S.: Studying the efficiency of the spatial distribution of civil defense centers using geographic information systems GIS. J. Fac. Arts, 1113–1127 (2019). Special number for conferences 5. Shok, M.E.: Optimal saptial distribution of fire stations using geographic information systems Bagghdad case study. IOP Conf. Ser. Mater. Sci. Eng. 737, 012225 (2020) 6. Johanson, J.L.: A Procedure for Evaluuation of Fire Station Locations and Deployment (Executive Development). An applied research project submitted to the National Fire Academy as part of the Executive Fire Officer Program (1999) 7. Encyclopaedia Britannica: Al-Samawah, capital of Al-Muthanna governorate, Iraq (2011). https://www.britannica.com/place/Al-Samawah 8. Directorate of Civil Defense in Muthanna Governorate (2021)

12

W. A. Rzaij et al.

9. Directorate of Statistics in the city of Samawah (2021) 10. Bashir, M.: Spatial analysis of the distribution of civil defense centers in Muscat Governorate using geographical information systems, Kuwait Geographical Society, Issue No. 356ESRI, G. 2007. GIS for Fire Station Locations and Response Protocols. White Paper, Redlands, CA (2010) 11. Gay, W., Siegel, A.: Fire station location analysis: a comprehensive planning approach. MIS Rep. 19, 1–8 (1987) 12. Ministry of construction, Housing, Municipalities and Public Works. Urban Housing Standards (2018) 13. Ayad, A.F., Mohammed, S.R.: Spatial analysis of fire brigade stations in the city of Baghdad using information systems Geography. J. College Educ. (3), 134 (2012)

Drinking Water Assessment Using Statistical Analyses of AL-Muthana Water Treatment Plant Mohammed Abed Naser1,2(B) and Khalid Adel Abdulrazzaq1 1 Civil Engineering Department, University of Baghdad, Baghdad, Iraq [email protected], [email protected] 2 Directorate of Education Al-Muthana, Ministry of Education, Baghdad, Iraq

Abstract. Water is essential for survival, and controlling water quality is one of the most basic requirements for protecting this natural wealth from pollution and extinction. Statistical analysis technique was used with the SPSS v26 software to evaluate the quality of raw water period from (2016–2020), and 14 water parameters were assessed (Ka, Na, TSS, TDS, Mg, Ca, SO4 , Alk, TH, pH, Turbid, Temp, Cl, and Ec). Five principal components have eigenvalues value greater than unity and explain (76.159%) of the total variance of original data set. The first component was (28.678%) of the total variance with high loading on (TH, Ca, Mg, Cl and Ka), the second component was (16.141%) with positive loading on (TSS, Turb, and Temperature), the third component was (14.826%) with positive loading on (TDS and Ec), the fourth component was (8.929%) with positive loading in (Alk and SO4 ), and the last one has (7.59%) from total variance which high positive loading in (pH). The Multiple Linear Regression (MLR) results in a strong relationship between water conductivity and total suspended solid with other water parameters which the coefficient of determination (R2 ) values were 0.963 and 0.92. The ANN model was created to forecast river turbidity based on influent TSS, Mg, TDS, and Ca. The sum of squared errors and relative errors being (0.231, 0.101) and (0.009, 0.027) respectively, respectively, the error rate in predicting the model is low, indicating that the model is successful in predicting the turbidity of the river’s raw water. Keywords: Drinking water · Assessment · Statistical analyses

1 Introduction Water is one of the most important basic sources of life. Water pollution has a significant impact on human health, so keeping clean water free of pollution is a high priority for a disease-free life [1]. Detecting variations in the quality of drinking water through the use of statistical analysis techniques, which improve the reliability of the system in laboratories, allowing the administration to make the appropriate decision, and these techniques provide foresight into future changes and challenges confronting the authorities in charge of managing and regulating water [2]. Monitoring water quality over time and using statistical analysis is one of the most common and effective methods for evaluating time changes and environmental problems that occur in raw water sources © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 13–20, 2023. https://doi.org/10.1007/978-981-19-7358-1_2

14

M. A. Naser and K. A. Abdulrazzaq

based on chemical and physical parameters and biological indicators, and it contributes significantly to assisting researchers in changing the state of pollution [3]. According to [4], the temporal data of raw water quality is a fundamental method for discovering hypotheses that were not present when measurements were taken and were not expected, and it is of a standard value that reveals important patterns that allow us to identify trends and rare events that appear, and thus discover undesirable characteristics in the quality of drinking water. One of the most important components of machine learning and artificial intelligence is artificial neural networks. It is inspired by the structure of the human brain and operates as if it were made up of interconnected nodes where simple manipulations can be performed [5]. Artificial Neural Networks (ANNs), also known as neural networks, are cutting-edge computational systems and methods for deep learning, knowledge presentation, and finally applying acquired knowledge to maximize complicated system output responses [6]. Also, the principal component analysis (PCA) is another statistic technique used in water quality sample testing to summarize and abbreviate data, converting a large number of implicitly, albeit partially, correlated variables into a much smaller set of imaginary independent variables, which are usually called principal components. It is calculated primarily from the original variables in ratios and amounts that increase or decrease depending on the role and influence of the original variables [7]. This study aims to use statistical analysis tools to assess drinking water quality in Al-Muthanna Water Treatment Plant and find mathematical models that allow us to predict basic water quality parameters.

2 Materials and Methods The following points can summarize the statistical analysis and modeling prediction of source water using SPSS v26: • Multiple linear regression (stepwise regression model) was used for raw water quality parameters to find a mathematical relationship that predicts the value of (Ec and Turb) with other water parameters. • Principal component analysis (PCA): is a technique for identifying a smaller number of uncorrelated variables known as principal components from a larger set of data. This technique is commonly used to highlight differences and capture strong patterns in data sets. It is one of the most widely used methods for analyzing water quality data and reducing variables without affecting the system [8]. • The ANN model was created to forecast river turbidity based on influent TSS, Mg, TDS, and Ca turbidity. Water turbidity is one of the most important parameters indicating the quality of drinking water because it is an integrated parameter that is closely related to the rest of the water quality parameters and can be used to infer the quality of drinking water [9].

Drinking Water Assessment Using Statistical Analyses

15

3 Results and Discussion 3.1 Principal Component Analysis (PCA) The data were standardized, and the Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests were computed. The KMO test resulted in 0.62, and the Bartlett sphericity test resulted in less than 0.001. The value of (Kaiser-Meyer-Olkin (KMO) test and Bartlett sphericity test) equals (0.66), which is an acceptable value because the minimum value is (0.5), indicating that the measurement is good and the significant degree of the measurement has been reached (0). Eigenvalues accounts and scree plot in Table 1 listed that only five principal components have eigenvalues greater than unity and explain (76.159%) of the total variance in the data set. The first component accounted for about (28.678%) of the total variance with high loading on (TH, Ca, Mg, Cl, and Ka). Polyvalent mineral ions cause water hardness. Water hardness is formed primarily by calcium and magnesium. An increase in hardness has a negative impact on human health and the industries that use water [10]. The second component accounted for about (16.141%) of the total variance and has a height positive loading for (TSS, Turb, and Temperature). The increase in suspended matter and turbidity has a negative impact on drinking water quality and the efficiency of drinking water treatment plants [11]. The third component accounted for about (14.826%) of the total variance and has a height positive loading for (TDS and Ec). TDS and Ec are water quality parameters that indicate salinity. These two parameters are correlated and are usually expressed using the following simple equation: k Ec = TDS [12]. The fourth component accounted for about (8.929%) of the total variance and has a height positive loading in (Alk and SO4 ), and the last one has (7.59%) from total variance which high positive loading in (pH). Increased pH and Alk values indicate that the water contains alkaline salts like (NaOH) and (CaOH)2 [13]. Table 1. Total variance explained. Comp.

Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

Total

1

4.014

28.672

28.672

4.014

28.672

28.672

3.383

2

2.260

16.141

44.813

2.260

16.141

44.813

3.246

3

2.076

14.826

59.640

2.076

14.826

59.640

2.304

4

1.250

8.929

68.569

1.250

8.929

68.569

1.721

5

1.063

7.590

76.159

1.063

7.590

76.159

1.268

16

M. A. Naser and K. A. Abdulrazzaq

3.2 Multiple Regression Results (MLR) Multiple linear regression is an advanced statistical method that ensures the accuracy of inference in order to improve research results through the optimal use of data in finding causal relationships between the phenomena of the subject of study. The multilinear regression analysis was used to determine the relationship between the dependent and independent water quality parameters. The dependent variables (TSS and Ec) were selected, and the most independent influence parameter of water quality was chosen using a stepwise regression mod. The Multiple Regression Results listed in Table 2 and Fig. 1 a strong relationship between water conductivity and other water parameters, with a coefficient of determination (R2 ) value (0.963), indicating a positive relationship with a high degree of predictability. The test also indicated that electrical conductivity is a function of (TDS) which can be represented in Eq. (1) that can be used to predict current and future values Ec = 55.917 + 1.595 TDS

(1)

Table 2. Model summary of Ec prediction. Model

R

R2

Adjusted R2

Std. error of the estimate

1

.981a

.963

.953

10.4614

a Dependent variable Ec

1600 1400 1200

Ec observed

1000 800 y = 1.0041x - 5.9794 R² = 0.9968

600 400 200 0 400

600

800

1000

1200

1400

1600

Ec predicted

Fig. 1. Comparison of Ec (µS/cm) between observed and predicted values.

Table 3 and Fig. 2 listed that high relegation factor between (TSS) and other water quality parameters with (R2 ) value (0.92), and indicated that total suspended solid is a function of (Turbidity), which can be produced by the Eq. (2). TSS = 0.93 + 1.903 Turb

(2)

Drinking Water Assessment Using Statistical Analyses

17

Table 3. Model summary of TSS prediction. Model

R

R2

Adjusted R2

Std. Error of the estimate

1

.959a

.920

.897

6.5505

120

TSS observed

100 80 60 40 y = 1.0002x - 0.017 R² = 0.9018

20 0 0

20

40

60

80

100

120

TSS predicted

Fig. 2. Comparison of TSS (mg/L) between observed and predicted values.

Both above equations have been tested in the field and have proven to be reliable equations capable of producing very high results. 3.3 Artificial Neural Network (ANN) The neural network application is an important tool for predicting water quality in drinking water treatment plants in order to reduce analysis and operating costs, evaluate performance, and control operating conditions more broadly [14]. The ANN model was created to forecast river turbidity based on influent TSS, Mg, TDS, and Ca. The model required 61 input data points divided into 47 for training and 14 for testing. Standardization is the data scaling method, and the number of hidden layers is one. Table 4 listed the amount of error was small in both training and testing, with the sum of squared errors and relative errors being (0.231 and 0.101) and (0.009 and 0.027) respectively, which the error rate in predicting the model is low, indicating that the model is successful in predicting the turbidity of the river’s raw water. Table 5 explains the nature of the strength of the relationship between the independent and dependent factors in the input, hidden, and output layers, illustrated in Fig. 3. It shows that the gray line indicates positive values, and the blue line indicates negative values. The thickness of the line depends on the element’s influence in the prediction process, regardless of whether the value is positive or negative.

18

M. A. Naser and K. A. Abdulrazzaq Table 4. ANN model summary.

Training

Testing

Sum of squares error

0.213

Relative error

0.009

Stopping rule used

1 consecutive step(s) with no decrease in error

Training time

0:00:00.00

Sum of squares error

0.101

Relative error

0.027

Dependent Variable: Turbidity a. Error computations are based on the testing sample

Table 5. Hidden layer parameters parameter estimates. Predictor

Predicted Hidden layer 1

Input layer

Hidden layer 1

Output layer

H (1:1)

H (1:2)

H (1:3)

(Bias)

.118

.098

−.246

TSS

−.405

−.541

.420

Ca

−.026

−.122

−.337

TDS

.025

−.470

−.142

Mg

.169

−.307

.305

Turbidity

(Bias)

.332

H (1:1)

−1.624

H (1:2)

−.355

H (1:3)

.611

Figure 4 compares the prediction of turbidity concentrations based on different turbidity input parameters observed in this study. The expected trend follows the observed trend for all input data, and there is a significant convergence between the expected and actual values, with a small disparity.

Drinking Water Assessment Using Statistical Analyses

19

Fig. 3. Artificial neural network. 60

Predicted value

50 40 y = 0.9906x + 0.4242 R² = 0.9886

30 20 10 0 0

10

20

30 Turbidity observed

40

50

60

Fig. 4. Actual turbidity (NTU) versus predicted turbidity.

4 Conclusions The following were the conclusions reached through the use of statistical analysis techniques: • Only five principal components have eigenvalues value greater than unity and explain (76.159%) of the total variance in the data set.

20

M. A. Naser and K. A. Abdulrazzaq

• The Multiple Linear Regression (MLR) results in a strong relationship between water conductivity and total suspended solid with other water parameters which the coefficient of determination (R2 ) values were (0.963) and (0.92) sequentially, indicating a positive relationship with a high degree of predictability. • The ANN modeling results show that the model performed at 99.06% prediction accuracy

References 1. Juntunen, P., Liukkonen, M., Pelo, M., Lehtola, M.J., Hiltunen, Y.: Modelling of water quality: an application to a water treatment process. Appl. Comput. Intell. Soft Comput. 2012 (2012) 2. Nnorom, I.C., Ewuzie, U., Eze, S.O.: Multivariate statistical approach and water quality assessment of natural springs and other drinking water sources in Southeastern Nigeria. Heliyon 5(1), e01123 (2019) 3. Sun, X., et al.: Analyses on the temporal and spatial characteristics of water quality in a seagoing river using multivariate statistical techniques: a case study in the Duliujian River, China. Int. J. Environ. Res. Public Health 16(6), 1020 (2019) 4. Burt, T.P., Howden, N.J.K., Worrall, F.: On the importance of very long-term water quality records. Wiley Interdiscip. Rev. Water 1(1), 41–48 (2014) 5. Kujawa, S., Niedbała, G.: Artificial neural networks in agriculture. Agriculture 11(6), 497 (2021) 6. Chen, M., Challita, U., Saad, W., Yin, C., Debbah, M.: Artificial neural networks-based machine learning for wireless networks: a tutorial. IEEE Commun. Surv. Tutor. 21(4), 3039– 3071 (2019) 7. Teixeira de Souza, A., Carneiro, L.A.T., da Silva Junior, O.P., de Carvalho, S.L., AméricoPinheiro, J.H.P.: Assessment of water quality using principal component analysis: a case study of the Marrecas stream basin in Brazil. Environ. Technol. 42(27), 4286–4295 (2021) 8. Zavareh, M., Maggioni, V., Sokolov, V.: Investigating water quality data using principal component analysis and granger causality. Water 13(3), 343 (2021) 9. Iglesias, C., et al.: Turbidity prediction in a river basin by using artificial neural networks: a case study in northern Spain. Water Resour. Manage 28(2), 319–331 (2014) 10. Akram, S., Rehman, F.: Hardness in drinking-water, its sources, its effects on humans and its household treatment. J. Chem. Appl. 4(1), 1–4 (2018) 11. Serajuddin, M., Chowdhury, A.I., Haque, M.M., Haque, M.E.: Using turbidity to determine total suspended solids in an urban stream: a case study. In: Proceedings of the 2nd International Conference on Water and Environmental Engineering, Dhaka, pp. 19–22 (2019) 12. Rusydi, A.F.: Correlation between conductivity and total dissolved solid in various type of water: a review. In: IOP Conference Series: Earth and Environmental Science, vol. 118, no. 1, p. 012019. IOP Publishing, February 2018 13. Putro, P.G.L., Hadiyanto, H.: Water quality parameters of tofu wastewater: a review. In: IOP Conference Series: Materials Science and Engineering, vol. 1156, no. 1, p. 012018. IOP Publishing, June 2021 14. Nasr, M.S., Moustafa, M.A., Seif, H.A., El Kobrosy, G.: Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performanceEGYPT. Alex. Eng. J. 51(1), 37–43 (2012)

Simulation Design and Performance of a Residential Complex Using Liquefied Petroleum Gas Network Amna A. Farouq(B) and Basim H. K. Al-Obaidi Civil Engineering Department, University of Baghdad, Baghdad, Iraq {Amna.farouq2001M,dr.basimal-obaidy}@coeng.uobaghdad.edu.iq

Abstract. The population has been trying to use clean energy instead of combustion. The choice was to use liquefied petroleum gas (LPG) for domestic use, especially for cooking due to its advantages as a light gas, a lower cost, and clean energy. Residential complexes are supplied with liquefied petroleum gas for each housing unit, transported by pipes from LPG tanks to the equipment. This research aims to simulate the design and performance design of the LPG system in the building that is applied to a residential complex in Baghdad taken as a study case with eight buildings. The building has 11 floors, and each floor has four apartments. The design in this study has been done in two parts, part one is the design of an LPG system for one building, and the second part is the design of an LPG system for a complex containing eight buildings. The results were obtained by using mathematical equations and using the Pipe Flow expert v7.30 program to design and analyze with explaining steps in the program to design. Keywords: LPG · Simulation · Design · Performance · Pipe flow software · Residential complex

1 Introduction Many people who live globally lacked access to clean cooking, a modest rise over 2012. Although South and Southwest Asia had the most significant number of these people, Sub-Saharan Africa had a tremendous shortage. Because the population grows by 25 million per year, while access to clean cooking increases by just 4 million, access is proportional to population. Getting to Goal seven of the Sustainable Development Goals to ensure cheap and dependable access [1]. Because of its clean-burning properties and practical advantages over solid fuels and kerosene, liquefied petroleum gas (LPG), which primarily consists of propane and butane, is particularly well suited to residential cooking and heating needs. It is more convenient, safer, and cleaner in particular. It’s also lightweight and has a high calorific value, both volume, and mass. As a result, switching from solid fuels and kerosene to LPG can have significant health, developmental, and environmental benefits [3]. Liquid Petroleum Gases (LPG) classify as light gases like propane and butane. Among its characteristics, it is considered a gas at normal temperature and pressure © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 21–30, 2023. https://doi.org/10.1007/978-981-19-7358-1_3

22

A. A. Farouq and B. H. K. Al-Obaidi

and is liquid when exposed to moderate pressure and cooling. LPG is mainly used for domestic cooking. The use of gas has increased significantly to make some changes to control environmental sustainability and health reasons and save costs for the family. The gas transportation process is by a network of pipes [13]. LPG has many advantages over other fuels: clean-burning, high energy value, easy storage, no spills, no smoke, burners have a longer life span, requiring less maintenance, fuel that is good for the environment. Corrosion effects are minimized, and it can be used for a wide range of applications with avoiding scaling. Direct firing systems are effective and efficient since heat loss is minimized. LPG is used in various commercial, industrial, and domestic applications, including cooking, heat treatment, forklifts, and water heating (geysers and boilers) [2]. For this reason, the design steps were developed based on equations to reduce friction losses and maintain pressure and drain for consumer devices. A suitable pipe design is required for a safe LPG delivery. LPG must be delivered from the tank to the building’s pipelines. Another factor to be considered when establishing a pipeline is the environment. The building must be inspected and maintained regularly to look for problems and malfunctions in the tank and plumbing. A well-organized pipeline reduces the risk of accidents and boosts output [4]. The LPG storage tank is the most important part of the system. In Iran, the South Pars field has been developed to treat reservoir fluids, produce large gas quantities, and transport it to land [6]. The first LPG storage station under Lake Nomyang in the western part of Korea was designed to facilitate gas storage under ownership limits. Confidential evaluation of geological data and numerical analyses was carried out. The new depth of the site was determined to determine pressure to obtain the largest capacity and continuous monitoring during the construction of caves and storage tunnels [5]. Design simulation based on liquefied petroleum gas (LPG) pipeline networks for gas distribution (LPG). Gas networks in Nigeria were studied using total demand, which was calculated using the average monthly gas consumption in each household, head height, pipeline diameter, gas velocity, gas mass flow rate, head losses, and network pressure drop analysis [12]. Natural gas is produced in large quantities as a raw component in oil and gas wells. Although productive, the lack of efficient transmission and distribution systems burns a greater proportion of this energy source. Using pipeline simulation software, PIPEPHASETM, a network of pipelines to transport and distribute natural gas to the city of ORI, was designed. The gas distribution network is constructed in this work to deliver gas to at least four organizational divisions in a sequential manner [7]. The cost of transmission and distribution networks is one of the most important variables influencing natural gas prices. As a result, lowering the overall cost is critical. Gas networks are being fine-tuned to reduce network costs. This effort is a first step in creating computer code that can model and optimize gas distribution networks in all pressure ranges, including low, medium, and high-pressure networks. The goal is to keep network diameters as small as possible while maintaining maximum flow velocity and minimum node pressure [8]. The types of gas distribution networks are series, parallel, grating, toroidal and radial. The network consists of main tubes and subsidiary tubes. The series network contains two

Simulation Design and Performance

23

or more pipe connections, the flow is in one path in the system, but in the event of a failure, it is difficult to provide gas in the lines of the network and the low pressure at the end. A parallel network is a combination of two or more main pipes. The main disadvantage of this system is the increased cost of pipelines and fittings such as valves, but one of its advantages is that it improves pressure in service ports compared to a serial network. Grid pipeline networks refer to the interconnection of the main trunk, reticulation, and service pipes. The grid system has additional pipes and valves, which helps maintain consistent gas pressure at service outlets and is unaffected by pipeline failure because the system has several supply sources. The ring pipeline system is encircled by circular main line pipelines, with reticulation pipelines encircling some areas. Small areas will be impacted in the event of a breakdown because there are several supply sources, but the reliability is poorer due to the increased number of valves than other networks. The gas storage tank for the radial pipeline network system is located in the center of the estate, with the trunk lines spreading out radially towards the periphery service pipelines. In the event of a gas tank breakdown, the radial network is restricted by the accident’s severity. To design the pipeline network which depends on the slope and trajectory of gas quality and cost avoidance [12]. The number of compressor stations, diameter, length of pipeline segments, and operating conditions are all established simultaneously to optimize the gas network’s design [9]. To improve the design of the natural gas supply chain network, it is necessary to know the locations and suppliers of pipelines in the design of the gas supply network, the times of requests, prices, and technical specifications of gas. They were analyzed using GAMS 24.4.6 software optimization program was applied to two gas wells, three import operations (three contracts), two compressor stations, two refineries, two storage tanks, one receiving station, two oil well injection clients, two export clients, two types of towing and two storage type. The results showed that one compressor station is open and that one contract between the final receipt and the supplier is valid as the contract starts in the first period and continues during the second period [10]. To analyze networks, an algorithm is used on existing gas networks, where pressure ranges are analyzed and applied to low and high pressure. This algorithm was applied to the gas network in Egypt in the city of Alexandria, and the network has already been analyzed, and the optimization data of the network met the required restrictions [8]. For this reason, the research aimed to simulate the design and performance of the LPG system in the buildings, and a study area will be taken to implement the study’s objectives.

2 Case Study Description The complex is located in the city of Baghdad/Rusafa in the green area as shown in Fig. 1. The complex includes eight residential buildings, each building contains 11 floors, and each floor has four apartments so the number of apartments in the complex is 352 as shown in Figs. 2 and 3.

24

A. A. Farouq and B. H. K. Al-Obaidi

Fig. 1. Location of residential complex in Baghdad using GIS.

Fig. 2. Plan of the residential complex in Baghdad using AutoCAD program.

Simulation Design and Performance

25

Fig. 3. Plane of a typical floor in the building.

3 Design and Analysis Lpg Network Using Pipe Flow Expert Program Pipe Flow Expert is used to design and analyze complex pipeline network’s inflow and pressure balancing to solve the system and handle the flow of incompressible and compressible fluids. The pipelines in the program are very simple systems that can solve large and complex networks of connected pipes and may be simple for the distribution of liquids. Pipelines are modeled by drawing connection points between the pipes. Horizontal, vertical, or diagonal lines can be used to connect the nodes with the pipes. Data is entered, including internal volume, internal roughness, length of connected pipes, knot height, internal and external flow, liquid level, and surface pressure for each tank. When determining the density of the liquid and its viscosity under normal conditions and entering the temperature of the liquid, the program calculates the pressure and gas expansion in calculating the friction loss [11].

26

A. A. Farouq and B. H. K. Al-Obaidi

The design of supplying petroleum gas for outlets inside the residential building was carried out in two processes using mathematical equations in addition to the use of the Pipe Flow program. The system network design must calculate the tank’s size, the pipe’s diameter, their type’s material from NFPA (National Fuel Gas Code)-54, then calculate pipe cross-sectional area and gas velocity that can be calculated from Eqs. (1 and 2) [15]. Q A

(1)

πD2 4

(2)

V= A= V: velocity ft/s or (m/s). Q: flow rate ft3 /s or (m3 /s). A: area ft2 or (m2 ). D: diameter (in) or (mm).

While, Darcy’s formula is used to find the friction in pipe from Eq. (3) [15]. hL =

fLv2 2gD

(3)

f : friction in pipes of gas(0.0065) L: Length of pipe (ft) or (m). For pressure drop in pipes Eq. (4) is used [16] h=

Q2 SL D5 (0.0071)2

(4)

h: pressure drop (millibar) Q: flow rate (m3 /hr) S: specific gravity of the gas L: length of pipe (m) D: diameter of the pipe (mm) Using the previous mathematical equations, calculate storage tank volume for one building containing 11 floors and four apartments on each floor. The storage capacity required per one apartment is 1 kg/day (I.S). The appliances used in the building for cooking range 65000 Btu/hr. From Table (A.5.4.2.1) of [14], the total demand is 2860 ft3 /hr. So, the tank volume is 60 m3 . The common pressure tank ranges from (200–100) psi. Accordingly, in this study has been taken 6.89 bar. Extending pipes contain four stages. For the first stage of 20 ft which is from tank to first pressure regulator, the design piping calculations revealed that the total demand is 2860 ft3 /hr. By using HDPA SDR11 with a diameter of 100 mm and pressure reducing valve 0.7 bar. The next stage is 15

Simulation Design and Performance

27

ft, from the first regulator valve to the building, with the total demand of the building being 2860 ft3 /hr. by using HDPA SDR11, and a pipe diameter of 100 mm. The general velocity in the gas pipe should not exceed 20 m/sec, so the velocity in the outlet building is 2.77 m/sec (Eq. (1)) which is acceptable. The third stage consisted of a vertical pipe 72 ft length of the building, with a total demand of 715 ft3 /hr. According to the NFPA-54, copper material of a diameter of 20 mm should be used and with using Eq. (1) velocity of the gas in pipes is 19.7 m/sec. In the final stage, the horizontal pipe from the previous stage to the range has 5 m with 65 ft3/hr demand. And by using Table (6.2.1) in reference [14], the copper with a diameter 15 mm should be used and velocity is 4 m/sec. The design for the network residential complex as shown in Fig. 2, calculate the total demand for all building complex is equal to 22880 ft3 /hr. Tank volume is (243 × 2) m3 . Extending pipes from tank to building is HDPA material SDR11, but the diameters of piping different. The main diameter pipe is 125 mm and the service diameter pipe is 80 mm. The following steps are required to model the design above in the Pipe Flow program. Table 1 shows fluid data and attributes at the start, which were chosen from the program’s fluid properties as revealed in Fig. 4. Table 1. Information’s fluid data used in the program. Fluid name

LPG (propane mainly)

Chemical formula

C3 H8 (mainly)

Temperature °C

20.000

Pressure bar.g

0.0000

Density kg/m3

1.833105

Centistokes

4.370726

Centipoise

0.008012

State

Gas

The following steps defined the design and analysis in one building. The tank properties (outlet pressure of tank and elevation). Depending on the building design AutoCAD program files, pipes system of LPG with fitting and pressure reducing valve (PRV) has been drawn as illustrated in Fig. 5. Knowing that the PRV is used to reduce the pressure from the tank to the building, in study 1 PRV was assumed. Piping information should specify the material type of pipe, diameter, and fittings, as illustrated in Table 2. While the design and analysis for complex residential in the program are shown in Fig. 6, the piping information used in the program should specify the material type of pipe, diameter, and fittings, as illustrated in Table 3.

28

A. A. Farouq and B. H. K. Al-Obaidi

Fig. 4. Pipe flow fluid properties database.

Fig. 5. Building pipes system of LPG with fitting.

Table 2. Piping information used in the designed building. Pipe use

Material

Diameter (in)

Diameter (mm)

From tank to first regulator building

HDPA SDR11

4 in

100

Vertical pipe

Copper material

¾ in

20

Horizontal pipe

Copper material

½ in

15

Simulation Design and Performance

29

Fig. 6. Network residential complex system of LPG.

Table 3. Piping information was obtained from the design of the building using the program. Pipe use

Material

Diameter (in)

Diameter (mm)

From tank to first regulator building

HDPA sec11

6

150

Main pipe

HDPA sec11

3

80

Service pipe

HDPA sec11

2

50

4 Conclusions • Pipe flow program results revealed that utilizing the above calculation to build a home network, the pipe material, diameters, fittings, type of PRV, component pressure losses, velocity, and pressure drop are appropriate. • For the design of one building, the operating pressure of the tank is 6 bar, the first pressure regulator reduces pressure to 0.7 bar, the pressure loss is 5.5053 bar, and the end operating pressure of the range cooking is 0.3 bar. • For design complex residential, the operation pressure of one tank is 6 bar while the first pressure regulator reduces pressure to 0.7 bar and the pressure loss is 5.3 bar. The second pressure regulator reduces pressure to 0.4 bar, and the pressure loss is 0.29 bar. The end operating pressure of the range is 0.3 bar. • The total discharge for the building shown in the program results is 17152 ft3 /hr. At the same time, the operating discharge for the range is 65 ft3 /hr. • The velocity of the gas in piping as shown in the program results, doesn’t exceed 20 m/sec.

30

A. A. Farouq and B. H. K. Al-Obaidi

Acknowledgment. The authors wish to thank the staff of the sanitary engineering laboratory and the Civil Engineering Department/Engineering College-University of Baghdad for their valuable support to complete this work.

References 1. Van Leeuwen, R., Evans, A., Hyseni, B.: Increasing the use of liquefied petroleum gas in cooking in developing countries (2017) 2. AEGIS: LPG an Exceptional Fuel (2017) 3. World LP Gas Association, WLPGA: The socioeconomic impact of switching to LPG for cooking. Trevor Morgan, Menecon Consulting, Neuilly-sur-Seine, France (2018) 4. Arieta, A.T., et al.: LPG Pipeline Design of a Hotel a Capsule Proposal. Polytechnic University of the Philippines (2013) 5. Park, J.J., Jeon, S.: Design of Pyongtaek LPG storage terminal underneath the Lake Namyang. KSCE J. Civ. Eng. 9(2), 81–89 (2005) 6. Badalpur, M., Hafezalkotob, A.: Methodology based on MCDM for risk management in EPC projects: a case study of LPG storage tanks construction. J. Ind. Syst. Eng. 8(3), 1–23 (2015) 7. Anyadiegwu, C.I.C., Ohia, N.P., Ukwujiagu, C.M.: Natural gas transmission and distribution in Nigeria. Nat. Gas 2(8) (2015) 8. Djebedjian, B., El-Naggar, M., Shahin, I.: Optimal design of gas distribution network: a case study. Mansoura Eng. J. (MEJ) 36(3), 35–51 (2011) 9. Edgar, T.F., Himmelblau, D.M., Bickel, T.C.: Optimal design of gas transmission networks. Soc. Petrol. Eng. J. 18(02), 96–104 (1978) 10. Daghigha, R., Pishvaeeb, M.S.: Integrated optimization of gas supply chain network design problem and Liquefied Natural gas contract selection (2017) 11. PFE Guide: Pipe Flow Expert User Guide (2020) 12. Adegbola, A.A., Ozigis, I.I., Muhammad, I.D.: Conceptual design of gas distribution pipeline network for estates in Nigeria. Niger. J. Technol. 40(1), 25–36 (2021) 13. PRIF: LPG and Natural Gas as Alternative Energy Sources for the Pacific (2016) 14. NFPA-54: National Fuel Gas Code. ANSI Z223.1 (2018) 15. Mulyandasari, V.: Piping fluid flow material selection and line sizing (engineering design guidelines) (2010) 16. The Institute of Plumbing. IoP: Plumbing Engineering Services Design Guide (2002)

Disinfection Performance of Polyvinyl Chloride (PVC) Membrane Incorporating with AgNPs Asmaa N. Al-Himeiri(B) and Alaa H. Al-Fatlawi Department of Environmental Engineering, University of Babylon, Hilla, Iraq [email protected], [email protected]

Abstract. Water is an essential need that all life forms require for sustenance. However, there is significant concern about the access to safe potable water, particularly among individuals living in developing countries, especially in rural parts. Membrane technology has been seen as one such technology as disinfected water with constant high quality is now being produced using membrane technology as an alternative method by Dorcas (2016). The main objective of this research is to determine the coated filter’s performance related to disinfection and silver elution and how that performance changes over time. A flat sheet PVC micro filtration prepared from PVC (14 wt. %) added to dimethylacetamide (DMAc) (86 wt. %) has been utilized. Utilizing the disk diffusion test, the antimicrobial effect of AgNP solution against Escherichia coli (E. coli) has been explored. Experiments were conducted to quantify the deactivation of E. coli over time. The findings demonstrate that the disinfection efficacy (LRV) against Ecoli has been above 3.8 for a period of continuous filtration for synthetic feeding. The log10 reduction value (LRV) describes the bacterial removal efficiency. The Atomic Emission Spectrometer (AES) demonstrated that the silver eluted from the filters was less than 0.01 µg/L, well below the WHO’s recommended limit of 0.1 mg/L for safe potable water. Keywords: Micro-filtration · Membrane · Polyvinyl chloride · Silver nanoparticles · Disinfection · Cloves extract

1 Introduction Water is one of the important requirements for life on the earth, and most human activities involve the use of water in one way or another [1]. In most developing countries, a lack of drinkable water of sufficient quality is widely regarded as a major obstacle to health and economic development [2]. Point-of-use (POU) systems treat only the part of water utilized for potable [3]. Water treatment devices of various types and proper safety technologies, such as filtration, disinfectants, distillation, solar disinfectants, reverse osmosis, and water purifiers, have started to reduce endemic diarrhea as a result caused by waterborne illnesses and enhance the microbial and chemical water quality [4]. The remediation of drinkable water at the domestic level by membrane separation has recently become more appealing as potable water guidelines have become © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 31–37, 2023. https://doi.org/10.1007/978-981-19-7358-1_4

32

A. N. Al-Himeiri and A. H. Al-Fatlawi

more stringent [2]. Microfiltration (MF) is commonly utilized for separating the suspended solids and is applied to simple-to-treat waters. These were clear, cold bodies of water that were potentially contaminated by microorganisms [5]. The combination of MF and the antibacterial capability of nano-silver is a solution for optimizing the performance of the microfiltration process [6]. Nanomaterials are excellent sensors, catalysts, and adsorbents since their high reactivity and large specific surface area, and silver nanoparticles were stated to be the most effective antimicrobial agent [7]. Unlike traditional chemical disinfectants, these antimicrobial nanomaterials were not strong oxidants and were moderately inert in water. As a result, they are unlikely to produce harmful disinfection by-products (DBPs) [8]. Green synthesis of silver nanoparticles is a biocompatible and environmentally friendly process that generally includes utilizing a capping agent-stabilizer (yeast, plant extracts, or bacteria) to control the size and prevent agglomeration [9]. The principal objective of this research is to determine the coated filter performance related to disinfection and silver elution and how the performance is impacted over time.

2 Materials and Methods 2.1 Materials PVC microfiltration membrane E. coli inoculation has been obtained from the Microbiology Lab/College of Science/Babylon University. Clove powder, nutrient agar, silver nitrate, and distilled water have been obtained from the has been purchased from Essam office for scientific equipment/ Hilla. 2.2 Methods 2.2.1 Incorporation of AgNPs on the PVC Membranes In this study, a flat sheet PVC micro filtration syntheses in the Chemical Engineering Department/University of Technology from PVC (14 wt. %) added to DMAc (86 wt. %) has been utilized. An aromatic clove plant was extracted as a reducing agent and stabilizer. The prepared PVC membrane was immersed with AgNPs in dark conditions. Depending on the Well diffusion methods, the membrane was embedded in a 7.5 ml AgNO3 solution with an amount of 0.003 M for 30 min. 41.5 mL deionized water and 1 mL clove extract with ph = 6 in a beaker stirred for 30 min, then the embedded membrane with silver nitrate (AgNO3 ) was added to the beaker. After adding the reaction mixture, stir for 20 min at 70 °C. The membrane has been incubated overnight while being constantly stirred. 2.2.2 Well Diffusion Methods The diffusion method has been utilized to assess silver nanoparticles’ antibacterial properties (AgNPs) produced from clove aromatic plant extract against Escherichia coli (E. coli). In sterile surroundings, 20 mL of nutrient agar medium has been cast into autoclaved Petri plates and left to solidify. Following that, 1 mL of synthesized feeding

Disinfection Performance of Polyvinyl Chloride (PVC) Membrane

33

containing (333 × 103 ) amounts of Ecoli was swabbed on the agar plate and uniformly spread with sterile cotton swabs. Agar wells with a diameter of 8 mm were prepared to utilize a sterile cutting machine. The wells were poured with a sample of nanoparticles solution utilizing a micropipette (50 µL, 100 µL, and 150 µL). The dish was incubated for 24 h at 37 °C, before being explored for the presence of inhibition zones. The clear zone has been calculated and expressed in millimeters. 2.2.3 Membrane Disinfection Efficacy Over Time The present study attempted to investigate the coated filters’ performance over time by utilizing synthetic feeding. 20 L of synthetic feeding (66000 CFU/100 mL) was filtered, and 100 mL of permeate was selected at random into a sterilized glass bottle and evaluated E. coli and silver present. Disinfection efficacy of Ecoli is obtained utilizing the expression (Eq. 1): LRV = log10 (Cf/Cp)

(1)

where: Cf = The amount of E. coli in feeding (CFU/100 mL). Cp = The amount of E. coli in permeate (CFU/100 mL).

3 Results and Discussion 3.1 Well Diffusion Silver nanoparticles synthesized from natural plant extracts demonstrated effective antimicrobial activity. Table 1 records the inhibition zone diameter around every well (150, 100, and 50 µL). As illustrated in Fig. 1, 150 µL of silver nanoparticles were stated to have the highest antimicrobial activity against E. coli (20 mm), and 50 µL of silver nanoparticles had the lowest antibacterial effect (17 mm). Table 1. The inhibition zones of nanoparticles were synthesized by extracting plant versus Escherichia coli. Silver nanoparticle (µL)

Inhibition zone (mm) against Ecoli

50

17

100

18

150

20

34

A. N. Al-Himeiri and A. H. Al-Fatlawi

Inhibition zone

AgNPs sample

Fig. 1. The inhibition zones of AgNP in the well diffusion method.

3.2 Effect of Time on Silver Elution from AgNPs Coated Materials Silver elution is caused primarily by weak adhesion forces between AgNPs and the PVC membrane, and also nano-silver dissolution in water leads to the release of Ag ions [10]. Nanomaterials in potable water at amounts above allowable levels lead to health issues for users and are thus unacceptable. Deionized water has been filtered with a system test for 2 h at 1 bar to investigate the evolution of silver elution over time and the effect of permeate flow rate on silver leaching. An Atomic Absorption Spectrophotometer has been utilized to determine the silver amount of filtered water analysis every 15 min. As demonstrated in Figs. 2 and 3, silver elution decreased significantly during continuous filtration due to the decrease in the rate of flux decline after the first two h of operation. A large percentage of the silver release appears to occur at the beginning of the immersion in the water. This decrease in the silver release is subjectively similar to other studies [11, 12].

Silver concentration, μg/L

0.008 0.007 0.006 0.005 0.004 0.003 0

15

30

45

60

75

90

Time, min.

Fig. 2. Silver amounts permeate over time.

105

120

Disinfection Performance of Polyvinyl Chloride (PVC) Membrane

35

Flow rate, L/min.

0.11

0.09

0.07 0

15

30

45

60

75

90

Time, min.

105

120

Fig. 3. Permeate flow rate over time.

Silver ion amounts in the permeate were very low infiltration testing, with less than 0.01 mg/l, satisfying the United States Environmental Protection Agency’s (US-EPA) potable water guideline (less than 0.1 mg/L). Furthermore, that is related to the filter’s silver nanoparticles’ stability. Employing silver immobilized membranes for membranes is unlikely to cause any health risks, as the Ag released has been significantly lower than the WHO standard. 3.3 Disinfection Efficacy Over Time Disinfection in this study refers to both physical removals of Ecoli due to size rejection by membrane separation and inhibition by AgNPs. As demonstrated in Fig. 4, the disinfection efficacy against Ecoli is greater than 3.8. Furthermore, the silver nanoparticle-coated PVC membrane’s performance has been assessed in silver in effluent and adherence to potable-water quality standards. This demonstrates that the PVC membrane incorporated with AgNPs had effective antimicrobial activity against Ecoli. 4

LRV

3 2 1 0 15

30

45

75

90

105

120

Time, min

Fig. 4. Effect of sample collection time on the disinfection efficacy for 66000 CFU/100 mL synthetic feeding.

36

A. N. Al-Himeiri and A. H. Al-Fatlawi

Pore blockage by Ecoli may be to blame for the enhanced rejection in synthetic feeding water. Aside from pore blockage, Ecoli’s sticky biofilm release after dying could also contribute to E. coli rejection in synthetic feeding water. According to the studies of the membrane bioreactor by [13], Because it coats the membrane’s surface, this biofilm completely closes the pores and is typically very sticky. These findings indicate that Ag+ has been the primary antimicrobial agent. 3.4 Disinfection Efficacy Versus Leaching Rate Utilizing an Atomic Absorption Spectrometer, permeate samples from the disinfection performance filters were collected at irregular intervals and tested for silver leaching. The elution of silver results from the filters is depicted in Fig. 5. Silver leaching declined over time, most likely because the silver amount on the filters was reduced. This find agrees with reference [14], who reported that the amount of silver in the filtrate decreases as more water has been filtered. When 10% of the silver applied to the UF polysulfone membranes was lost, the antibacterial and antiviral properties of the coated membranes were significantly reduced.

Silver concentration,μg/L

0.01 0.008 0.006 0.004 0.002 0 0

15

30

45

60

75

90

105

120

Time, min.

Fig. 5. Silver amount from the membrane with time.

Although silver elution contributes to the disinfection efficacy, it also reduces the amount of silver on the membranes, potentially reducing the filter’s effectiveness. Bacterial removal effectiveness improves with membrane usage and increased filtration time. This is due to the biological layer maturing due to increased membrane usage. When the pores become clogged due to dirt collection, the biological layer is also responsible for the decrease in flow rate, which lengthens the contact period between the membrane and the biological layer, which agrees with the results [10].

4 Conclusions Using a Polyvinyl chloride microfiltration membrane to treat water eliminates the need to add chemicals to the water, avoiding chemical taste and odor issues as well as the

Disinfection Performance of Polyvinyl Chloride (PVC) Membrane

37

formation of disinfection by-products, many of which are toxic (carcinogenic) over time. The efficacy of E. coli removal is greater than 99.8%. Furthermore, the silver nanoparticle-coated PVC membrane performance was assessed in terms of the presence of silver in the effluent less than 0.01 ppb, well below the WHO’s recommended limit of 0.1 mg/L for safe drinking water.

References 1. Al-Mamori, H.Z., Al-Fatlawi, A.H.: Floating plastic media filtration system for water treatment. MS.C thesis, Environmental Engineering, University of Babylon (2017) 2. Alfa, D., Rathilal, S., Pillay, V.L., Pikwa, K., Chollom, M.N.: Development and evaluation of a small scale water disinfection system. J. Water Sanit. Hyg. Dev. 6(3), 389–400 (2016) 3. Pooi, C.K., Ng, H.Y.: Review of low-cost point-of-use water treatment systems for developing communities. NPJ Clean Water 1(1), 1–8 (2018) 4. Mwabi, J.K., et al.: Household water treatment systems: a solution to the production of safe drinking water by the low-income communities of Southern Africa. Phys. Chem. Earth Parts A/B/C 36(14–15), 1120–1128 (2011) 5. Van Ginkel, S.W., Lamendella, R., Kovacik, W.P., Jr., Santo Domingo, J.W., Rittmann, B.E.: Microbial community structure during nitrate and perchlorate reduction in ion-exchange brine using the hydrogen-based membrane biofilm reactor (MBfR). Biores. Technol. 101(10), 3747–3750 (2010) 6. Vatanpour, V., Madaeni, S.S., Moradian, R., Zinadini, S., Astinchap, B.: Fabrication and characterization of novel antifouling nanofiltration membrane prepared from oxidized multiwalled carbon nanotube/polyethersulfone nanocomposite. J. Membr. Sci. 375(1–2), 284–294 (2011) 7. Zhang, S., Tang, Y., Vlahovic, B.: A review on preparation and applications of silvercontaining nanofibers. Nanoscale Res. Lett. 11(1), 1–8 (2016) 8. Li, Q., et al.: Antimicrobial nanomaterials for water disinfection and microbial control: potential applications and implications. Water Res. 42(18), 4591–4602 (2008) 9. Ahmad, S., et al.: Green nanotechnology: a review on green synthesis of silver nanoparticles— an ecofriendly approach. Int. J. Nanomed. 14, 5087 (2019) 10. Achisa, C.M.: Evaluation of silver nanoparticles impregnated woven fabric microfiltration membranes for potable water treatment (Doctoral dissertation) (2014) 11. Dong, C., Wang, Z., Wu, J., Wang, Y., Wang, J., Wang, S.: A green strategy to immobilize silver nanoparticles onto reverse osmosis membrane for enhanced anti-biofouling property. Desalination 401, 32–41 (2017) 12. Rus, A., Leordean, V.D., Berce, P.: Silver Nanoparticles (AgNP) impregnated filters in drinking water disinfection. In: MATEC Web of Conferences, vol. 137, p. 07007. EDP Sciences (2017) 13. Judd, S.: The MBR Book: Principles and Applications of Membrane Bioreactors for Water and Wastewater Treatment. Elsevier, Amsterdam (2010) 14. Zodrow, K., et al.: Polysulfone ultrafiltration membranes impregnated with silver nanoparticles show improved biofouling resistance and virus removal. Water Res. 43(3), 715–723 (2009)

Optimal Bedding Selection with the Specific Soil Type According to the Thrust Forces Generated in the Water Distribution Networks Using the Restraining Joint System Murtadha H. Dawood(B) , Amer F. Izzet, and Basim H. Khudair Civil Engineering Department, University of Baghdad, Baghdad, Iraq [email protected], {amer.f, dr.basimal-obaidy}@coeng.uobaghdad.edu.iq

Abstract. A study has been performed to compare the beddings in which ductile iron pipes are buried. In water transmission systems, bends are usually used in the pipes. According to the prescribed layout, at these bends, unbalanced thrust forces are generated that must be confronted to prevent the separation of the bend from the pipe. The bed condition is a critical and important factor in providing the opposite force to the thrust forces in the restraint joint system. Due to the interaction between the native soil and the bedding layers in which the pipe is buried and the different characteristics between them. Also, the interaction with the pipe material makes it difficult to calculate the real forces opposite to the thrust forces and the way they are distributed. For that, a numerical simulation of the soil-pipe interaction of the five types of bedding (A, B, C, D and E) specified by [11] was performed to find the effect of bedding and choose the best possible type using properties of a local type of soil with ABAQUS CAE/2019 program. The results obtained from the software were relied on (Von Mises stresses that transmitted to the native soil, length of stresses on both sides of the bend, frictional resistance provided by bedding type, and maximum displacement occurring in the pipe). Based on these results, the optimal type of bedding for the studied soil type was type E. It was concluded that the frictional resistance is the most affected when changing from the worst type A to the best type E, while the length of the stresses is the least affected. Keywords: Thrust force · Bedding types · Joint restraint system · Water distribution systems

1 Introduction Pipes are the ideal and common way to transport fluids between two areas [1, 2]. Sometimes, difficulties or barriers force changing the pipes’ path at different angles [3]. At these bends, an unbalanced force called thrust forces will be generated that can cause damage to the joint that may lead to the separation of the pipe in this region, but this force may be resisted using a thrust block system or joint restraint system both [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 38–51, 2023. https://doi.org/10.1007/978-981-19-7358-1_5

Optimal Bedding Selection with the Specific Soil Type

39

Each method is scientifically and practically feasible in specific conditions [5]. A joint restraint system represents an engineering system capable of resisting excessive axial movement and separation of the pipe from the joint [6]. The main purpose of using this system is to transfer the unbalanced force (thrust force) to the soil surrounding the pipe safely and without exceeding the internal resistance of the soil [7]. The counter force to thrust force is generated from two sources, the first source is the friction between the soil and the pipe, and the second is the bearing between the soil and the pipe [8]. These forces are mainly dependent on the diameter of the pipe, internal pressure, soil properties, and the depth of the cover over the pipe [4]. Soil properties are the main factor in determining the counterforce per unit length [9]. The goal of designing this system is to find the length to be restricted to the soil to the right and left of the bend [6]. Since this system depends on the properties of the soil and on the internal forces of the soil, this system cannot be used for pipes above the surface of the ground, and other types of support must be provided [10]. Since the magnitude of the forces opposed to the thrust forces depends on the properties of the soil surrounding the pipe, a bed for the pipe must be chosen from good materials that provide high resistance to the thrust forces. Five types of bedding are stipulated in [11], each provides a certain passive bearing resistance and provides a specified restraint length. Each organization or specification deals with the interaction between the pipe and trench bed separately from the other. Some of them enter it explicitly, such as the (British Code) and some not explicitly, such as (Ductile iron pipe research association).

2 Restraint Joint Design Theory In order to design a joint restraint system, it is necessary to understand the principle of the general theory and to know the variables involved in this design, such as total thrust force and variables related to the characteristics of the soil. When the water flows inside the pipe in a straight line, the thrust resulting from the flow of the water is resisted internally by the force that opposes it at the same point (the principle of equilibrium). At that, friction and bearing resistance provide as an additional resistance only. Whereas when a bend occurs in the pipe path, the thrust force does not find the opposite at the bend region. It will try to push the bend out of the pipe path. Using the self-anchoring system alone without thrust blocks, the pipeline behaves as a thrust block that transfers thrust force to the surrounding soil. The total thrust force that affects the horizontal bend is expressed by Eq. 1: ϕ T = 2PAsin( ) 2 where: T = Total thrust force. P = Internal pressure. ϕ = Angle of the bend. A = Internal area of the pipe.

(1)

40

M. H. Dawood et al.

While the force generated by the soil surrounding the pipe in reaction to the thrust force is stated by Eq. 2: 1 θ ϕ R = Fs Lr cos( ) + Rs Lr cos( ) 2 2 2

(2)

where: Fs = Unit frictional force between soil and pipe. Lr = Pipe length to be restricted. Rs = Unit bearing resistance of the soil. Figure 1 shows the maximum thrust on the curve of the tube, as well as the reaction of the soil to this force:

Fig. 1. Resultant frictional, bearing, and thrust forces on a pipe bend [12].

By equalizing the acting force (the thrust force) and the soil reaction in the direction of the thrust force, the length to be restricted on both sides of the joint can be found by Eq. 3: (3) SF = factor of safety. American Water Works Association clarified how to calculate the (Fs) and (Rs) [13].

3 Design Variables To design of the restraint joint system, many variables must be taken into account, some of which relate to the hydraulic analysis (the internal pressure of the pipe) and the others relate to the properties of the native soil in which the pipe is to be buried and trench dimension (width, depth). Also, some variables related to the material of the pipe are secondary to the numerical modeling of the pipe. In the common case of most water distribution networks, the operating pressure in the water distribution systems usually is between 60 psi and 75 psi [14]. In the design, the maximum pressure generated inside the pipe is calculated as test pressure or maximum hydrostatic pressure that usually

Optimal Bedding Selection with the Specific Soil Type

41

equals approximately one and a half times the working pressure. In many cases, the pipe is tested under a pressure of 10 bar, which is approximately 150 psi, so this value of pressure will be adopted as a critical case to be studied. Test pressure depends on the pipelines in order to know the maximum pressure in the pipes without any leakage, while the Angle of bend to be studied which will cause the thrust force in the pipeline is 45°, and the pipe is made of ductile iron with the following properties demonstrated in Table 1. Table 1. Pipe properties. Property

Magnitude

Outer diameter (m)

1.13

Inner diameter (m)

1.06

Wall thickness (m)

0.035

Modulus of elasticity (kPa)

170,000,000

Poisson ratio

0.27

The trench depth will be 3m under the native soil surface, while the trench is usually expressed as a function of pipe size [4]. Thus, the width of the trench will be 1.67 m. As for the characteristics of the native soil, they have been calculated on the site and in the laboratory by making investigations for the soil as well as gathering information from other published issues [15–17]. The most important characteristics that enter into the design of the restraint joint system are summarized in Table 2. Table 2. Native soil properties. Property

Magnitude

Soil type (USCS)

SM

Dry density of native soil (kN/m3 )

15

Soil cohesive (kPa)

6.0

Internal friction angle (degree)

28.7

Modulus of elasticity (kPa)

12,000

Poisson ratio

0.3

Regarding the values of the modulus of elasticity, it was adopted according to [18] whereas the value of Poisson’s ratio was [19]. The characteristics of the soil layers used in each type of bedding; will be mentioned when studying each type separately.

42

M. H. Dawood et al.

4 Verification of Numerical Model A complete description and numerical simulation of the soil-pipe interaction of the five types of bedding (A, B, C, D, and E) will be provided and extract the required results that give an indication and a decision about the best type of laying that can be used, depending on the variables established in Sect. 3. Pipe bedding types are illustrated in Fig. 2.

Fig. 2. Laying condition types [11].

All details of them can be known from [11]. The types of bedding were modeled using ABAQUS CAE/ 2019 software. The total length of the pipe is 50 m, extended for 25m on each side of the bend and was represented as the shell element, meshed using 4572 linear quadrilateral element of type SAR, as illustrated in Fig. 3.

Fig. 3. Pipe mesh.

The soil was modeled with a three-dimensional solid element, built-up with a crosssection dimension equal to (28 × 14 m) along the length of the pipe with the same bending Angle, and meshed with 26718 hexahedral elements of type C3D8R except for regions adjacent to the pipe where 1220 linear wedge element of type C3D6 were used (Fig. 4).

Optimal Bedding Selection with the Specific Soil Type

43

Fig. 4. Soil mesh.

a) Laying condition (type A). The soil layers of this type are illustrated in Fig. 5.

Native soil backfills (SM) Native soil (SM)

Fig. 5. Laying type A model

The properties of the soil under the pipe as well as the properties of the backfill soil, are mentioned in Table 2. The analysis was carried out under the influence of the thrust force at a bend to calculate the stresses formed in the native soil and to calculate the friction resistance provided by the type of bedding and the maximum movement of the pipe at the bend. Figures 6, 7, and 8 show the results obtained from the analysis:

Fig. 6. Stress distribution at native soil (MPa).

Fig. 7. Frictional resistance of bed (MPa).

44

M. H. Dawood et al.

Fig. 8. Pipe movement due to thrust force (mm).

It can be noted that the maximum stress in the native soil was 40 kPa extended along 37.2 m of the pipe length and the maximum friction resistance provided by this type of bedding was 5.0 kPa, in contrast, the maximum movement occurred at the bend was 31.949 mm. b) Laying condition (type B). The soil layers of this type are shown in Fig. 9.

Native soil backfills (SM) Lightly consolidated soil (SM) Native soil (SM)

Fig. 9. Laying type B model.

The physical properties of the soil under the pipe are mentioned in Table 2. In contrast, the backfill to the center of the pipe is lightly consolidated (its dry density is equal to16 kN/m3). At the same time, the other characteristics do not change. The backfill characteristics above the pipe are mentioned in Table 2. The results obtained after the analysis are shown in Figs. 10, 11, and 12. By using type B laying condition, the enhancements were observed represented with reducing the maximum stress in the soil to 38 kPa, which extend to 33.6 m of the pipe length, and the maximum friction resistance provided by this type of laying was 8.0 kPa, as well the reduction in the maximum movement occurred in the joint which was 27.063 mm.

Optimal Bedding Selection with the Specific Soil Type

Fig. 10. Stress distribution at Native soil (MPa).

45

Fig. 11. Frictional resistance of bed (MPa).

Fig. 12. Pipe movement due to thrust force (mm).

c) Laying condition (type C). The soil layers of this type are shown in Fig. 13.

Native soil backfills (SM) Lightly consolidated soil (SM) Native soil (SM)

Fig. 13. Laying type C model.

Type C has the same characteristics as types A and B soil, except for lightly consolidated soil, continuing to the top of the pipe. The analysis results are shown in Figs. 14, 15 and 16. The maximum stress in the soil was reduced to 34 kPa along the pipe length of 32.27 m, and the maximum friction resistance provided by this type of laying was 9.0 kPa, while the maximum movement at the pipe bend was 23.983 mm. d) Laying condition (type D). The soil layers of this type are illustrated in Fig. 17.

46

M. H. Dawood et al.

Fig. 14. Stress distribution at Native soil (MPa).

Fig. 15. Frictional resistance of bed (MPa).

Fig. 16. Pipe movement due to thrust force (mm).

Native soil backfills (SM) Compacted backfill (SM) well-graded clean sand (SW) Native soil (SM)

Fig. 17. Laying type D model.

In this type, the bottom of the pipe is a layer of well-graded clean sand with a thickness of 0.14 m, the soil characteristics properties are listed in Table 3. While the soil on both sides of the pipe (SM) up to the top is compacted to about 80% of the standard Proctor test (dry density 17.5 kN/m3 ). The backfill over the pipe has the same properties revealed in Table 2. After transferring to the fourth type of bed and conducting the analysis, the results are as shown in Figs. 18, 19, and 20. It can be observed that the maximum stress in the soil reduced to 21 kPa extended to a length of 29.22 m, and the maximum friction resistance provided by this type of bedding was 11.0 kPa, whereas the movement which was noticed in the pipe bend was 21.746 mm.

Optimal Bedding Selection with the Specific Soil Type

47

Table 3. Well-graded sand properties. Property

Magnitude

Soil type (USCS)

SW

Dry density (kN/m3 )

18

Soil cohesive (kPa)

0

Internal friction angle (degree)

38 (20)

Modulus of elasticity (kPa)

100,000

Poisson ratio

0.3

Fig. 18. Stress distribution at Native soil (MPa).

Fig. 19. Frictional resistance of bed (MPa).

Fig. 20. Pipe movement due to thrust force (mm).

e) Laying condition (type E). The soil layers of this type are shown in Fig. 21. This type is similar to type D, but the SW soil used in this type is well-graded dense sand with internal friction angle (38°) [20] supplied underneath and on both sides of the pipe up to the center of the pipe. The remaining circumference of the pipe is of the same backfill (SM). Still, it is compacted with 90% of the standard proctor test (dry density approximately 18.5 kN/m3 ), the backfill above the pipe remains as the previous types. The results are shown in Figs. 22, 23, and 24. The maximum stress in the soil was 17.0 kPa along the pipe length of 26.5 m, and the maximum friction resistance provided by this type of laying was 12.0 kPa, while the lesser movement at the pipe bend was observed (17.818 mm).

48

M. H. Dawood et al.

Native soil backfills (SM) Compacted backfill (SM) well-graded clean sand (SW) Native soil (SM)

Fig. 21. Laying type E model.

Fig. 22. Stress distribution at Native soil (MPa).

Fig. 23. Frictional resistance of bed (MPa).

Fig. 24. Pipe movement due to thrust force (mm).

5 Results and Discussion After modeling and simulating the five types of bedding and extracting results that can indicate the optimal type of bedding in a specific soil, the results of the five types will be compared to know the effect of changing the of bedding and choosing the optimal type. The comparison was based on Von mises stresses that transmitted outside the trench to the native soil as a result of the thrust force after being resisted by the layers of trench soil, length of these stresses at right and left of bend, based on frictional resistance that offered by each type and based on maximum movement that occurred at pipe bend [4]. Figures 25, 26, 27 and 28 illustrate the comparison between the five types of bedding based on the numerically obtained results.

50 40 30 20 10 0

35 30 25 20 15 10 5 0

Type A

Type B

Type C

Ttpe D

Type A

Type E

Fig. 25. Max. Stress on native soil.

Type B

Type C

Type D

Type E

Fig. 26. Length of stress on native soil.

14

35

Max. movement of pipe (mm)

Frictional resistance of bedding types (kPa)

49

40

Length of stresses on native soil (m)

Von Mises stress on native soil (kPa)

Optimal Bedding Selection with the Specific Soil Type

12 10 8 6 4 2 0 Type A

Type B

Type C

Type D

Type E

Fig. 27. Max. Frictional resistance.

30 25 20 15 10 5 0 Type A

Type B

Type C

Type D

Type E

Fig. 28. Max. Movement at pipe bend.

After reviewing the results in the previous section, the five types are compared in terms of the obtained results. Table 4 shows the variations of the obtained results in comparison with the type A. Table 4. Comparison between laying types. Laying Max. (%) Length (%) Max. (%) Frictional (%) condition Stress Decrease of Decrease Movement decrease resistance increase (kPa) stress (mm) (kPa) (m) A

40

–

37.2

–

31.949

–

5

–

B

38

5.00

33.6

9.67

27.063

15.29

8

37.50

C

34

15.00

32.27

13.25

23.983

24.93

9

44.44

D

21

47.50

29.22

21.45

21.746

31.93

11

54.54

E

17

57.50

26.5

28.76

17.818

44.22

12

58.33

Results show that the variation of bedding properties is mainly affected by decreasing the maximum generated stress in native soil and increasing the soil friction resistance. They decrease the pipe movement and limit the stress to extend the length. Using bedding

50

M. H. Dawood et al.

condition type E, the enhancement was recorded by decreasing native soil stress and its extended length, 57% and 26%, respectively, with 58% increasing soil friction. In contrast, pipe movement decreased by 44% compared with type A bedding. Also, according to the stresses transmitted to the native soil, the maximum change occurs when bedding conditions shift from type C to D. Acording to provide friction, the clearest change occurs when type B is used instead of A. Also, when the maximum movement in the joint is the governing factor, then type E is preferable to D.

6 Conclusions • Type E represents the optimal choice among the five types in terms of providing high friction resistance, reducing pipe movement, and reducing stress on the native soil. • All the obtained results are based on a specific type of original soil, as its properties represent the main factor in choosing the type of bedding; therefore, the original soil should be studied well before choosing the bedding type. • It is possible that changing the type of bedding has a limited effect if the native soil has properties similar to those stipulated in the types of bedding.

Acknowledgment. I would like to thank the staff of the Civil Engineering Department/University of Baghdad for their support and assistance to complete this paper.

References 1. Liu, H.: Pipeline Engineering. CRC Press (2017) 2. Watkins, R.K.: Structural mechanics of buried pipes. CRC Press (1999) 3. Hussien, O.S.: Comparative study for designing the horizontal thrust blocks in pipelines for water and sewage networks. Water Sci. Technol. 84(5), 1302–1308 (2021) 4. Ductile Iron Pipe Research Association (DIPRA): Thrust Restraint Design for Ductile Iron Pipe, 7th edition Ductile Iron Pipe Research Association, P.O. Box 190306, Birmingham, AL 35219 (2016) 5. Dawood, M.H., Izzet, A.F.and Khudair, B.H.: Comparative study between the behavior of the concrete thrust block and the restraint joint in a water distribution system-review. J. Eng. 28(5) (2022) 6. United States Pipe and Foundry Company (U.S.PIPE). Restrained Joints for ductile iron pipelines (2006) 7. Jeyapalan, J.K., Rajah, S.K.: Unified approach to thrust restraint design. J. Transp. Eng. 133(1), 57–61 (2007) 8. Ebaa Iron Connection. Thrust Restraint Design Equations and Soil Parameters for Ductile Iron and PVC Pipe (1995) 9. Farhadi, B., Wong, R.C.: Numerical modeling of pipe-soil interaction under transverse direction. In International Pipeline Conference, vol. 46100, p. V001T03A021. American Society of Mechanical Engineers, September 2014 10. Plumping, Heating, cooling and piping (PHCP) (2018). https://www.phcppros.com/articles/ 7531-water-main-failures

Optimal Bedding Selection with the Specific Soil Type

51

11. American Water Work Association ANSI/AWWA C151/A21.51-09 (2009). Ductile-Iron Pipe, Centrifugally Cast 12. Rahman, S., Diamond, M.: Containing Thrust Forces in Municipal Pipelines: An Integral Joint Restraint System for PVC Pressure Pipe. Plastic Pipes XII. CDROM. Transportation Research Board of the National Academies, Washington, DC (2006) 13. American Water Works Association. Manual of Water Supply Practices M23: PVC Pipe– Design and Installation. Denver, CO, pp. 42–52 (2002) 14. Chambers, R.L.S.C.: Keizer city council regular session. City 5, 45 (2019) 15. Sadiq, A.M., Albusoda, B.S.: Experimental and theoretical determination of settlement of shallow footing on liquefiable soil. J. Eng. 26(9), 155–164 (2020) 16. Ali, N.A., Karkush, M.O.: Improvement of unconfined compressive strength of soft clay using microbial calcite precipitates. J. Eng. 27(3), 67–75 (2021) 17. Al-Baidhani, A.F., Al-Taie, A.J.: Shrinkage and strength behavior of highly plastic clay improved by brick dust. J. Eng. 26(5), 95–105 (2020) 18. Obrzud, R.: The hardening soil model: A practical guidebook. Zace Services (2010) 19. Karkush, M.O., Ala, N.A.: Numerical evaluation of foundation of digester tank of sewage treatment plant. Civil Eng. J. 5(5), 996–1006 (2019) 20. Carter, M., Bentley, S.P.: Correlations of soil properties (1991)

Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System Using WaterGEMS Abdulrahman A. Abdulsamad(B) and Khalid Adel Abdulrazzaq Department of Civil Engineering, University of Baghdad, Baghdad, Iraq {abd.alsamad2001m,aleoubaidy}@coeng.uobaghdad.edu.iq

Abstract. To ensure that the distribution system has safe drinking water. It is necessary to know the residual chlorine concentrations at various points in the network. A chlorine photometer device was used to measure twenty points taken every day for a week at a selected time in the distribution system. Both pressures and flows in the network were measured using bourdon gauge and Tuf-2000H Handheld Digital ultrasonic flow meters. WaterGEMS CONNECT Edition update one software was used to simulate the flow in the network. The Baghdad water department provided the data about the network, such as the lengths of pipes, the layout of the network, and pipes diameters. The network calibrated consists of 781 pipes of different lengths and 542 junctions. The analysis results showed that the chlorine concentration ranged between (0.33–1) mg/l in the network, which indicates that the chlorine concentration was within the permissible limits (less than 5mg/l and more than 0.2 mg/l). Keywords: Water quality · WaterGEMS · Chlorine decay

1 Introduction Drinking water disinfection is considered essential for preserving water quality in distribution systems. Because of chlorine’s low cost, stability, and efficiency is the most extensively used disinfectant [1]. As a result of external contaminants entering the supply system through the broken pipes or escaping from the treatment system, Chlorine disinfectants leave a residue to prevent the regrowth of microorganisms in the distribution systems [2]. While residual chlorine flows through the pipe system, it is consumed due to a reaction with chemicals in the water, or it interacts with the material of the water supply system components [3]. However, over-dose chlorine is detrimental to human health, while lower-dose chlorine is ineffective in killing bacteria and viruses [4]. Bulk decay is the reaction between chlorine and dissolved organic and inorganic chemicals in the water. It also interacts with several pipe materials at the biofilm layer and deposits, resulting in a reaction known as wall decay [5]. As a result, chlorine decay in piping systems is generally modeled as a sum of the two responses, bulk decay and wall decay [6]. Chlorine is a powerful oxidant, so it interacts with several chemicals (Organic and inorganic matter) to generate potentially dangerous disinfection by-products (DBPs) in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 52–61, 2023. https://doi.org/10.1007/978-981-19-7358-1_6

Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System

53

treated and distributed water. Some of these (DBPs) are carcinogenic and have adverse effects on growth and health, so it is essential to protect customers from these (DBPs); water supply authorities must regulate chlorine disinfection within upper and lower limits of residual chlorine [7, 8]. This study investigates the ability to use WaterGEMS CONNECT Edition update one software to simulate water network chlorine decay, estimating residual chlorine concentrations at any point in the water network.

2 Watergems Program Description Designed for water distribution systems, WATERGEMS is a hydraulic modeling program that includes enhanced interoperability, GIS model construction, optimization, and asset management features. WATERGEMS is an easy-to-use environment for engineers to study, design, and optimize water distribution systems. Assessments of fire flow and constituent concentration and power consumption, and total cost management can all benefit from its utilization. In addition to enhanced interoperability, GIS model-building, optimization, and asset management features, WATERGEMS is a multiplatform hydraulic and water quality modeling solution for water distribution systems. WATERGEMS is an easy-to-use environment for engineers to study, design, and optimize water distribution systems. Other water systems and infrastructure data may be managed using WATERGEMS [9]. 2.1 Advective Transport in Pipes As the carrier fluid moves down the pipe, a dissolved material will move at the same rate as it reacts (either growing or degrading) simultaneously. Under normal operational conditions, longitudinal dispersion is a minor factor in transit. In other words, there is no admixture of mass between neighboring parcels of water moving through a conduit. The following equation represents advective transport in a pipe [10]: ∂C ∂Ci = −ui + r(Ci ) ∂t ∂x

(1)

where: Ci = concentration in pipe i (mass/volume). ui = velocity in pipe i (length/time). r = rate of reaction (mass/volume/time). The fluid mixing at connections receiving flow through two or more pipes is assumed to be total and instantaneous at these locations. As a result, the concentration of substances in water leaving the junction is equal to the flows sum of the concentrations in the water entering the junction. The equation can be written as the following for a given node k [10]:  jεIk Qj C . + Qk,ext Ck,ext . j.x= Lj  (2) C. = 0 = jεIk Qj + Qk,ext . i.x where

54

A. A. Abdulsamad and K. A. Abdulrazzaq

I = link with flow leaving node k. Ik = set of links with flow into k. Lj = length of link j. Qj = flow in link j0. Qk,ext = external source flow entering the network at node k. Ck,ext = concentration of the external flow entering at node k. C. = the concentration at the start of link i. . j.x=0 C. = the concentration at the end of link i. . j.x= Lj

3 Methodology 3.1 Study Area The AL-Yarmouk region is one of the oldest regions established in Baghdad, Iraq, and is located on the Karkh side (33°17 49.6"N 44°20 19.5"E). It has an area of approximately 4.82 km2 and is divided into six subdivisions (608, 610, 612, 614, 616, and 618) according to the division of the Municipality of Baghdad. The study area is shown in Fig. 1.

Fig. 1. The Al-Yarmouk studied area.

Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System

55

During the previous decade, the French company SOBEA constructed Al-Yarmouk water distribution system in 1984 and still to now day. Because this network has not been updated since its construction, it is constantly subject to cracking due to the high pressures inside the pipes. In addition, the inner walls of the pipes are layered as a result of corrosion and sedimentation within the pipes throughout the years. The Municipality of Baghdad is responsible for this network, which is considered water-carrying pipes. The internal network comprises pipes with diameters ranging from 100 mm to 200 mm, and it has been updated over the years (2009–2012). Al- Mansour Municipality is responsible for this network. The pipe material in all distribution systems is ductile iron. 3.2 Data Collection The study area’s population, a general layout map of the area under study, altitudes of nodal junctions in the water distribution system derived from a topographic map, and water demand at every node in the distribution network are all data utilized in the analyses. The data were obtained from the Baghdad Municipality Department, the Ministry of Planning, and the municipality of Al-Mansour. The network layout is shown in Fig. 2.

Fig. 2. Network layout using WaterGEMS and GIS.

56

A. A. Abdulsamad and K. A. Abdulrazzaq

3.3 Field Work The fieldwork was divided into three stages. Firstly, the measurement of the pressures using bourdon gauge in twenty-point in the network. Secondly, a chlorine photometer device was used to periodically measure the chlorine concentrations in twenty points for one week. Thirdly, the flow was measured at the five-point, which considered the water sources to the network by using the Tuf-2000H Handheld Digital ultrasonic flow meters. The data obtained from the fieldwork are shown in Table 1. Figure 3 shows the measuring points’ location and the main pipe in the network. Table 1. The field measurements data (* : No data were measured). Pressure (m H2 O)

Chlorine concentration (mg/l)

Flow (m3 /h)

1

17.2

1.3

1543

2

19.6

1.1

309

3

20.3

1

894

4

21.2

1.3

308

5

22.8

1.6

1697

6

18

0.78

*

7

17.5

0.68

*

8

19

0.97

*

9

11.5

0.76

*

10

17.2

0.7

*

11

15

0.67

*

12

14.1

0.6

*

13

15.9

1

*

14

9.7

0.88

*

15

16

0.64

*

16

13.9

0.36

*

17

14.1

0.4

*

18

8.9

0.66

*

19

7.2

0.47

*

20

7.1

0.45

*

Point

Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System

57

Fig. 3. Locations of measuring points.

3.4 Model Calibration The calibration process used field measurements to modify the network model to reflect actual system behavior [11]. The bulk (kb ) and wall decay (kw ) were determined and set to be −1.2 day−1 and −0.4 m/day, respectively [12]. The calibration process was based on measuring the pressures in the network and determining the inflow and outflow from the network. The Hazen William was set to be 120 for the main pipes because they are old pipes, while the lateral pipes with coefficient value of 130 As it is an updated network. The analysis time was based on the steady-state of the average daily consumption.

4 Results and Discussion Chlorine concentrations were measured at twenty points, and then the standard deviation and coefficient of variation for each point have calculated. The results are shown in Table 2. Table 2. The standard deviation and coefficient of variance for the twenty-point. Point

1

2

3

4

5

6

7

8

9

10

σ (mg/l) 0.3105 0.2607 0.1549 0.1673 0.0689 0.1264 0.087

0.1472 0.0636 0.0854

C.V

0.238

0.237

0.154

0.128

0.043

0.158

0.124

0.147

0.079

0.122

point

11

12

13

14

15

16

17

18

19

20

σ (mg/l) 0.0471 0.0328 0.1662 0.2065 0.0253 0.0494 0.0414 0.0141 0.0894 0.0469 C.V

0.067

0.054

0.151

0.229

0.036

0.123

0.082

0.02

0.178

0.093

58

A. A. Abdulsamad and K. A. Abdulrazzaq

The standard deviation analysis shows that the data points tend to be incredibly near to the mean (since the standard deviation is closer to zero than to unity) and therefore concludes that chlorine concentrations measured in the field are centralized for all sampling locations. The data were obtained from simulation are shown in Fig. 4.

Fig. 4. Residual chlorine analysis

The analysis model results showed that the minimum concentration reached 0.33 mg/l in the lateral network, and the maximum value was 1 mg/l. The Chlorine concentrations with value (0.7–0.8) mg/l were recorded at 18% of the whole network, consisting of 781 pipes of different lengths, and It is the largest percentage in the network. Figure 5 shows the percentage distribution of chlorine concentrations mg/l in pipes. The comparison between the data obtained in the field and the simulated program showed a Great similarity in results for the sampling points, as shown in Table 3. Figures 6 and 7 compare the obtained and predicted values for chlorine concentrations and pressures in the network.

Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System

59

Fig. 5. The chlorine concentrations percentage in pipes.

Table 3. Obtained and predicted value of studied parameters. Point

Chlorine obtained (mg/l)

Chlorine predicted (mg/l)

Error

Pressure obtained (m H2 O)

Pressure predicted (m H2 O)

Error

6

0.8

0.84

0.047

18

8.16

0.032

7

0.7

0.68

0.028

17.5

18

0.027

8

1

0.96

0.041

19

19.37

0.019

9

0.8

0.76

0.05

11.5

11.17

0.028

10

0.7

0.78

0.102

17.2

16.62

0.033

11

0.7

0.69

0.014

15

15.11

0.007

12

0.6

0.65

0.076

14.1

14.51

0.028

13

1.1

1.03

0.063

15.9

16.33

0.026

14

0.9

0.85

0.055

9.7

9.84

0.014

15

0.7

0.7

0

16

16.12

0.007

16

0.4

0.39

0.025

13.9

14.28

0.026

17

0.5

0.51

0.019

14.1

14.35

0.017

18

0.7

0.75

0.667

8.9

9.33

0.046

19

0.5

0.53

0.056

7.2

7.88

0.086

20

0.5

0.51

0.019

7.1

7.74

0.082

60

A. A. Abdulsamad and K. A. Abdulrazzaq 1.2 1.1 Chlorine concentration (mg/l)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

obtained

predicted

0 6

7

8

9

10

11

12 13 14 Point number

15

16

17

18

19

20

Fig. 6. A comparison of observed and simulated data for the chlorine concentrations. 20 18 16

Pressure(m H2O)

14 12 10 8 6 4 2

obtained

predicted

0 6

7

8

9

10

11

12 13 14 Point number

15

16

17

18

19

20

Fig. 7. A comparison of observed and simulated data for the pressures.

5 Conclusions This paper simulated and modeled residual chlorine concentration using the WaterGEMS program. The chlorine concentrations that were measured throughout the study period were stable, which indicates that the water treatment plants have a regular dosing system, as well as the water quality, is stable. The study results show that chlorine concentrations in the samples taken from the network ranged from (0.33 to 1) mg/l, which indicates a good water quality concerning disinfectant by chlorine and with Standard Specifications. The results also showed that the network works well in the winter season, during

Simulation of Residual Chlorine in Al-Yarmouk Drinking Water System

61

the average daily consumption. It is possible to conclude that the (WaterGEMS) software is reliable in simulating water quality in water distribution systems. However, there are some differences between the predicted and observed data regarding chlorine concentrations and pressures.

References 1. Kim, H., Kim, S., Koo, J.: Modelling chlorine decay in a pilot scale water distribution system subjected to transient. Procedia Eng. 119, 370–378 (2015) 2. Gang, D.C., Clevenger, T.E., Banerji, S.K.: Modeling chlorine decay in surface water. J. Environ. Inf. 1(1), 21–27 (2003) 3. Meier, R.W., Barkdoll, B.D.: Sampling design for network model calibration using genetic algorithms. J. Water Resour. Plan. Manag. 126(4), 245–250 (2000) 4. S Shihab, M., I Al-Hyaly, A., H Mohammad, M.: Simulation of chlorine concentrations in Mosul University’s distribution network using EPANET program. Al-Rafidain Eng. J. (AREJ) 17(6), 28-41 (2009) 5. Clark, R.M., et al.: Chlorine fate and transport in distribution systems: Experimental and modeling studies. J. Am. Water Works Ass. 102(5), 144–155 (2010) 6. Monteiro, L., Carneiro, J., Covas, D.I.: Modelling chlorine wall decay in a full-scale water supply system. Urban Water J. 17(8), 754–762 (2020) 7. Abdullah, M.P., Yee, L.F., Ata, S., Abdullah, A., Ishak, B., Abidin, K.N.Z.: The study of interrelationship between raw water quality parameters, chlorine demand and the formation of disinfection by-products. Phys. Chem. Earth Parts A/B/C 34(13–16), 806–811 (2009) 8. Wei, J., Ye, B., Wang, W., Yang, L., Tao, J., Hang, Z.: Spatial and temporal evaluations of disinfection by-products in drinking water distribution systems in Beijing. China. Sci. Total Environ. 408(20), 4600–4606 (2010) 9. Mehta, D.J., Yadav, V., Waikhom, S.I., Prajapati, K.: Design of optimal water distribution systems using WATERGEMS: a case study of Surat city. J. Glob. Anal. 2(4), 90–93 (2017) 10. www.Bently HAMMER CONNECT Edition Help 11. Mostafa, N.G .,Matta, M.E., Abdel-Halim, H.: Simulation of chlorine decay in water distribution networks using WATERCAD – case study. J. Eng. Appl. Sci. 60(1), 25–42 (2013) 12. Abdulsamad, A.A., Abdulrazzaq, K.A.: Assessment of the wall-decay coefficient for al-yarmouk region water distribution network using WaterGEMS. In: AIP Conference Proceedings. Acceptable research for publication (2022)

Optimization and Modelling of Electrochemical Removal of Nitrate from Solutions Muhammed A. Shallal1 , Sarah A. Ali2 , Haneen H. Hamzaa2 , Salam M. Naser2 , Maliheh Arab3 , and Raad Hashim4(B) 1 Educational Directorate Thi-Qar, Thi-Qar, Iraq 2 Al-Mustaqbal University College, Babylon, Hilla, Iraq 3 Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad,

Razavi Khorasan, Iran [email protected] 4 Civil Engineering Department, College of Engineering, University of Babylon, Babylon, Iraq [email protected]

Abstract. This study uses the electrocoagulation (EC) to remove nitrate from water. Box-Behnken Design method (BBD) was used to evaluate nitrate removal from synthetic water by the EC. BBD was firstly applied to optimise the effects of three parameters; pH of synthetic water (pHSW) (6–10), electrolysing time (ET) (20–80 min), and current density (CD) (1–3 mA/cm2 ) on nitrate removal. The EC cell is utilised aluminium electrodes to carry out the electrolysing process. The findings of this study generally showed high CD, high ET, and alkaline pHSW are favourable to nitrate removal. It was found the best removal of nitrate (93.2%) was obtained at pHSW, ET and CD of 8, 80 min and 3 mA/cm2 , respectively. The results of the statistical analysis showed the removal of nitrate by aluminium electrodes can be accurately forecasted using the BBD methodology, where the R2 of the created model was 0.949, and the maximum difference between the forecasted and actual removal of nitrate was 5.5%. It must be noted the created model is applicable for the studied ranges of the aforementioned factors. Keywords: Nitrate · Electrocoagulation · Removal · BBD

1 Introduction Nitrate in water generally results from the degrading of organic matters, which is the natural path of nitrate pollution and usually produces low concentrations in freshwaters. However, the nitrate concentration increased during the last two decades because of the discharging of industrial wastewaters (anthropogenic sources) that are very rich in nitrogen-based pollutants [1, 2]. Examples of anthropogenic sources are the applications of nitrogen-based fertilisers in farms, animal wastes, and domestic and industrial wastewaters [3, 4]. The nitrates are not responsible for health problems, such as blue baby syndrome, but it is also responsible for a wide range of environmental problems [5]. For example, high concentrations of nitrates or phosphates in water results in the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 62–72, 2023. https://doi.org/10.1007/978-981-19-7358-1_7

Optimization and Modelling of Electrochemical Removal

63

eutrophication of freshwater, seriously affecting water quality [6, 7] because this phenomenon depletes the dissolved oxygen rapidly through the decomposition of organic matters in water [8]. Furthermore, the decline in the availability of fresh water in rivers and lakes due to climate change maximised the concentrations of nitrates and other pollutants in freshwater [9, 10]. Apart from the problems above, nitrates are not toxic at reasonable concentrations (less than 50 mg/L for adults and 15 mg/L for infants) [11]; however, biologically reducing nitrates to nitrites is another source of hazards because the latter has many harmful impacts on humans’ health, including but not limited to the methemoglobinemia in infants and pregnant women and different types of cancers [12, 13]. Based on the mentioned reasons, the World Health Organisation (WHO) limited the nitrate concentration in drinking water to 50 mg/L [14, 15]. Many methods were applied to remove nitrate from water to minimise nitrate’s environmental and health hazards. For example, membrane technology, chemical treatment, adsorption and biological digestions [16–18]. But, the majority of used methods nowadays face many limitations. For example, the membranes can remove all pollutants from water making the treated water pure and safe. Gao, et al. [19] used an ultrafiltration membrane to remove nitrate and phosphate from solutions. The ultrafiltration membrane was made from Zr(OH)4 and quaternary ammonium and poly(vinylidene-fluoride). The results showed the ultrafiltration membrane separated nearly 10 and 16 mg/g of nitrate and phosphate from water. On the other hand, the researchers found membranes are expensive and technically completed [20, 21]. Similarly, the chemical removal of nitrates is effective and remove whatever concentration of nitrate in water or wastewater. Aghapour, et al. [22] removed nitrates using a chemical method that relies on the adding of alum and FeCl3 . The results showed the adding 4 mg/L of FeCl3 to the solution minimized the nitrate from 70 mg/L to less than 50 mg/L, but alum did no show high efficiency in the removal of nitrate. On the other hand, the chemical methods require chemical additives that have environmental impacts and also, the sludge of this method is toxic [23, 24]. The biological removal of nitrate was used in the industry and in researches [25, 26], for example Sposob, et al. [27] studied the removal of nitrate and sulphide using a biological reactor that was running for 4 months at N/S ratio of 0.35 and steady sulphur loading rate of 400 mg/m3 .day. The results showed the biological treatment (reactor) removed 98% of the sulphide and 100% of nitrate when the temperature of water was 25 °C. However, the biological treatment methods are slow and need a disinfection stage to kill the discharged bacteria [23]. The quick literature review presented in this study showed advantages and disadvantages of the nitrate removal routs and methods around the world. The results of the literature review showed the conventional methods are expensive, need chemicals or slow. Thus, new efforts were made to develop effective and affordable methods to remove nitrate. One suggested method is electrocoagulation (EC), which depends on electricity to melt metallic electrodes forming coagulants to remove nitrates and other pollutants from water or wastewater [28–30]. The EC method is attractive because of its many merits, such as its safe use [31], low cost [32] and high efficiency [33]. Thus, the EC method is used for the removal of many pollutants, including but not limited to heavy metals [34, 35], fluoride [36, 37], organic matter [38], and biological pollutants [39, 40]. Therefore, the goal of this study is investigating the effect of time, density, pH

64

M. A. Shallal et al.

on removal of nitrate from synthetic water. The Box-Behnken Design method (BBD) was used in this study to optimise the effects of mentioned three parameters.

2 Materials and Methods 2.1 The EC Cell The used EC cell in this work is shown in Fig. 1, which is made from a glass container with a total volume of 2000 cm3 . The container is provided with four aluminium electrodes. The dimensions of the electrode were 20 × 10 cm; the electrodes were partially immersed in water. The container was placed on the top of a magnetic stirrer, which is used to mix water during the treatment process (at a speed of 150 rpm) [41]. The electrodes were connected to a DC power source to provide the needed electric current to start the electrolysing process. Two electrodes were anodes, and another two electrodes were cathodes. Aluminium electrodes were used here because aluminium is affordable and provides the required coagulants to remove nitrate [42].

Fig. 1. The use EC cell in this study.

2.2 The Synthetic Solution and Tests The nitrates solution was made by mixing the required amount of KNO3 , purchased from Sigma Aldrich in Germany, in deionised water to produce 100 mg/L of nitrates. The solution was immediately transferred into the glass container of the EC cell and treated according to the required design of the experiments, including the pHSW, CD and ET. The pHSW is changed using hydrochloric acid or sodium hydroxide. The EC experiments were predesigned using the BBD methodology, where the effects of pHSW (from 6 to 10), CD (from 1 to 3 mA/cm2 ) and ET (from 20 to 80 min) on the removal of nitrate from water were optimised using the BBD. The optimisation was run using Minitab software. The lowest, average and highest values of the mentioned factors are listed in Table 1.

Optimization and Modelling of Electrochemical Removal

65

Table 1. The lowest, average and highest values of the studied factors. Factor

Lowest (−1)

Average (0)

Highest (+1)

pHSW

6

8

10

CD (mA/cm2 )

1

2

3

ET (minutes)

20

50

80

The experiments were run by applying the required CD (from 1 to 3 mA/cm2 ), and the removal of nitrate was measured by taking samples from the EC cell periodically (5 min periods). First, the samples were filtered through a 0.45µm filter to separate any floc as they affect the spectrophotometric measurements. The clean water sample was then tested using a Hach Spectrophotometer (DR3900). The efficiency of nitrates removal was calculated using Eq. 1 Exp.Re% =

Initial nitrate concentration − Measured nitrate concentration × 100 (1) Initial nitrate concentration

The distance between the electrodes was kept constant at 5 mm, and the experiments were run at room temperature (20 ± 1 °C). The nitrate concentration was measured according to the standard spectrophotometric method, the details of this method are listed a previous study [2].

3 Results 3.1 Experimental Results The results of the design experiments are shown in Table 2. Table 2. Coded lowest, average and highest values of the studied factors. pHSW

ET

CD

−1

0

1

0

−1

1

−1

0

−1

−1

−1

0

0

0

0

0

−1

−1 (continued)

66

M. A. Shallal et al. Table 2. (continued) pHSW

ET

CD

0

1

1

1

0

1

1

1

0

1

−1

0

0

1

−1

0

0

0

1

0

−1

0

0

0

−1

1

0

The coded values have been changed to actual experimental values to facilitate the modelling of the results later. The actual values of the studied factors are shown in Table 3. Table 3. Experimental lowest, average and highest values of the studied factors. pHSW

ET

CD

6

50

3

8

20

3

6

50

1

6

20

2

8

50

2

8

20

1

8

80

3

10

50

3

10

80

2

10

20

2

8

80

1

8

50

2

10

50

1

8

50

2

6

80

2

The required experiments to determine the removal of nitrate by the EC cell were run by applying the needed CD via the DC power source for the desired ET and pHSW.

Optimization and Modelling of Electrochemical Removal

67

The summary of the results is shown in Table 4, which shows the increase in pHSW, CD and ET is in favour of nitrate removal by the EC cell. Generally, the best nitrates removal was 93.2% which was achieved at pHSW, ET and CD of 8, 3 mA/cm2 and 80 min, respectively. At the same time, the minimum removal of nitrates (49.8%) occurred when the pHSW, ET and CD were 6, 20 min, and 2 mA/cm2 . The results confirmed the positive effects of the increase in the pHSW, ET and CD on nitrate removal using aluminium electrodes. These results agree with the results of many previous studies, such as the studies of Abdel-Aziz et al. [43], Benekos et al. [44], and Abdulkhadher and Jaeel [45]. Table 4. Experimental removal of nitrates by the EC cell. pHSW

ET

CD

Exp.Re%

6

50

3

83.5

8

20

3

60.5

6

50

1

78.8

6

20

2

49.8

8

50

2

86.4

8

20

1

59.4

8

80

3

93.2

10

50

3

84.7

10

80

2

90.1

10

20

2

54.3

8

80

1

74.8

8

50

2

85.4

10

50

1

63.5

8

50

2

88.9

6

80

2

86.5

3.2 Modelling of the Results A vast number of previous studies [46–48] focused on modelling the experimental results because such models help to understand the effects of the individual factors on the removal of pollutants. Thus, the BBD methodology was used in this study to create a model was created to forecast the removal of nitrate from water using the EC method (with aluminium electrodes). This model helps to avoid repeat experiments in the future, which saves time and cost of experiments. The model, which is shown in Eq. 2, forecasts the effects of the pHSW, ET and CD with the studied ranges of these factors. Exp.Re% = −35.3 + 17.8pHSW + 1.487ET − 3.1CD − 1.384

68

M. A. Shallal et al. pHSW2 − 0.01243ET2 − 3.74CD2 − 0.0038pHSW ∗ ET + 2.06pHSW ∗ CD + 0.1442ET ∗ CD

(2) First, the R2 value of the created model was calculated to investigate its accuracy. Generally, it is recommended to consider models with R2 value close to 1.0 because such models can forecast the removal efficiency accurately [49, 50]. The results of this study showed the R2 value of the created model was 0.949, which is a good value and within the obtained values in the previous studies, such as the study of Abd Aljaleel and and Alwan [51], Khan et al. [52], and Altufaily and Mohammad [53]. Then, the created model, in Eq. 2, was used to forecast the removal of nitrates using the designed experiments in Table 4. The forecasted and actual results were compared to have an idea about the agreement between the results and the reliability of the model. The results are shown in Table 5. Table 5. Experimental versus forecasted removal of nitrates. No

pHSW

ET

CD

Exp. Re%

Forecasted Re%

1

6

50

3

83.5

79.56

2

8

20

3

60.5

57.82

3

6

50

1

78.8

76.54

4

6

20

2

49.8

55.32

5

8

50

2

86.4

86.50

6

8

20

1

59.4

55.21

7

8

80

3

93.2

96.59

8

10

50

3

84.7

86.15

9

10

80

2

90.1

83.78

10

10

20

2

54.3

54.12

11

8

80

1

74.8

76.68

12

8

50

2

85.4

86.50

13

10

50

1

63.5

66.65

14

8

50

2

88.9

86.50

15

6

80

2

86.5

85.89

The forecasted results are very close to the actual results (with a maximum difference of 5.5%). Thus, the created model could be a good tool to forecast the removal of nitrate using the aluminium electrodes in the EC methods. This model is applicable for the following rages of the studied factors: • pHSW = 6 to 10 • CD = 1 to 3 mA/cm2 • ET = 20 to 80 min

Optimization and Modelling of Electrochemical Removal

69

Thus, it is recommended to expand this investigation in the future by including a wider range of these factors. In summary, the obtained results showed the EC method can remove nitrate with a good efficiency (93.2%), which agrees with the results of previous studies, such as the results of Acharya, et al. [54] and Tounsi et al. [55].

4 Conclusions The present paper dealt with the use of EC cells, which are based on aluminium electrodes, to remove nitrates from synthetic water. The experimental work in this study focused on the effects of three factors, which are pHSW, ET and CD, on the removal of nitrate, while the statistical analysis dealt with the modelling of the effects of the aforementioned factors using the BBD methodology. The results generally showed increasing pHSW, ET and CD helped to achieve high removal of the nitrate (93.2%). The results of the statistical analysis showed the removal of nitrate by aluminium electrodes can be accurately forecasted using the BBD methodology, where the R2 of the created model was 0.949, and the maximum difference between the forecasted and actual removal of nitrate was 5.5%. It must be noted the created model is applicable for the studied ranges of the factors mentioned above. Thus, it is recommended to expand this investigation in the future by including a more comprehensive range of these factors or including new factors; for example, the effects of water temperature could be investigated.

References 1. Ano, J., Briton, B.G.H., Kouassi, K.E., Adouby, K.: Nitrate removal by electrocoagulation process using experimental design methodology: a techno-economic optimization. J. Environ. Chem. Eng. 8(5), 104292 (2020) 2. Hashim, K.S., Shaw, A., Al Khaddar, R., Pedrola, M.O., Phipps, D.: Energy efficient electrocoagulation using a new flow column reactor to remove nitrate from drinking water experimental, statistical, and economic approach. J. Environ. Manage. 196, 224–233 (2017) 3. Nazlabadi, E., Moghaddam, M.R.A., Karamati-Niaragh, E.: Simultaneous removal of nitrate and nitrite using electrocoagulation/floatation (ECF): a new multi-response optimization approach. J. Environ. Manage. 250, 109489 (2019) 4. Acharya, S., Sharma, S., Chauhan, G., Shree, D.: Statistical optimization of electrocoagulation process for removal of nitrates using response surface methodology. Indian Chem. Eng. 60(3), 269–284 (2018) 5. Ghanim, A.: Application of response surface methodology to optimize nitrate removal from wastewater by electrocoagulation. Int. J. Sci. Eng. Res. 4(10), 1410–1416 (2013) 6. Hashim, K.S., et al.: Phosphate removal from water using bottom ash: adsorption performance, coexisting anions and modelling studies. Water Sci. Technol. 83(1), 1–17 (2020) 7. Hashim, K., Al-Rifaie, J.K., Aljaaf, A.J., Idowu, I., Amoako-Attah, J., Nikitas, G.: RSM (Response Surface Methodology) modelling of inter-electrodes spacing effects on phosphate removal. In: Proceedings of the 14th International Conference on Developments in eSystems Engineering, pp. 586–589 (2021) 8. Amarine, M., Lekhlif, B., Sinan, M., El Rharras, A., Echaabi, J.: Treatment of nitrate-rich groundwater using electrocoagulation with aluminum anodes. Groundw. Sustain. Dev. 11, 100371 (2020)

70

M. A. Shallal et al.

9. Ortmeyer, F., Mas-Pla, J., Wohnlich, S., Banning, A.: Forecasting nitrate evolution in an alluvial aquifer under distinct environmental and climate change scenarios (Lower Rhine Embayment, Germany). Sci. Total Environ. 768, 144463 (2021) 10. Salah, Z., Hashim, K., Ethaib, S., Al-Bdairi, N.S.S., Al-Bugharbee, H., Gharghan, S.K.: A novel methodology to predict monthly municipal water demand based on weather variables scenario. J. King Saud Univ.-Eng. Sci. 32(7), 1–18 (2020) 11. Majlesi, M., Mohseny, S.M., Sardar, M., Golmohammadi, S., Sheikhmohammadi, A.: Improvement of aqueous nitrate removal by using continuous electrocoagulation/electroflotation unit with vertical monopolar electrodes. Sust. Environ. Res. 26(6), 287–290 (2016) 12. Huang, C.-P., Wang, H.-W., Chiu, P.-C.: Nitrate reduction by metallic iron. Water Res. 32(8), 2257–2264 (1998) 13. Della Rocca, C., Belgiorno, V., Meriç, S.: Overview of in-situ applicable nitrate removal processes. Desalination 204(1–3), 46–62 (2007) 14. World Health Orgnisation (WHO): Guidelines for drinking-water quality World Health Organization (2004) 15. Hossini, H., Rezaee, A.: Optimization of nitrate reduction by electrocoagulation using response surface methodology. Health Scope 3(3), 1–10 (2014) 16. Rezvani, F., Sarrafzadeh, M.-H., Ebrahimi, S., Oh, H.-M.: Nitrate removal from drinking water with a focus on biological methods: a review. Environ. Sci. Pollut. Res. 26(2), 1124–1141 (2017). https://doi.org/10.1007/s11356-017-9185-0 17. Karaghool, H.A.K., Hashim, K., Kot, P., Muradov, M.: Preliminary studies of methylene blue remotion from aqueous solutions by ocimum basilicum. Environments 9(2), 1–17 (2022) 18. Al-Saati, N.H., et al.: Statistical modelling of turbidity removal applied to non-toxic natural coagulants in water treatment: a case study. Desalin. Water Treat. 150, 406–441 19. Gao, Q., Wang, C.-Z., Liu, S., Hanigan, D., Liu, S.-T., Zhao, H.-Z.: Ultrafiltration membrane microreactor (MMR) for simultaneous removal of nitrate and phosphate from water. Chem. Eng. J. 355, 238–246 (2019) 20. Uzun, H.I., Debik, E.: Economical approach to nitrate removal via membrane capacitive deionization. Sep. Purif. Technol. 209, 776–781 (2019) 21. Hashim, K.S., Shaw, A., AlKhaddar, R., Kot, P., Al-Shamma’a, A.: Water purification from metal ions in the presence of organic matter using electromagnetic radiation-assisted treatment. J. Clean. Prod. 280(2), 1–17 (2021) 22. Aghapour, A.A., Nemati, S., Mohammadi, A., Nourmoradi, H., Karimzadeh, S.: Nitrate removal from water using alum and ferric chloride: a comparative study of alum and ferric chloride efficiency. Environ. Health Eng. Manage. J. 3(2), 69–73 (2016) 23. Jing, G., Ren, S., Pooley, S., Sun, W., Kowalczuk, P.B., Gao, Z.: Electrocoagulation for industrial wastewater treatment: an updated review. Environ. Sci. Water Res. Technol. 7(7), 1177–1196 (2021) 24. Mamelkina, M., Tuunila, R., Sillänpää, M., Häkkinen, A.: Systematic study on sulfate removal from mining waters by electrocoagulation. Sep. Purif. Technol. 216, 43–50 (2019) 25. Mohseni-Bandpi, A., Elliott, D.J., Zazouli, M.A.: Biological nitrate removal processes from drinking water supply-a review. J. Environ. Health Sci. Eng. 11(1), 1–11 (2013) 26. Pang, Y., Wang, J.: Various electron donors for biological nitrate removal: a review. Sci. Total Environ. 794, 148699 (2021) 27. Sposob, M., Bakke, R., Dinamarca, C.: Metabolic divergence in simultaneous biological removal of nitrate and sulfide for elemental sulfur production under temperature stress. Biores. Technol. 233, 209–215 (2017) 28. Amarine, M., Lekhlif, B., Echaabi, J.: Nitrate removal from groundwater in Casablanca region (Morocco) by electrocoagulation. Groundw. Sustain. Dev. 11, 100452 (2020)

Optimization and Modelling of Electrochemical Removal

71

29. Karabulut, Y., Atasoy, A.D., Can, O.T., Yesilnacar, M.I.: Electrocoagulation for nitrate removal in groundwater of intensive agricultural region: a case study of Harran plain, Turkey. Environ. Earth Sci. 80(5), 1–9 (2021) 30. Arab, M., Faramarz, M.G., Hashim, K.: Applications of computational and statistical models for optimizing the electrochemical removal of cephalexin antibiotic from water. Water 14(3), 344–359 (2022) 31. Hashim, K.S., et al.: Escherichia coli inactivation using a hybrid ultrasonic–electrocoagulation reactor. Chemosphere 247, 125868–125875 (2020) 32. Mena, V., Betancor-Abreu, A., González, S., Delgado, S., Souto, R., Santana, J.: Fluoride removal from natural volcanic underground water by an electrocoagulation process: parametric and cost evaluations. J. Environ. Manage. 246, 472–483 (2019) 33. Abdulhadi, B., Kot, P., Hashim, K., Shaw, A., Muradov, M., Al-Khaddar, R.: Continuousflow electrocoagulation (EC) process for iron removal from water: experimental, statistical and economic study. Sci. Total Environ. 760(2), 1–16 (2021) 34. Bazrafshan, E., Mohammadi, L., Ansari-Moghaddam, A., Mahvi, A.H.: Heavy metals removal from aqueous environments by electrocoagulation process–a systematic review. J. Environ. Health Sci. Eng. 13(1), 1–16 (2015) 35. Al-Qodah, Z., Al-Shannag, M.: Heavy metal ions removal from wastewater using electrocoagulation processes: a comprehensive review. Separation Sci. Technol. 52(17), 2649–2676 (2017) 36. Castaneda, L.F., Rodriguez, J.F., Nava, J.L.: Electrocoagulation as an affordable technology for decontamination of drinking water containing fluoride: a critical review. Chem. Eng. J. 413, 127529 (2021) 37. Grich, N.B., Attour, A., Mostefa, M.L.P., Guesmi, S., Tlili, M., Lapicque, F.: Fluoride removal from water by electrocoagulation: effect of the type of water and the experimental parameters. Electrochim. Acta 316, 257–265 (2019) 38. Kumari, S., Kumar, R.N.: River water treatment using electrocoagulation for removal of acetaminophen and natural organic matter. Chemosphere 273, 128571 (2021) 39. Al-Qodah, Z., Al-Qudah, Y., Assirey, E.: Combined biological wastewater treatment with electrocoagulation as a post-polishing process: a review. Separation Sci. Technol. 55(13), 2334–2352 (2020) 40. Bhagawan, D., et al.: Industrial solid waste landfill leachate treatment using electrocoagulation and biological methods. Desalin Water Treat 68, 137–142 (2017) 41. Nwabanne, J.T., Obi, C.C.: Abattoir wastewater treatment by electrocoagulation using iron electrodes. Der chemica sinica 8(2), 254–260 (2017) 42. Elazzouzi, M., Haboubi, K., Elyoubi, M.: Electrocoagulation flocculation as a low-cost process for pollutants removal from urban wastewater. Chem. Eng. Res. Des. 117, 614–626 (2017) 43. Abdel-Aziz, M., El-Ashtoukhy, E.Z., Zoromba, M.S., Bassyouni, M., Sedahmed, G.: Removal of nitrates from water by electrocoagulation using a cell with horizontally oriented Al serpentine tube anode. J. Ind. Eng. Chem. 82, 105-112 (2020) 44. Benekos, A.K., et al.: Nitrate removal from groundwater using a batch and continuous flow hybrid Fe-electrocoagulation and electrooxidation system. J. Environ. Manage. 297, 113387 (2021) 45. Abdulkhadher, R.K., Jaeel, A.J.: The use of electrocoagulation to remove fluoride, nitrates and phosphorous from water. In: Proceedings of the IOP Conference Series: Earth and Environmental Science, 877, p. 012021 (2021) 46. Moradi, M., Ashrafizadeh, S.N.: Nitrate removal from tapwater by means of electrocoagulation-flotation process. Sep. Sci. Technol. 55(8), 1577–1587 (2020)

72

M. A. Shallal et al.

47. Uzoh, C.F., Nwabanne, J.T., Ozofor, I.H.: Electrocoagulation of Pb2+, Co2+, and Mn2+ from simulated wastewater: an algorithmic optimization using hybrid RSM–GA–PSO. Environ. Prog. Sustainable Energy 39(1), 13301 (2020) 48. Acharya, S., Sharma, S., Khandegar, V.: Nitrate removal from groundwater by electrocoagulation: process optimization through response surface method. J. Hazardous Toxic Radioactive Waste 25(3), 04021014 (2021) 49. Patel, P., Gupta, S., Mondal, P.: Electrocoagulation process for greywater treatment: statistical modeling and optimization, cost analysis, and sludge management. Cost Anal. Sludge Manage. Separation Purification Technol. 1(2), 121327 (2022) 50. Abdulredh, A., Kadhim, N.R., Abdulredh, M., Alquzweeni, S.S., Mubarak, H., Georgantzia, E.: Modelling of electrochemical removal of nitrate from water. In: Proceedings of the 14th International Conference on Developments in eSystems Engineering (DeSE), 582–585 (2021) 51. Abd Aljaleeland, M., Alwan, H.H.: Examine of the most influential variables in the process of removing copper from simulated wastewater by using an electrocoagulation reactor. Design Engineering, 10469–10484 (2021) 52. Khan, N.A., Ahmed, S., Farooqi, I.H., Ali, I.: Performance Evaluation and Parameter Optimization of Moving Bed Biofilm Reactor (MBBR) along with Electrocoagulation for Removal of Azithromycin. Lett. Appl. NanoBioScience 11(2), 3329–3343 (2021) 53. Altufaily, M.A., Mohammad, A.M.: Modeling and improvement of aqueous pollutants removal using electrocoagulation technique. J. Eng. Sci. Technol. 15(4), 2662–2678 (2020) 54. Acharya, S., Khandegar, V., Sharma, S.K., Kumar, A.: Nitrate removal from synthetic and real groundwater by electrocoagulation: effect of operating parameters and electrolytes. Int. J. Environ. Anal. Chem. 2(1), 1–19 (2022) 55. Tounsi, H., et al.: Electrocoagulation in the dual application on the simultaneous removal of fluoride and nitrate anions through respective adsorption/reduction processes and modelling of continuous process. J. Water Process Eng. 46, 102584 (2022)

Determination of Potential Sites for Landfill Using Geographic Information Systems Technology in Shatra City Mukhalad N. Mohammed1(B) and Faisel G. Mohammed2 1 Surveying Engineering Department, University of Baghdad, Baghdad, Iraq

[email protected]

2 Remote Sensing Sciences Department, University of Baghdad, Baghdad, Iraq

[email protected]

Abstract. The city of Shatra, located in southern Iraq, is one of the main cities in Dhi Qar, with 1744 km2 . The landfill site must be chosen carefully because of the environmental impacts resulting from the sites of landfills, which affect the health and economic environment of the area. GIS integration is used with a multicriteria decision-making, so fifteen criteria were used (Groundwater depth, urban centers, rivers, villages, school, roads, elevation, slope, power plant, water surface, land use, gas pipelines, power lines, oil pipelines, wells), and two methods from multi-criteria decision-making Analytic Hierarchy Process (AHP) and Ratio Scale Weighting (RSW) were used to obtain a suitable indicator map by weighted linear combination (WLC) method that was used to derive weights by using the pairwise comparison method. After comparing the results obtained by combining the two maps utilizing geographic information systems to determine the percentage of pixels for conformity and non-conformity, two suitable sites were selected for the sanitary landfill with an area of A(1.830 km2 ) B(9.217 km2 ) respectively, These selected sites can absorb waste for several years, This research provides an approach to determine the best sites and great support for decision-makers in choosing suitable sites for sanitary landfills. Keywords: GIS · Landfill · Spatial analysis · MCDM

1 Introduction The high rate of population growth, industrial and commercial activity in urban areas, and improving living standards are among the main reasons for the increase in waste quantities, municipalities, and government funding [1]. Modern and effective techniques have been used to dispose of these wastes. The most important techniques are landfills, treatment, Biological, and recycling [2]. Many factors must be taken into consideration in locating a sanitary landfill, where the combination of these factors in the process of locating a landfill is a complex process. These factors include economic and environmental factors. Social and economic factors include costs associated with the development of sanitary landfill sites [3]. Environmental factors must also be considered because © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 73–85, 2023. https://doi.org/10.1007/978-981-19-7358-1_8

74

M. N. Mohammed and F. G. Mohammed

landfill operations may harm a healthy landfill site [4, 5]. There is no sanitary landfill site in Shatra that is subject to health and environmental standards. The amount of waste generated in the city of Shatra in 2018 was 161.331 tons of solid waste, i.e., a generation rate of 0.54 kg/person/day. The GIS method has been used, which has a high capacity to manage large amounts of spatial data. It can also consider several factors from a range of sources [6–8] where the geographic information system has been mixed with multi-decision analysis methods such as AHP, RSW to decide to choose the most appropriate sanitary landfill site [9]. Furthermore, the use of these techniques reduces cost and time and provides digital information for long-term monitoring of sites [10]. This study was for Shatra, located in the north of Dhi Qar Governorate, and fifteen criteria were used, where the criteria were arranged from important to least important, The analytic hierarchy process is one of the most common methods for (MCDM) [11]. It can solve complex decision-making problems in several areas. This provides a suitable language for managing qualitative and inaccurate criteria with the analysis of quantitative factors. (RSW) was used to determine the weights of the criteria by giving a relative value to each criterion by decision-makers based on the opinion of experts and previous studies. There are many previous studies on determining the optimal site for sanitary landfills. A study [12] focused on choosing the optimal site for sanitary landfills using MCDM systems by merging layers using thirteen criteria, and WLC extracted a final map. Seven sites suitable for sanitary landfills were obtained in the governorate Sulaymaniyah. In another study [13], GIS was used to determine the most suitable sites for sanitary landfills using AHP with GIS and obtain a final map containing suitable places for sanitary landfills up to 2040 in Al-Diwaniyah Governorate. Also, Gemitzi et al. [14] used the combination of geographic information systems with MCDM to solve the problem of choosing a sanitary landfill site in one of the Greek provinces. Eighteen criteria were included in the overlapping analyzes, and an appropriate map was produced for the sanitary landfill site. The comparison between the two maps resulting from AHP and RSW was made by applying a method to determine the pixel ratio of the congruent and non-conforming areas of the two maps. The primary purpose of this topic is to evaluate suitable sites for sanitary landfills in the city of Shatra by integrating GIS with MCDM, using fifteen essential criteria.

2 Study Area The city of Shatra is located in the southern part of Iraq and is located on one of the two branches of the Garraf River, descending from the Tigris River in the middle Euphrates region, about 350 km from the capital Baghdad and administratively and geographically affiliated to the Dhi Qar Governorate. It is located between longitude (46°10 32.45"E) and latitude (31°24 30.17"N) (refer with Fig. 1) The city of Al-Shatra occupies a distinct geographical location due to its control over the roads linking Baghdad and the southern governorates. Its population is 859.523 according to the last census in 2014, and its area is (1744 km2 ) (Iraq Ministry of Planning, 2015). The city of Shatra is divided into several areas, including the city of Al- Gharraf. The city of Daway.

Determination of Potential Sites for Landfill

75

Fig. 1. Location map of the study area.

3

Methodology and Input Data

The geographic information system was integrated with a multi-decision analysis to assess the appropriateness of choosing the most appropriate site for the sanitary landfill. The most important of these steps. (A) Creating a digital map for each standard utilizing a geographic information system, creating a set of categories, and excluding inappropriate places. (B) Creating buffer zones around the areas according to specific percentages based on previous studies and expert opinion suits each standard map. (C) Using (AHP, RSW) to determine the weights and choose the most appropriate site according to the priority of the study’s goal. (D) A comparison was made between the maps produced by the Weighted linear combination (WLC) to obtain an appropriate digital map that contains the places of sanitary landfills. In this study, fifteen layers of input maps were prepared and evaluated for use in the analysis process by the GIS environment. Several steps were used in the GIS until the final layers were obtained, for example (buffer, extract, clip, Eerase, proximity, converts, reclassify and map algebra). Each criterion was divided into many categories and each category was given its appropriate degree based on previous studies and expert opinion. Buffer zones around each zone, and then the resulting maps were converted into a raster, as in the case of the depths of groundwater, where the layer was classified into four categories. Depth between (0–11 m, 11–12 m, 12–13 m, and more than 13 m, and given a rating of 2, 6, 8, 10, respectively (Fig. 2). For Urban centers, Buffer zone of less than 5000 m was given grades of zero, while Buffer zone between 5000 m and 10000 m was given the highest score was 10. When the distances were from 10000 m–15000 m, they were given a value of 5 moderately appropriate, and if they were greater than 15000 m, they were given a value of 1, as being very bad and inappropriate (Fig. 2). But the rivers, a buffer zone more than 1000 m from the river border was adopted to protect the river from pollution. As for a distance less than 1000 m, it was given a division of zero as unsuitable. Therefore, greater than this value was given as 10 (Fig. 2) for villages, a buffer zone distance less than 1000 m was given grading zero, while a buffer zone greater than 1000 m, were given a value of 10 (Fig. 3). For schools, a buffer zone has been approved or allocated for the school site. When the buffer distance is 2000 m, it is given a rating of 2. It is considered unsuitable, and if

76

M. N. Mohammed and F. G. Mohammed

the buffer distance is 4000 m, an evaluation is assigned to it by 10. Very suitable, but if the insulation distance is 6000 m, it is considered suitable, but in a lesser way, and has a rating of 4. Whereas, when the distance is greater than 6000 m, it is considered very bad and was given a zero rating (Fig. 3). The layer" Roads" a Buffer zone distance from the road to the land site less than 500 m, it is considered unsuitable and was given a value of zero in the classification of the criteria, while from 500 to 1000 m was given a value of 7 slightly suitable. The buffer zone 1000 to 2000 m was given a value of 10, which is considered very suitable. While the distance from 2000 to 3000 m was given a value of 5, so it is considered moderately suitable. Areas that are greater than 3000 m are given a value of 2, so they are considered bad and unsuitable areas (Fig. 3).

Fig. 2. Variation of groundwater depth, urban areas, and rivers in the study area.

Fig. 3. Variation of villages, schools, and road in the study area.

The criterion of topography, elevation, was determined or selected in this study, and the highest altitude of the city of Shatra is 11 m above sea level (a.m.s.l) in a certain area of the city of Shatra, which is located In the southeastern part of the State of Iraq. While the lowest height is 2 m (a.m.s.l). As in the (Fig. 4). For the "Slope" layer, sloping land is an important and main factor when choosing the optimal site for the sanitary landfill. The area of the slope when it is very steep will cause an increase of pollutants from the sanitary landfill site in the neighboring areas. It leads to leachate leaking easily

Determination of Potential Sites for Landfill

77

and based on the digital elevation map (DEM), the degree of the gradient of the city the slice is from zero to 20° above sea level. Therefore, slopes from 0 to 10 were given a value of 10 because it is considered very suitable. Whereas, places that are more than 10 degrees were given a value of zero. It is considered inappropriate (Fig. 4). For the layer of the power plant, The distance from the power plant to the landfill sites was more than 1000 m, where it was given a value of 10. Because it fits in the classification of these criteria, while the distance less than 1000 m was given a value of zero because it is not appropriate (Fig. 4). In the water surface, a buffer zone of more than 1000 m was allocated around the marshes, water bodies, and streams in the study area, to ensure safety and avoid environmental pollution. When the distance is 250 m, it was given a zero-rating. When the buffer distance was 500 m, it was given a value of 2 is unsuitable. When the insulation distance is 750 m, it has been assigned a rating of 8. It is considered suitable, but in a lesser way, and if the insulation distance is more than 1000 m, it has been given a rating of 10. It is very appropriate, see Fig. 5.

Fig. 4. Variation of elevation, slope, and power plant in the study area.

Fig. 5. Variation of water surface, land use, and gas pipelines in the study area.

Five categories used to prepare the (land use) layer; these are agricultural land, sandy lands, unused land, water areas, and industrial areas. Collecting the layers and then merging them into one layer called (land use), so the unused land categories were

78

M. N. Mohammed and F. G. Mohammed

given a rating of 10 and considered very suitable, while sandy lands were given a rating of 5 as moderately suitable. The rest of the categories were given a value of zero because they were unsuitable. As shown in Fig. 5, the buffer zones from “gas pipelines", power lines, oil pipelines, to a landfill site were taken in this study (300, 250, and 400 m) on both sides. It was given a grading value of zero. Distances more than these limits, they were given a score value of 10 as shown in Figs. 5 and 6. The layer "Wells" a buffer distance of more than 1000 m is considered very appropriate and was given a value of 10. When the distance is less than 1000 m, it is considered unsuitable. It was excluded and given a value of zero (Fig. 6).

Fig. 6. Variation of power lines, oil pipelines, and wells in the study area.

4 Selection of Suitable Sites 4.1 Analytical Hierarchy Process (AHP) Method After preparing the criteria by geographic information systems and extracting the weight for each criterion and then reclassifying the layers based on their weights by (map algebra) to create a map of the appropriate sites and according to the classification (unsuitable, moderately suitable, suitable, most suitable, and excellently suitable), then the pairwise comparison matrix is obtained. Then the weights are set as shown in Table 1. Calculating the weights of the matrix. It includes calculating the weights of three steps: (a) Add the values in each column of the matrix. Then divide each element in the matrix by the sum of its columns (the matrix product is indicated, as normalized Pair-wise comparisons matrix), (b) the items in each row are then averaged for the Normalized matrix, which involves dividing the normalized value of each row by the number of criteria. This average gives an estimate of the relative weights of the parameters being compared, (c) Estimate or estimate consistency. The purpose is to know if the comparisons are consistent or not. It involves following operations. 1) Determining the weighted sum vector by multiplying the first criterion by the first column of the original Pair-wise comparisons matrix. 2) Determining the weight sum vector according to the weights of the previously determined criteria.

0.2

0.2

0.2

0.1

0.2

0.1

0.2

Road

Elevation

Slope

Power plant

Water surface

Land use

Gas pipeline

0.1

0.2

Schools

Wells

0.5

Villages

0.2

0.3

Rivers

0.2

0.3

Urban area

Oil pipelines

1

Groundwater depth

Power lines

Groundwater depth

Criteria

0.1

0.1

0.1

0.1

0.1

0.3

0.1

0.2

0.2

0.2

0.3

0.5

0.5

1

3

Urban area

0.1

0.1

0.1

0.2

0.2

0.5

0.2

0.2

0,3

0.3

0.5

0.5

1

2

3

Rivers

0.1

0.2

0.2

0.1

0.1

0.2

0.2

0.2

0.3

0.3

1

1

2

2

2

Villages

0.1

0.1

0.1

0.1

0.1

0.2

0.2

0.2

0.3

0.3

1

1

2

3

4

Schools

0.1

0.1

0.2

0.2

0.2

0.2

0.5

0.5

0.5

1

3

3

3

4

5

Road

0.2

0.3

0.3

0.3

0.3

0.5

1

0.5

1

2

3

3

3

4

4

Elevation

0.2

0.2

0.2

0.5

0.5

0.5

1

1

2

2

5

4

4

5

5

Slope

0.3

0.3

0.3

0.5

1

1

1

1

1

2

5

4

4

6

7

Power plant

0.2

0.3

0.3

0.5

0.5

1

1

2

2

4

5

5

2

3

4

Water surface

0.5

0.5

1

1

1

2

1

2

3

5

6

6

5

6

6

Land use

0.3

0.5

0.5

1

1

2

2

2

3

5

6

7

5

7

8

Gas pipeline

0.5

0.5

1

2

1

3

3

4

3

5

7

8

6

6

8

Power lines

Table 1. Pairwise comparison matrix for selecting a suitable landfill location.

0.5

1

2

2

2

3

3

4

3

6

7

8

7

7

8

Oil pipelines

1

2

2

3

2

4

3

5

4

6

7

8

8

8

9

Wells

0.249

0.304

0.361

0.459

0.465

0.756

0.725

0.845

1.027

1.451

2.422

2.625

2.593

3.368

4.464

Eigenvalue

0.011

0.014

0.016

0.021

0.021

0.034

0.033

0.038

0.046

0.066

0.11

0.119

0.117

0.152

0.202

Priority vector

Determination of Potential Sites for Landfill 79

80

M. N. Mohammed and F. G. Mohammed

3) The lambda (λmax ) is calculated from the following.

λmax =

n k=j

m {Wj aij}

(1)

i=1

where aij represent the sum of criteria in each columns in the matrix and Wj represents the value of weight for each criterion, which is conforming to the priority vector in the matrix of decision. Therefore, the consistency index (CI) is determined by the following Eq. CI =

(λmax − n) (n − 1)

(2)

The consistency ratio (CR) was obtained according to [15]. The variation of RI with n are given in Table 2. CR(%) = CI/RI

(3)

Table 2. Random-inconsistency-indices for different values-of (n) [16]. n

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

RI

0

0

0.5

0.9

1.1

1.2

1.3

1.4

1.4

1.4

1.5

1.4

1.5

1.5

1.5

4.2 The Ratio Scale Weighting (RSW) Method The decision-making process in the RSW method depends on determining the appropriate degree value for each criterion, and this is according to the importance of the criterion, then a value of 100 is given. The criterion that is more important among the criteria and to be the basis of values for the remaining criteria. The other criteria will be given values less than 100, see Table 3. According to the importance of the criteria, for estimating the original weighting for criteria NW, by using the RSW method, where the relative weight of each standard was divided by the relative weighted value of the standard that is less than it. Then, the standard weights were determined by the RSW method. NWi i = 1, 2, 3, . . . .n Wi =  n j=1 NWj

(4)

Wi; represent the normalized weight of criterion which divided by their sum and NWi; represent the original weight of each criteria of “area” i under criteria j, n; number-of criteria.

Determination of Potential Sites for Landfill

81

Table 3. The criterion weightings defined for the RSW method and normalized weights. No.

Criteria

Ratio scale value

New weight

Normalized weight

1

Groundwater

100

20

0.209

2

Urban area

77

15.4

0.161

3

Rivers

70

14

0.146

4

Villages

55

11

0.115

5

Schools

41

8.2

0.085

6

Road

26

5.2

0.054

7

Elevation

26

5.2

0.054

8

Slope

25

5

0.052

9

Power plant

12

2.4

0.025

10

Water surface

12

2.4

0.025

11

Land use

9

1.8

0.018

12

Gas pipelines

7

1.4

0.014

13

Power lines

7

1.4

0.014

14

Oil pipelines

6

1.2

0.012

15

Wells

5

1

0.010

95.6

1.000

Sum

5 Result and Discussion 5.1 Producing the Maps of AHP and RSW The accuracy in AHP is within the required level, so The consistency index is equal to (0.086), which is less than (0.1), and also the value of (λmax ), which is equal to (16.21) and random-index (RI) is equal to (1.59) It was noted that the accuracy is high depending on the value of the consistency ratio (CR), where consistency index was divided by random-index and equal to (0.054) which is not more than 0.10, which shows a suitable range. To reach the value of the appropriateness of the potential sites for the sanitary landfill, fifteen layers were entered into the GIS environment. After that, the (WLC) method was used, using the following equation. n WJ ∗ Cij (5) Ai J=1

Ai; represent the suitability index for “area” i, W j; represent relative importance weight of the criteria, Cij; grading value” of area i, n; represent the total number of criteria. The equation was used for all criteria by extension tools, map algebra in geographic information systems. The appropriate index was estimated by summing the outputs by multiplying the values of the criteria scores with the relative weight of each criterion. The output map is divided into five categories, according to the appropriate locations (unsuitable, moderately suitable, suitable, most suitable, and excellent suitable), the Total

82

M. N. Mohammed and F. G. Mohammed

area by AHP (104.5736, 281.5543, 552.3436, 684.3422, 214.9142 km2 ) respectively, see Fig. 7.

Fig. 7. Final model maps of a suitability landfill using AHP and RSW.

5.2 Comparison of the Results from AHP and RSW The maps that were produced from multi-resolution analysis methods (AHP, RSW) were compared, and each map was classified into five categories: “unsuitable, moderately suitable, suitable, most suitable, and excellently suitable. To compare these two methods, the maps were entered into the geographic information system by using the spatial analysis tool (map algebra) by applying the “Combine” formula (AHP raster map, RSW raster map). The maps were collected in a single map called the final comparison map, and the final map includes the number of pixels for each of the categories resulting from AHP and RSW. It also includes the compatibility ratios for each category to be used in matching refer with Table 4. The similar combine several raster categories for AHP and RSW (1,1); (2,2); (3,3); (4,4); and (5,5) considered “matching” to their number of pixels result from the methods. The different combine a number of raster categories for AHP and RSW [(1,2); (2,1); (2,3); (3,2); (3,4); (3,5); (4,3); (4,5); and (5,4)] it considered non matching. The output map has been classified for comparison, showing the categories of the matching output number of the raster data categories. Where we merged the pixel categories to obtain matching regions, while the other classes were combined to obtain non-conforming regions (acceptance regions), see Fig. 8. Where the area of the matching region is (80.36%) and the non-conforming pixels represent (19.64%). If the matching and non-conforming pixels are combined, it can be concluded that they are identical with (100%). After conducting a comparison process between the methods of multi-decision analysis, four candidate sites were identified that meet all the requirements of the landfill site and are located in different places in the city of Sharja, each site was named by letters (A and B) and the area of these sites was 1,830 km2 , 9.217 km2 respectively, see Fig. 9.

Determination of Potential Sites for Landfill Table 4. The results of combining two maps resulted from (AHP) and (RSW) methods. Value

Count

Raster category AHP

1

1395

(S)3

(S)3

9.37%

Matching

2

2601

(MS)2

(MS)2

18.14%

Matching

3

1461

(U)1

(U)1

10.19%

Matching

4

1089

(VS)4

(ES)5

7.59%

Non-matching

5

492

(MS)2

(S)3

3.43%

Non-matching

6

3818

(VS)4

(VS)4

26.62%

Matching

7

1532

(ES)5

(ES)5

15.68%

Matching

8

472

(S)3

(MS)2

2.29%

Non-matching

9

612

(VS)4

(S)3

0.27%

Non-matching

10

608

(U)1

(MS)2

4.24%

Non-matching

11

201

(U)1

(MS)2

1.40%

Non-matching

12

37

(MS)2

(U)1

0.26%

Non-matching

13

1

14

21

Raster category RSW

Corresponding pixels ratios

Classification

(S)3

(ES)5

0.01%

Non-matching

(ES)5

(VS)4

0.15%

Non-matching

Fig. 8. The comparison map of RSW and AHP.

83

84

M. N. Mohammed and F. G. Mohammed

Fig. 9. Location of candidate sites.

6

Conclusions

Using multi-decision analysis with the geographic information system to choose the optimal site, methods were used to integrate MCDM with GIS, compare the results, and know the accuracy, and there are two security methods for MCDM (AHP, RSW). The areas that were classified into five categories appeared (unsuitable, moderately suitable, suitable, most suitable, and excellently suitable) and areas Calculated by pixels in GIS; the results indicate that (excellent suitably) achieved an area of 18.2% and 16.1%, respectively, in the AHP and RSW roads. In addition, there is only one sanitary landfill site in Shatra city, but it is random and not subject to health and environmental standards and was built in the wrong way. In addition, this research provides a community service due to the accumulation of waste in the cities and the lack of roads, leading to difficulty reaching the places designated for landfills. Moreover, through spatial analysis processes, two sites were identified in the city of Shatra. Among the best sites is a site (A) as it is considered the best and most appropriate for its ease of access. Studies have proven that GIS is a powerful tool in dealing with the large volume of Data and appropriate positioning ranges. In the end, the accuracy of the analysis processes must be tested in order to ensure the analysis is correct, and for this reason, field visits were carried out in order to compare with the results of the modeling. It was revealed through field visits that the distance from roads and distances from power lines, oil pipelines and land uses are all acceptable with a small percentage of error in Some criteria that are of little importance compared to other criteria during the modeling and classification processes.

References 1. Minghua, Z., et al.: Municipal solid waste management in Pudong new area. China. Waste Manage. 29(3), 1227–1233 (2009)

Determination of Potential Sites for Landfill

85

2. Kim, K.R., Owens, G.: Potential for enhanced phytoremediation of landfills using biosolids–a review. J. Environ. Manage. 91(4), 791–797 (2010) 3. Yesilnacar, M.I., Cetin, H.: An environmental geomorphologic approach to site selection for hazardous wastes. Environ. Geol. 55(8), 1659–1671 (2008) 4. Lober, D.J.: Resolving the siting impasse: modeling social and environmental locational criteria with a geographic information system. J. Am. Plann. Assoc. 61(4), 482–495 (1995) 5. Kontos, T.D., Komilis, D.P., Halvadakis, C.P.: Siting MSW landfills on Lesvos island with a GIS-based methodology. Waste Manage. Res. 21(3), 262–277 (2003) 6. Sener, ¸ S, ¸ Sener, E., Karagüzel, R.: Solid waste disposal site selection with GIS and AHP methodology: a case study in Senirkent-Uluborlu (Isparta) Basin. Turkey. Environ. Monitoring Assessment 173(1), 533–554 (2011) 7. Jaseim, E.S., Mohammed, F.G.: Waterways finding in the province of Kirkuk-Iraq based on hydrological analysis of digital elevation model. Int. J. Sci. Eng. Res. 6, 158–162 (2015) 8. Kara, C., Doratli, N.: Application of GIS/AHP in siting sanitary landfill: a case study in Northern Cyprus. Waste Manage. Res. 30(9), 966–980 (2012) 9. Olusina, J.O., Shyllon, D.O.: Suitability analysis in determining optimal landfill location using multi-criteria evaluation (MCE), GIS & remote sensing. Int. J. Comput. Eng. Res. 4(6), 7–20 (2014) 10. Ebistu, T.A., Minale, A.S.: Solid waste dumping site suitability analysis using geographic information system (GIS) and remote sensing for Bahir Dar Town, North Western Ethiopia. Afr. J. Environ. Sci. Technol. 7(11), 976–989 (2013) 11. Moeinaddini, M., Khorasani, N., Danehkar, A., Darvishsefat, A.A.: Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj). Waste Manage. 30(5), 912–920 (2010) 12. Alkaradaghi, K., Ali, S.S., Al-Ansari, N., Laue, J., Chabuk, A.: Landfill site selection using MCDM methods and GIS in the Sulaimaniyah Governorate. Iraq. Sustain. 11(17), 4530 (2019) 13. Alkhuzaie, M.M., Janna, H.: Optimum location for landfills sites based on GIS modeling for Al-Diwaniyah City, Iraq. Int. J. Civil Eng. Technol. 9(8), 941–951 (2018) 14. Gemitzi, A., Tsihrintzis, V.A., Voudrias, E., Petalas, C., Stravodimos, G.: Combining geographic information system, multicriteria evaluation techniques and fuzzy logic in siting MSW landfills. Environ. Geol. 51(5), 797–811 (2007) 15. Saaty Thomas, L.: The Analytic Hierarchy Process. McGrow-Hill, New York (1980) 16. Chang, C.W., Wu, C.R., Lin, C.T., Lin, H.L.: Evaluating digital video recorder systems using analytic hierarchy and analytic network processes. Inf. Sci. 177(16), 3383–3396 (2007)

Selection of Optimal Location for Wind Turbines in Diyala Governorate Using the Analytic Hierarchy Process (AHP) with GIS Technique Qater AL- Nada Rasim Rejap(B) and Yousif H. Khalaf Department of Surveying Engineering, University of Baghdad, Baghdad, Iraq {k.rejab1412,yousef.hussein}@coeng.uobaghdad.edu.iq

Abstract. Renewable energy is considered one of the possible alternatives in attention to severe economic improvement and growing power consumption. The improvement of wind farm projects calls for the right making plans and evaluation. Usually, the suitability for locations of wind turbines is based on wind speed. The (AHP) became a singular method to find out the possible locations for the wind turbines in the study areas, primarily depending entirely on four main criteria (Climatical, Economical, Geomorphological and Environmental). Each main group has sub-criteria that influence the wind turbine location. That ranking for every criterion had been based on the to be had a review of the literature. The ’consistency ratio’ among the experts’ viewpoints were examined using the pairwise contrast approach, and a very last weight was computed for every criterion. A map was created showing the suitability sites for wind turbines using GIS technology. It was found that (8%) of the study area had higher suitable, good suitable areas (38%), less suitable areas (42%), and unsuitable areas (12%). The western side of Diyala Governorate is the optimal area for the construction of wind turbines. Keywords: AHP · GIS · Model builder · Reclassify of raster data · Suitable areas map

1 Introduction Energy is the riding force for worldwide economic improvement and industry. Fossil fuels are presently the number one available source that deliver the country’s growing energy needs. Nevertheless, fossil fuel reservoirs were limited, and their use has negative environmental risks. Renewable energy sources have been a topic of study since the first oil crisis since they are renewable, sustainable, and environmentally - friendly. Renewable energy resources for generating energy have improved and are increasingly trying to replace fossil fuel power plants. The negative environmental impact of old energy generation processes, particularly coal and oil-fired power plants, has attracted increasing public attention. Wind energy is now one of the maximum significant renewable sources of energy. Because of its matured and cost-powerful energy production technologies, it is a quickly growing, general use, and commercially appealing source of energy generation. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 86–98, 2023. https://doi.org/10.1007/978-981-19-7358-1_9

Selection of Optimal Location for Wind Turbines

87

value of electricity supplied from wind energy may compete with energy from fossil fuel plants. Identifying appropriate areas for wind turbine usage by incorporating outcomes of the survey and study is complicated and calls for decision-making on this issue to human error. Geographic information systems could assist in reducing those mistakes by identifying potential areas using a mixture of digitized thematic maps and a conceptual model for data integration. Choosing wind turbine locations is the essential selection for improving a wind farm. It’s great done by identifying the criteria influencing the environment, economy, and climate. GIS and AHP were used to assist in determining the optimal location for a wind turbine [1, 2].

2 Literature Review This study reviews available literature addressing Choosing the optimal locations for wind turbines. Where Tegou et al. [3] conducted a study integrated to assess land appropriateness for wind turbine location on the island Lesvos of Greece. Results confirmed that just 1.3% of the full area of Lesvos might be possible for wind turbine installations, although the favorable wind ability exists in the bigger parts of the islands. In another study, Saleous et al. [4] Conducted a study to evaluate the possibility of constructing wind turbine farms offshore of Abu Dhabi, Emirates, to determine the favorable location for this farm the usage of GIS techniques. Some appropriate areas are near outlying islands, including Delma Island, but may be taken into consideration for wind turbines to feed the islands. Second, there are also the ones close areas to the western coastline (extended extra than 50 km) that may perform as ability location for wind turbine plantation and power technology feed the mainland. Similarly, Al-Shabeeb et al. [5] Studied to identify potential wind turbine sites in northwestern Jordan primarily based on 5 criteria (wind speed, rainfall, slope, elevation, and land use) that influence wind turbines locations, using the AHP inside the GIS system. The outcomes of this study indicated that primarily depending totally on the criteria, the regions which may have high and really high appropriateness represent 45% of the overall study area. In a different study, Ali et al. [6] Conducted a study to choose the optimal location for an onshore wind farm in South Korea with the use of a comprehensive and structured GIS-MCDM method. The site (latitude 34.58 and longitude 138.42) was found to be the most suitable place overall for the development of a wind farm on the shore, with a total area of 29.4 Square kilometers and a mean wind speed of 50 m above floor level was found to be 6.61 m/s. In the work of Öztürk and Serkendiz [7] Studied choose location for wind farms in Balıkesir, NW Turkey, by the use of GIS. The following map became divided into 4 Categories primarily based totally on their suitability (in some way appropriate, moderately appropriate, appropriate, and highly relevant). The Categories have been distributed as highly appropriate by 12%, appropriate by 29%, moderately appropriate by 36%, and in some way proper by 23%. Similar studies were additionally performed in Saudi Arabia by Baseer et al. [8], they used the MCDM method based on GIS modeling for suitable wind farm site choice. Firstly, the most appropriate sites are observed to be close to Ras Tanura at the coast in the Eastern Provide. Secondly, Turaif in Al-Jawfregionat northern borders. Lastly, Al-Wajh is on the coast in the western area. The middle and southeastern

88

Q. AL-Nada Rasim Rejap and Y. H. Khalaf

area is discovered to be unsuitable because of scarce wind resources, few settlements, and much less connectivity via way of means of roads and the electric grid. Habib and Matouk [9] additionally performed a study on establishing the best-suitable area of wind parks in Syria by evaluating spatial multi-criteria inside a GIS environment using the AHP. The resulting map of wind turbine farms in Syria indicates that most of the high suitable areas were located in the southwestern and a parts center of the county.

3 Study Area and Methodology Diyala Governorate is one of the governorates located in central Iraq, and its center is the city of Baqubah, as shown in Fig. 1. Diyala governorate is bordered from the north by Sulaymaniyah and from the northwest by Salah al-Din governorate, Baghdad governorate from the southwest, and Wasit governorate from the southwest. These are its internal borders, an external border area, and an external area Iraq’s eastern border with Iran. It is located in the flat sedimentary plain between two latitudes (33.3–35.6) north and longitude (44.22–45.65) east, covering an area of about (17685 km) and constituting (4.1%). This site plays a major role in determining the importance of the commercial area and its relationship with the neighboring areas of the area, located near Baghdad and Iran. Diyala Governorate is divided into the following districts (Baquba, Muqdadiyah, Khanaqin, Khalis, Kifri and Baladrouz). The prevailing winds in the governorate, in general, are northwest. There is a noticeable variation in wind speed between winter and summer. The highest rates of wind speed in summer are 4.1 m/s and 2.1 m/s, and the lowest rates in winter are 1.1 m/s [10].

Fig. 1. Study area.

The methodological scheme for choosing the optimal site for wind turbines implemented in this research is installed in wonderful steps that have been summarized and clarified in Fig. 2.

Selection of Optimal Location for Wind Turbines

89

Fig. 2. Flowchart of the adopted methodology.

3.1 Determination of Criteria The criteria are determined that affect selecting the optimal sites for wind turbine farms. These criteria were adopted from previous research and studies. The questionnaire includes four groups of the main criteria (climatic, economic, geomorphological, and environmental). Each main group has sub-criteria, as shown in Table 1. Table 1. Criteria adopted in this study. Main criteria

Sub – criteria

Climatical

Wind speed Rainfall Temperature The humidity

Economical

Distance to main roads Distance to cities Distance to power lines Distance to electricity substations

Geomorphological

Slope (continued)

90

Q. AL-Nada Rasim Rejap and Y. H. Khalaf Table 1. (continued)

Main criteria

Sub – criteria Altitude Orientation aspect

Environmental

Land cover/use

A questionnaire was conducted showing the criteria that affect and their importance in choosing the optimal location for wind turbine farms. By adopting an expert opinion in this questionnaire, those who have experience with wind turbine energy and have full knowledge of evaluating the location of wind turbines and developing wind energy. They are experts from different universities and employees with specializations in energy. 3.2 Determination of Criteria Weight Weights are calculated for the main and sub-criteria, where the Analysis Hierarchical Process (AHP) method was used to calculate these weights. The Analytical Hierarchical Process is a multi-criteria decision-making approach. This method attracted the hobby of many researchers specifically because of the fine mathematical properties of the approach and the reality that the specified enter information or, as a substitute, easy to obtain [11]. In fact, to AHP to be performed, three steps have to be carried out sequentially [12] First, the aim of trouble. In addition to the hierarchies of diverse criteria and sub-criteria influencing the aim needs to be clearly defined, that is, in this case is selecting the most appropriate locations for wind farms utilizing economic, environmental, geomorphological and economic criteria. Then, the alternative, i.e., the appropriate areas, is organized at the lowest of the hierarchical [13]. In a second step, make pair comparisons of all main and Sub Criteria impacting. As a rule, criteria may be evaluated in steps classically varied from 1 to 9. This is proven in Table 2. Table 2. AHP importance scale. Importance scale

Definition of importance scale

1

Equally important

2

Equally to moderately important

3

Moderately important

4

Moderately to strongly important

5

Strongly important (continued)

Selection of Optimal Location for Wind Turbines

91

Table 2. (continued) Importance scale

Definition of importance scale

6

Strongly to very strongly important

7

Very strongly important

8

Very strongly to extremely important

9

Extremely important

Lastly, a weight vector was calculated that indicates the relative importance of the criteria. Then it was calculated. The consistency index (CI) and the consistency ratio (CR) can be calculated from the following equations CI =

λ max − n n−1

(1)

CR = CI/RI

(2)

where: λmax is the most of eigenvalue of the judgmental matrix, and n: matrix’s size. RI (random index) is being selected from Table 3, which changed into calculated the usage of generated random matrixes for every length of matrix n. Table 3. The RI values. n

1

2

3

4

5

6

7

8

9

10

RI

0

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

After which, a programming code was used to do the calculations to AHP by MATLAB. This is for easy and quick calculations. 3.3 Data Collection The wind farm location assessment data have been obtained from diverse sources and have been in varied file formats. A few of the data had to be preprocessed earlier than they had been used in the evaluation study. The particular data utilized, the file format, and the sources are provided in Table 4.

92

Q. AL-Nada Rasim Rejap and Y. H. Khalaf Table 4. Data collection and their sources.

Data

File format

Locations of meteorological stations Coordinates sheet

Data Source Iraqi meteorological organization and Seismology

Wind speed

Table

Rainfall

Table

Temperature

Table

The humidity

Table

City borders Cities location

Shape file/polyline General Authority of Survey, Iraqi Ministry of Water Resources Shape file/point

The road network

Shape file/polyline

Water bodies

Shape file/polyline

Runway

Shape file/polyline

Oil pipelines

Shape file/polyline

Oil fields

Shape file/polyline

Electric lines Electric station

Shape file/polyline Energy Institute, Iraqi Ministry of Shape file/polyline Electricity

Archeological sites

Raster map

Iraqi Ministry of cultural, Tourism and Antiquities

DEM / SRTM

Raster map

United States Geological Survey (USGS) https://earthexplorer.usgs.gov/

Land cover

Raster map

https://www.divagis.org/gdata

3.4 Site Selection in GIS A Geographical Information System (GIS) is a system for capturing, storing, studying, and dealing with data and associated features, which can be spatially referenced to the Earth. Functions of GIS include data entry, data display, data management, data retrieval, and analysis [14]. The optimal location for a wind turbine is selected through the GIS, the use of the abilities of the Model Builder, in which Model Builder is used to creating, edit, and manage geoprocessing models that automate the one’s tools. Models are workflows that collective string sequences of geoprocessing tools, feeding the output of 1 tool into any other tool as input, as shown in Fig. 3. Model Builder can also be a visible programming language for building workflows. The model is built to choose the optimal site through several steps: Step 1: The data used must be in a raster format, so geoprocessing tools were used to convert criteria into raster models. • Use the IDW tool to convert climate criteria into raster models.

Selection of Optimal Location for Wind Turbines

93

Table 5. Shows the weights of the main and sub criteria. Main criteria

Local weights

Sub – criteria

Local weights

Total weights

Climatical

0.51

Wind Speed

0.70

0.36

Economical

Geomorphological

Environmental

0.28

0.12

0.10

Rainfall

0.13

0.06

Temperature

0.11

0.05

The humidity

0.07

0.04

Distance to main roads 0.25

0.07

Distance to cities

0.26

0.07

Distance to power

0.29

0.08

Distance to electricity substations

0.21

0.06

Slope

0.27

0.03

Altitude

0.43

0.05

Orientation aspect

0.29

0.03

Land cover/use

1

0.10

• Using the Euclidean Distance Tool to generate raster data for economic criteria. • Use of spatial analysis tools (Slope tool and Aspect tool) To create slope, aspect, and raster models. Step 2: Reclassify is used to simplify or change the interpretation of raster data. This was done using the reclassify tool from the spatial analysis tools. The new reclassified raster data included five categories, which were later provided with the weight value. Step 3: The weighted overlay tool overlays several raster datasets using a common measurement scale and weights that had been calculated by the AHP method. This tool was used to establish the final map and suitability index of the different locations in the study areas. The input raster datasets where the reclassified factor layers with their influence obtained from the pairwise comparison. Step 4: Condition and Filter tools have been adopted. • Use the Con tool to create a new raster layer for the areas of high suitability by excluding the regions of low suitability. • Use the Filtering tool to get rid of the few spaces to create wind turbine farms. • Use the Erase tool to remove areas that overlap with protected areas.

94

Q. AL-Nada Rasim Rejap and Y. H. Khalaf

Fig. 3. Shows the optimum site selection using the model builder.

4 Results and Discussions 4.1 Criteria Weights Twelve criteria affecting the selection of wind turbine sites were adopted through the questionnaire. Then the weights of the main and sub-criteria were calculated using the AHP method with the results of the questionnaire. The results for the weights of the main criteria showed that the climatic criteria had the highest weight at a value (0.51) and that the environmental criteria had the lowest weight at a value (0.10). As for the results of the weights of the sub-criteria, they are as follows: • The highest weight among the climatic sub-criteria is the wind speed criterion at a value (0.70). • The highest weight among the economic sub-criteria is the criterion of distance to power lines at a value (0.29).

Selection of Optimal Location for Wind Turbines

95

• The highest weight among the geomorphological sub-criteria is the Altitude criterion at a value (0.43).

4.2 Criteria Raster Models After performing the reclassify process, the following was obtained: 1) The raster data of the climatic criteria (wind speed, temperature, humidity, and precipitation). Since the increase in wind speed and humidity increases the performance of wind turbines and vice versa with respect to temperature, Fig. 4 shows that wind speed and humidity increase in the western and southern areas, and the temperature is high in the northern and eastern areas, as for rain it is Suitable in the center of the study area. As a result, the southwest area is likely to be the best for constructing wind turbines according to climatic criteria.

Fig. 4. Reclassified raster models for the climatic criteria.

2) The raster data of the geomorphological criteria (slope, elevation, and aspect). Figure 5 shows that the southwestern areas are less elevated and flat, and the northeastern areas are the elevation and steepest. The southwest region is likely to be the most suitable for constructing wind turbines according to geomorphological criteria.

96

Q. AL-Nada Rasim Rejap and Y. H. Khalaf

Fig. 5. Reclassified raster models for the geomorphological criteria.

3) The raster data of the economic criteria. Where the value No. 5 shown in Fig. 6 indicates the areas near cities, roads, power transmission lines, and electrical stations, which are preferably these nearby areas are the most suitable for the construction of wind turbines.

Fig. 6. Reclassified raster models for the economic criteria.

4.3 Suitable Areas and Optimal Areas Through the main and secondary criteria that affect the construction of wind turbines, it was made clear that wind speed, distance to cities, power transmission lines, and other

Selection of Optimal Location for Wind Turbines

97

criteria are appropriate in the western region of the study area, and the northeastern parts are economically costly. After completing the reclassification process and applying the weighted fitting tool, the result obtained is a linear model classified into 4 categories depending on the degree of suitability. As shown in Fig. 7, the first category in dark brown color was unsuitable, and the second category in light brown is less suitable. The third category in light green color is good suitability, and the last category in dark green color is the highest suitability.

Fig. 7. Suitable areas for the construction of wind turbines.

Then the con and majority filtered tools were applied to get the results of the optimal areas for the construction of wind turbines, as shown in Fig. 8.

Fig. 8. Optimal areas for the construction of wind turbines.

98

Q. AL-Nada Rasim Rejap and Y. H. Khalaf

5 Conclusions The objectives of this study were to select the suitable and the optimal site for the construction of wind farms in Diyala Governorate/Iraq, using the Analysis Hierarchical Process (AHP) method and geographic information systems (GIS) technology. Where the GIS-based model was effective in determining the potential sites for building wind farms. The results identified unsuitable, less suitable, good suitable, and higher suitable areas, where the area of the unsuitable 2315.87 km2 (12.26%), less suitable areas 7954.31 km2 (42%), good suitable areas 7229.16 km2 (38%) and high suitable areas 1389.81km2 (8%). The optimal areas were identified on the western side of the study area.

References 1. Rodman, L.C., Meentemeyer, R.K.: A geographic analysis of wind turbine placement in Northern California. Energy Policy 34(15), 2137–2149 (2006) 2. Noorollahi, Y., Yousefi, H., Mohammadi, M.: Multi-criteria decision support system for wind farm site selection using GIS. Sustain. Energy Technol. Assess. 13, 38–50 (2016) 3. Tegou, L.I., Polatidis, H., Haralambopoulos, D.A.: Wind turbines site selection on an isolated island. WIT Trans. Ecol. Environ. 127, 313–324 (2009) 4. Saleous, N., Issa, S., Al Mazrouei, J.: Gis-based wind farm site selection model offshore Abu Dhabi Emirate, UAE. Int. Arch. Photogramm. Rem. Sens. Spat. Inf. Sci. 41 (2016) 5. Al-Shabeeb, A.R., Al-Adamat, R., Mashagbah, A.: AHP with GIS for a preliminary site selection of wind turbines in the North West of Jordan. Int. J. Geosci. 7(10), 1208 (2016) 6. Ali, S., Lee, S.M., Jang, C.M.: Determination of the most optimal onshore wind farm site location using a GIS-MCDM methodology: evaluating the case of south korea. Energies 10(12), 2072 (2017) 7. Öztürk, B., Serkendiz, H.: Location selection for wind turbines in Balikesir, NW Turkey, using GIS. Int. J. Environ. Geoinform. 5(3), 284–295 (2018) 8. Baseer, M.A., Rehman, S., Meyer, J.P., Alam, M.M.: GIS-based site suitability analysis for wind farm development in Saudi Arabia. Energy 141, 1166–1176 (2017) 9. Habib, M., Matouk, A.: March. Integrating AHP and GIS as a decision-making tool for the optimal allocation of wind farm: a case study of Syria. In: IOP Conference Series: Materials Science and Engineering, vol. 800, no. 1, p. 012019. IOP Publishing (2020) 10. Jafar, A.T.: The natural controls of Diyala Governorate and their impact on land transportation. Diyala Mag. (2011) 11. Bhushan, N.K.R.: The analytic hierarchy process. In: Strategic Decision Making. Applying the Analytic Hierarchy Process, pp. 11–21. Springer, London (2004) 12. Höfer, T., Sunak, Y., Siddique, H., Madlener, R.: Wind farm siting using a spatial analytic hierarchy process approach: a case study of the städteregion aachen. Appl. Energy 163, 222– 243 (2016) 13. Saaty, T.L.: How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48(1), 9–26 (1990) 14. Hunter, A.P.G., Bishop, A.P.I.: Introduction to GIS Definition of GIS, pp. 1–13. The University of Melbourne, Department of Geomatics (2013)

Automatic Co-registration of UAV-Based Photogrammetry and Terrestrial Laser Scanning in Urban Areas Mohammed G. Ahmed(B) and Fanar M. Abed Surveying Engineering Department, University of Baghdad, Baghdad, Iraq [email protected], [email protected]

Abstract. Laser scanning and Photogrammetry have become common techniques for providing monitoring services and producing 3D models for various applications in recent decades, including 3D modeling, reverse engineering, geoscience and anthropology, virtual reality, manufacturing engineering, and many more. A co-registration strategy is presented in this research to overcome the separated limitations of terrestrial laser scanners and Photogrammetry. The integration methodology used here is based on generating synthetic laser data to mitigate the extracting features problem for both 3D laser scanner and 2D Unmanned Aerial Vehicle (UAV) images. Then after, a structure-from-motion (SfM) procedure was applied following the automatic registration application. The fusion approach significantly reduced the roughness level of UAV images and resulted in more excellent density point clouds. This has a favorable impact on the level of detail obtained via fusionbased point clouds. Aside from increasing the finished model’s geometrical and graphical quality, this approach fixes the voids in Terrestrial Laser Scanning (TLS) data, retrieving more information, increases coverage, and assigning genuine colors to TLS data. This research represented a practical application in the urban city environment that seems typical towards future insights to make the compound a sample smart city based on geometry through a modified workflow starting from planning, data collection and processing, and data analysis and validation. Results have been analyzed statistically and discussed thoroughly for future co-registration development and application in multiple sectors. Keywords: Co-registration · Data fusion · SfM-MVS photogrammetry · UAV · TLS

1 Introduction Photogrammetry and laser scanning (PLS) standalone techniques or hybrid integrated techniques are both important in extracting 3D realism features. Every approach and procedure has its advantages and disadvantages. A point cloud technology is a dynamic concept in a 3D environment, and photo-realistic, scalable, georeferenced 3D data is extremely valuable for a wide range of applications.3D modeling extraction of cities is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 99–112, 2023. https://doi.org/10.1007/978-981-19-7358-1_10

100

M. G. Ahmed and F. M. Abed

a computer depiction of the Earth’s surface and its associated items, such as buildings, trees, and vegetation [1]. Geomatics is a broad term that covers several mapping and modeling techniques. Popular geomatics techniques include Photogrammetry, laser scanning, satellite remote sensing, Geographic Information System (GIS), radar-grammetry, and Global Positioning System (GPS). TLS and CRP are the two most extensively used methods for 3D feature extraction [2]. The construction of a realistic 3D model in urban areas relies heavily on geomatics techniques [3]. Photogrammetry is considered the main source of image-based standalone approaches to deliver 3D data. When used in conjunction with Computer Vision (CV) algorithms, Photogrammetry is considered today a state-of-the-art method for obtaining 3D data from 2D digital photographs [4]. The hybrid solution based on co-registration algorithms has evolved to address the needs of end-users and developed a way for creating realistic 3D products using photogrammetry and laser scanning (image and range-based) techniques [5]. So using image-based techniques such as Photogrammetry to extract realism and precise 3D features requires careful planning to apply in good illumination conditions and a professional selection of camera type and photo capture settings to deliver high-end products [6, 7].

2 Review of Literature In 2007, [8] investigated a method for producing an accurate 3D model of any structure using a regular digital camera as part of a rigorous case study. The main tool used in this work is a digital camera, which is freely accessible on the market. Close-range Photogrammetry works well for detailed 3D modeling. Later, [9] produced 3D city modeling using aerial and close-range Photogrammetry (CRP). Aerial Photogrammetry produces LOD2 from 2D photos. Due to scaling, the initiative seeks to construct a smaller building model using CRP. The CRP was blended with the aerial photos to simulate the scene. Architectural CRP was used to generate the DTM and 3D city models. Creating streets, trees, and parking from aerial photos. We created the base model and stereo model using aerial photos. In this case, RMS was 0.005 m, which satisfied the required precision [10]. Describe how to reconstruct three-dimensional polyhedral models from aerial photographs. Planar projection involves photometry. This method is featureless and relies on raw image brightness to optimize. Feature extraction and comparison are not included. So, they optimized an objective method based on image-based discriminant evaluation and gradient score across many aerial images. The optimization approach is Differential Evolution. Less accurate than feature-based 3D reconstruction [11] for 3D city modeling utilizing multi-source photogrammetric data to confirm LOD3 buildings. Create a 3D digital city of the University of Baghdad campus utilizing UAV and handheld static camera pictures. The study employed commercial image-based technologies like Agisoft Photoscan and Pix4Dmapper to integrate data. The conclusions are confirmed using ground truth data from two data collection situations. Less data is needed to produce better architectural models than 2D raw images. Using UAVs to photograph towering building facades is also advised. 3D city model from Baghdad University has RMSE of 0.271051 m in easting, 0.250703 m in northing, and 0.301240m in elevation.

Automatic Co-registration of UAV-Based Photogrammetry

101

While a study by [12] presented a scenario for the automated generation of 3-d photo-realistic models using Lidar data. They developed an automated 3D point cloud registration method, presented automatic target identification for georeferencing, and an automatic plane detection technique for surface modeling and texture mapping. They presented a method for creating accurate and georeferenced 3-D photo-realistic models using point clouds which they find valuable for future 3D realistic visualization. On the other hand, [13] presented a partnership on airborne laser scanning (ALS)/TLS data integration. The most powerful of these scanning instruments, Lidar can gather over 50 points per square meter and detect several echoes for landscape and cover restoration. However, ALS data has an accuracy of less than 10 cm and is sometimes incomplete. To make up for this, TLS can register unavailable or invisible components to ALS (interiors, facades, etc.). The combination of TLS and ALS data is necessary to construct a detailed 3D model of a building using a wavelet-based approach. While [5] georeferenced photos using lidar data. They employed a digital surface model and genuine orthophotos to enhance the realism of the display. They added 3D elements to the city model. They evaluated this work on the University of Calgary’s faculty. On the other hand, [14] examine the distinction between point clouds created using images and point clouds made using a laser. They highlight how photogrammetric accuracy is superior to lidar technology and how the density of object surface is significantly larger when photos are used. Additionally, they discovered some advantages to the photogrammetric technique. Further, the infusion concept [15] rebuilds objects’ surfaces using digital Photogrammetry and TLS data. The technology has grown to acquire precise and dense 3D points of item surfaces. This combination of two sensors fully utilizes the measurement ideas. Large-scale time of flight (TOF) TLS systems can capture point clouds at medium distances, whereas image-based surface reconstruction methods can gather data at near ranges. This mix supports innovative solutions to challenging problems. Needs include, but are not limited to, filling gaps in laser scanning data to decrease modeling errors, extracting more information in higher resolution, and target-free registration of many laser scan. At last [16], historically significant historic cities (or villages) and found to be intrinsically valuable. While these cities have a rich history, they also hold promise. So, it’s critical to understand how to conserve them and assess their extraction potential. As 3D virtual representations are a wonderful way to introduce visitors to these cities, increasing understanding of these concepts is required. Uneven urban structures and unique human dynamics in ancient urban contexts may limit the use of certain 3D data collection systems.

3 Case Study and Methodology The study area was selected to be in Al-Ayadi residential complex lies in Karkh side to     the west of Baghdad with coordinates (Lat; 33◦ 18 22 N Long; 44◦ 18 14 E). The project covers an area of 117500 sq. (11.7 hectares). It has 37 mid-rise buildings, schools, a social club, a hospital, a supermarket, shops, restaurants, and a mall. The new compound was chosen as our study site because it resembled a typical small city and could potentially be transformed into a smart city using geomatics data. Drone flight over the site, GPS, and laser scanner devices were all legal. Due to the proximity of the Baghdad International

102

M. G. Ahmed and F. M. Abed

Airport, the drone’s flight path was restricted, and the autopilot was not allowed to deliver the planned flight paths. It manually selected an area of interest within the compound and took aerial photos with a drone joystick. The aerial flight mission was executed after DGPS data collection. Then the Leica C10 laser scanner was used to collect data. The research methodology is separated into three stages: constructing 3D models using TLS and UAV images individually, combining laser scans with digital images in a single SfM scenario. The whole procedure of the work divides into two parts the fieldwork and the office work. In contrast, the fieldwork contains plaining and data capturing processes. The office work contains the raw data processing using a computer application that produces data of every step analyzed by computer programs to check the quality requirements. Figure 1 illustrates the implemented methodology in this research. The workflow applied is discussed in detail in the following sections.

Fig. 1. Methodology workflow.

3.1 Data Collection This section includes all fieldwork implemented on the site, starting from site planning, equipment settings, data capturing, etc. The UAV DJI phantom 4 pro, GNSS UNITs LEICA GS (10–15), Leica TLS (terrestrial laser scanner) C10, And Total station TOPCON GM-55 were equipment used to collect data in the site towards meeting the research objective.

Automatic Co-registration of UAV-Based Photogrammetry

103

3.2 GCP Establishment Collecting ground control points (GCPs) was critical for evaluating any virtual city project in 3D. This project phase includes major steps: setting the main primary control point, distributing the ground targets around the research area, see Fig. 2; and observing the targets with suitable tools for accuracy.

Fig. 2. The distribution of GCPs in the study site.

Fig. 3. Types of GCP targets used in the study site, (a) targets used for UAV & kinematic GPS, (b) targets used for static GPS.

Three types of targets used for the GCPs all made from wooden material. The first one was designed to be (15 × 15 cm) used for the main (Static measure) GCP shown in Fig. 3,b, whereas the second one was designed from wood plate to be (60 × 60 cm), with a cross marks in the center which divided it to for square painted parts in black and white, see Fig. 3,a. However, it was not permitted to stick targets on the building facets in order to collect GCPs for post-processing. Therefore, natural targets are carefully selected instead. The targets were distributed optimally in the study site and far enough away from the shade region and trees to guarantee their presence in nearly all photos. Further, these positions were selected to offer the best geometric position and later verify the triangulation process. The GCPs coordinate system used in these measurements

104

M. G. Ahmed and F. M. Abed

was WGS/UTM 1984 (EGM 2008), which corresponded to the internal GPS photo coordinates of the UAV. 3.3 UAV Data Capturing The UAV used in this study was a DJI Phantom 4 Pro quadcopter with a Pix4D Capture smartphone application. To get the best results from post-processing, you need to design your flight mission carefully based on the available data and the required outputs. Unfortunately, the nadir mission was terminated due to restricted security zones applied by the Baghdad international airport navigation system. Its close location to the study site prevented the UAV autopilot from applying the mission autonomously. Alternatively, one building represented by the central social club building was targeted and decided to represent the study herein. Therefore, the circular UAV coverage was implemented manually over this building to provide the aerial and the facet image coverage for coregistration with laser data. So that the photoshoot started late in the afternoon when the sun is vertical to avoid shadowing effects. Overlap was 80% end-lap and 60% side-lap, which are critical conditions for 3D object extraction from UAV-photogrammetry. The work was to cover the social club elements with circular path coverage. The camera gimbal was tuned to 75° (25 equivalent) for the circular path coverage in 3D city modeling applications. The GSD (Ground Sampling Distance) was calculated to be 0.99 cm/PX when using UAVs. The circular UAV coverage was manually applied over this building to obtain aerial and facet picture coverage for eventual laser data co-registration. 3.4 Terrestrial Laser Scanner Data Capturing For over a decade, TLS (Terrestrial Laser Scanner) has been the dominant method for 3d modeling and recording cultural buildings. This method is gaining popularity due to its reliability and efficiency. TLS has improved Geomatics measurement capabilities and product quality. In this study, the social club’s façades were scanned using a TOF 3D laser scanner (Leica c10). It is a pulsed laser scanner. The social club scanning process follows a circular path around the building Fig. 4. The scan stations are arranged in a closed traverse to maximize the overlap between scans. The work took two days (about 20 m away from the target) within the same scale. Nineteen scan stations for post-processing and registration were delivered. The scan resolution was set to “high” to get good resolution data and maximize storage. 3.5 Data Processing This section summarizes the processing and post-processing of data captured from the fieldwork. 3.5.1 GCPs Adjustment and Post-processing After observing GCPs on the site, the data collected as PPS (Post Process Static) was exported to the opus (Online Positioning User Service) for adjustment and postprocessing by uploading the RINEX (Receiver Independent EXchange). Later after

Automatic Co-registration of UAV-Based Photogrammetry

105

Fig. 4. Scan stations with Leica C10 device.

the adjustment of the base point started, the other GCPs observed in RTK mode were adjusted following base correction using Leica infinity software, see Fig. 5. Later, the corrected points are utilized as ground truth (error-free) reference points in the triangulation process.

Fig. 5. Processing GCPs in Leica infinity.

106

M. G. Ahmed and F. M. Abed

3.5.2 UAV Triangulation and Processing in Reality Capture (RC) RC software created photogrammetric 3D models from UAV images. The software is an all-in-one SfM-DMVR solution. It processes images and laser scans simultaneously and individually. A sparse point cloud is created to align images in RC. Many different methods exist to densify sparse points and remove large inaccuracies. There are three levels of detail (LOD) in RC software: preview, standard, and high. This produces huge (high-poly) models that conventional CPUs can’t handle. RC has mesh-simplification techniques that can reduce polygon count. When 99 photos were registered in RC, they created (573005) points, with 7 ground control points and 5 checks (ground test) points resulting in location accuracy (RMSE) equal to (0.02749) in GCPs and (0.04125) in GPTs. A dense 3D mesh with the required quality can be built up using a section of the model created by the camera positions. According to the triangle (21393482) and vertices (21393482) counts, the triangular mesh is rich in information (10723944). See in Fig. 6, the sequence of processing and final result. 3.5.3 TLS Data Processing Laser scanning has long been a research focus due to the time and effort required to produce clear and detailed point clouds. Leica cyclone register 360 software was used to register the scans delivered from fieldwork for basic processing stages; preprocessing and registration. A later version of RC was used to extract textured 3D models with real-world coordinates.

Fig. 6. The chain process of Photogrammetry in RC (a) sparse point cloud. (b) Dense point cloud using MVR methods. (c) 3D mesh. (d) Colorized 3D model.

Automatic Co-registration of UAV-Based Photogrammetry

107

4 Results and Discussion 4.1 Registration of Laser Scanning Data Leica cyclone register 360 is one of the most widely used point cloud registration software. It provides easy, guided processes, automatic registration, manual alignment, and QC report. Proven speed, professional-grade scale, one-step import and processing, professional registration reporting, powerful QA/QC, and full scan data utilization. RC helps users work smarter, provide better results, visualize better, and interact more efficiently. In our case, cloud to cloud registration requires Visual Alignment over other auto cloud (auto align) tools. To avoid misalignment, auto registration using coded targets is recommended. However, natural targets were used to align the scans in our case study. The registration precision depends on the scan overlap, scan resolution, and range distance between both the objects. Later, LM-ICP ran all the strips in one bundle simultaneously to refine the results and distributed the bundled error on all the strips. The registration error reflected the precision with which the point clouds were aligned. The process uses 19 scan stations to link the strips. The strength is 50%, representing the proportional stiffness of the link restrictions in various measured directions. The overlap is 37%, which means the proportion of points that overlap between configurations within a bundle and the link. So the cloud to cloud equal (0.007) m expresses the average accuracy of the alignment between scan strips that explain the miss-alignment, see Fig. 7.

Fig. 7. Overall quality of the registration process in Cyclone register 360.

4.2 Processing TLS Data in RC After Leica cyclone preprocessing and scanning, the data is ready to be imported into RC for further processing and modeling. RC has proven to be the best solution for extracting 3D models from 3D laser data. After adding the laser scanner data and selecting the desired settings in RC, imported laser scans were treated as standard images. Registration, meshing, texturing, coloring, and post-processing work with 2D photos. So, after 3D scene alignment, RC will show a portion of the 3D points. The next step is to assume fully-registered inputs and provide consistent data, i.e., control points, coordinates, and distances. Then the 3D model was built from laser data with the result of the work count

108

M. G. Ahmed and F. M. Abed

66 synthetic images registered to produce (266586) points using 7 ground control points and 5 checks (ground test) points, as shown in Fig. 8. After the model has been built, it can generate a dense 3D mesh with the desired quality. There is a lot of detail in the triangular mesh extracted based on (138754476) triangle counts and (69421897) vertices counts.

Fig. 8. 3D geometric model of TLS data of the social club.

4.3 Data Fusion Approach RC software is the only vendor in the world capable of registering terrestrial laser scans and UAV images. Unlike other methods, RC software uses “simultaneous bundles block modifications” of laser scan and UAV camera photos to fill in gaps and enhance the texture of the social club building. In our case for the social club building, the facades are scanned by TLS as the main data source and the UAV images as the secondary source to fill the gaps and enhance the texture. RC software uses “simultaneous bundles block modifications” of laser scan and UAV camera photos, unlike other methods. Using both datasets with GCPS in a single SfM paradigm yields exact picture orientation and sparse point clouds in WGS 84 coordinate system. To create a point cloud using 3D visual data from the synthetic pictures (or LSP files). The absolute orientation of laser data is computed using three-dimensional correspondences. The TLS coordinate system camera orientation parameters can now be used to reconstruct dense surface point clouds. This reduces user interaction and simplifies data fusion. The RC program generates a fusion data scenario from two datasets, including registration, meshing, texturing, and coloring, see Table 1.

Automatic Co-registration of UAV-Based Photogrammetry

109

Table 1. Data fusion results in RC. Procedure

Results

Procedure

Results

Registered image

150/159

No. of GTPs

5

Points count

754265

GCPs RMSE

0.02373 (m)

Total projections

1431305

GTPs RMSE

0.02346 (m)

No. of GCPs

7

Consuming alignment time

50 (min)

Table 2. Accuracy analysis results in the three investigated scenarios. Procedure

UAV

TLS

Fusion

No. of GCPs

7

7

7

No. of GTPs

5

5

5

GCPs RMSE

0.02749 m

0.02396 m

0.02373 [m]

GTPs RMSE

0.04125 m

0.02338 m

0.02346 [m]

5 Validation and Discussion of Fusion Scenario For the sake of comparison, the data fusing technique has been evaluated by comparing the photogrammetric and the laser scanning standalone approaches in order to respond to these challenges: • Evaluating each technique’s capacity to fulfill conservation requirements, such as geometric accuracy, realistic appearance, mobility, automation, and affordability. • Highlighting the benefits of combining digital photographs and laser scans for the social club development case study. 5.1 Precision Analyses Our team compared the benefits and drawbacks of terrestrial laser scanning, UAV photography, and fusion. The social club 3D point clouds were tested for roughness, density, mesh details, and measurement accuracy. Beyond that, the RC program reported acceptable re-projection errors in the fusion 3D point cloud (0.35 pixel at the average). Due to variable incidence angles and range detector signal thresholds, most Times of Flight (TOF) scanners generate noise (or roughness) during range collection. The distance between each point and the best-fitting plane can be used to assess point cloud roughness. To calculate roughness, use the Cloud Compare application’s radius-based tool. On the other hand, TLS point clouds were (0.00405) rougher than UAV-imagery point clouds. (0.00199). (0.00192). Roughness level (0.00266) for UAV-image data after combining laser scans and digital photographs (0.00266). (0.00262). A dense photogrammetric point cloud dataset is shown in Fig. 9. Laser data is smoother and more stable than UAV data. The acquisition range is longer, and the surface materials are more varied.

110

M. G. Ahmed and F. M. Abed

Figure 9 shows a mesh with an inadequate resolution created by merging UAV and laser scan data from the social club due to the high level of roughness in the UAV data sets. The material composition of the social club, the sensor’s accuracy, and the flight altitude could all be contributing factors. The TLS internal threshold is applied by the C10 signal post-processing algorithm, the range finder’s accuracy, and/or filtering processes.

Fig. 9. The mesh model of the social club (a) laser scans, (b) UAV images and (c) fusion scenario.

Cloud compare calculates densities of point clouds. It had a higher density, with a standard deviation and mean of (67713.1) and (65277.9). While UAV-imagery point clouds had a lower standard deviation (288.9), their coverage (735.2) was still less than laser data. The fusion mean (64010.4) and standard deviation (68991.7). In all three cases, color surface density maps are shown Fig. 10. TLS datasets had denser coverage than UAV imagery, despite using the usual scanning resolution, which was an option throughout the data collection session.

Fig. 10. Color map result of surface density (a) UAV-imagery, (b) TLS, (c) the fusion dataset.

Automatic Co-registration of UAV-Based Photogrammetry

111

To assess the position accuracy of the Photogrammetry, TLS, and the Fusion datasets, the same GCPs and GTPs belong to the same coordinate system (UTM/WGS 84). The RMSE of UAV–Photogrammetry for GCPs was (0.02749 m) and for GTPs was (0.04125 m). To map non-scaled point clouds produced by Photogrammetry, GCPs were used to convert them into metric coordinates. On the other hand, the TLS RMSE for GCPs was (0.02396 m), whereas for GTPs it was (0.02338 m). This may acquire as a matter of TLS recovering true-scale point clouds with high-level metric precision after assessing laser scan data by adopting GCPs measurements instead of other methods. Because of the advantages provided by the selected fusion technique, laser point clouds were used as control points in the 3D transformation to decrease the errors delivered from UAV-photogrammetry reflected through the RMSE values to be (0.02373 m) for GPCs and (0.02346 m) for GTPs. By using TLS to offer primary coverage and UAV images as secondary coverage, the combination of laser scans and digital photos has shown to be an effective way to capture buildings, see Fig. 11 for the result of the fusion scenario (Table 2).

Fig. 11. Fusion model shows the quality of the different types of facades of the social club.

6 Conclusions The possibility of co-registration of UAV images (Photogrammetry) and laser scanning in 3D digital city approaches are being investigated in this study. TLS and UAV imagery are both efficient and need little user intervention. Hence the hybrid co-registration scenario was chosen. As a result, the 3D coverage was completed. Data gaps in laser and image-based point clouds can be caused by occlusions, object complexity, lighting, and processing difficulties. By combining TLS and UAV pictures, they were able to fill in data gaps and extract additional information. UAV-Image Data Noise Reduction Data fusion has decreased the roughness of UAV pictures to a reasonable degree compared to TLS data point clouds. Adding color to laser data Color information in point clouds is lacking due to issues with the Leica C10’s integrated cameras. As a consequence,

112

M. G. Ahmed and F. M. Abed

certain colors were produced using real picture textures. UAV images should be scaled. In Photogrammetry, the pixel approach is used to construct arbitrary point clouds. With excellent metric precision, TLS restores true-scale point clouds.

References 1. Lafarge, F., Mallet, C.: Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation. Int. J. Comput. Vis. 99(1), 69–85 (2012) 2. Singh, S.P., Jain, K., Mandla, V.R.: Virtual 3D city modeling: techniques and applications. ISPRS-Int. Arch. Photogramm. Rem. Sens. Spat. Inf. Sci. 40, 73–91 (2013) 3. Barrile, V., Fotia, A., Leonardi, G., Pucinotti, R.: Geomatics and soft computing techniques for infrastructural monitoring. Sustainability 12(4), 1606 (2020) ˇ The surface 4. Ruzgien˙e, B., Berteška, T., Geˇcyte, S., Jakubauskien˙e, E., Aksamitauskas, V.C: modelling based on UAV Photogrammetry and qualitative estimation. Measurement 73, 619– 627 (2015) 5. Kersting, A.P., Zhai, R., Habib, A.: Strip adjustment using conjugate planar and linear features in overlapping strips. In: ASPRS 2008 Annual Conference (2008) 6. Ali, H.H., Abed, F.M.: The impact of UAV flight planning parameters on topographic mapping quality control. In: IOP Conference Series: Materials Science and Engineering, vol. 518, no. 2, p. 022018. IOP Publishing (2019) 7. Kadhim, I., Abed, F.M.: Investigating the old city of Babylon: tracing buried structural history based on photogrammetry and integrated approaches. In: Earth Resources and Environmental Remote Sensing/GIS Applications XII , vol. 11863, pp. 75–90, September 2021 8. Shashi, M., Jain, K.: Use of photogrammetry in 3D modeling and visualization of buildings. ARPN J. Eng. Appl. Sci. 2(2), 37–40 (2007) 9. Amat, N., Setan, H., Majid, Z.: Integration of aerial and close-range photogrammetric methods for 3d city modeling generation. Geoinf. Sci. J. 10(1), 49–60 (2010) 10. Hammoudi, K., Dornaika, F.: A featureless approach to 3D polyhedral building modeling from aerial images. Sensors 11(1), 228–259 (2010) 11. Hadi, R.H.: The Integration of Multi-Source Photogrammetric Datasets for Virtual 3D City Modeling Applications. Thesis, Middle Technical University, M.Sc (2019) 12. Li-Chee-Ming, J., Gumerov, D., Ciobanu, T., Armenakis, C.: Generation of three dimensional photo-realistic models from LiDAR and image data. In: 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH), pp. 445–450. IEEE, September 2009 13. Kedzierski, M., Fryskowska, A.: Terrestrial and aerial laser scanning data integration using wavelet analysis for the purpose of 3D building modeling. Sensors 14(7), 12070–12092 (2014) 14. Leberl, F., et al.: Point clouds : lidar versus 3D vision. Photogramm. Eng. Remote Sens. 76(12), 1123–1134 (2010) 15. Moussa, W.: Integration of digital photogrammetry and terrestrial laser scanning for cultural heritage data recording. Univ. Stuttgart, 725, 164 (2014) 16. Balsa-Barreiro, J., Fritsch, D.: Generation of visually aesthetic and detailed 3D models of historical cities by using laser scanning and digital photogrammetry. Digit. Appl. Archaeol. Cult. Herit. 8, 57–64 (2018)

Identify the Critical Risk Factors at the Tendering Phase in Iraq Marwa Makki Dishar(B) and Meervat Razzaq Altaie University of Baghdad, Department of Civil Engineering, Baghdad, Iraq {M.Dishar1901M,Meervat.r}@coeng.uobaghdad.edu.iq

Abstract. Risk factors can be considered unique in construction projects, especially in tendering phase. This research is directed to recognize and evaluate the importance of critical risk factors in the tendering phase related to Iraq’s construction project. As a rule, construction projects are impacted by risk factors throughout the project life cycle; without identifying and allocating these risk factors, the project cannot succeed. In this paper, the open and closed questionnaires are used to categorize the critical risk factors in tendering phase. Research aims to recognize the factors that influence the success of tendering phase, to determine the correct response to the risk’s factors in this research article, (IBM, SPSS, V23) package has been utilized to examine the foremost group of risk factors by Mean Score Technique (MS) and ranking from top to bottom. The risk factors of tendering phase analysis by using relative Importance Index (RII) method then ranking from top to bottom using their values. Consequences of this research article assessed that the technical criterion is the most efficient criteria at last, management criteria with all risk factors that showed in this research will be considered. Keywords: Tendering phase · Relative important index · Mean score · Risk factors

1 Introduction The risk management system showed that tendering risk assessment should be controlled in a software program and statistical programs to ensure an efficient and compresence tendering phase [1]. The tendering stage seems very confusing and tedious for these reasons, and many companies avoid it for public sector work [2]. The tendering phase is a structure process that includes the choice phase, expressions of intrigue from bidders the request for tender, and the evaluation phase [3]. Each tender phase is distinctive depending on the nature of the contract in questions and how the buyer request to evaluate the bidder [4]. It is also important to keep in mind that the bidding documents represent the documents assessed by Tender Committee with the user’s consent, which includes technical specifications, bid drawings, and appendices [5]. As part of the procurement process, the evaluation of both quality and price so that the buyer can take an informed decision at the selection phase on who is representing the best to deliver the contract [6]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 113–123, 2023. https://doi.org/10.1007/978-981-19-7358-1_11

114

M. M. Dishar and M. R. Altaie

There is no margin for error; therefore, the tender phase right must be gotten right [7]. The objectives of this study are: • Distinguished the criteria risk variables that need to be recognized, analyzed, and considered for recommending in the tendering phase. • Ranking of criteria risk factors by their values of Mean Score Technique. • Ranking the sub-criteria risk factors by Relative Important Index (RII).

2 Identification Critical Risk Factors in Tendering Phase The tendering process is distinguished as an important major activity in a construction process also, competitive tendering represents one of the most regularly employed methods to select who will be in charge of projects execution [8]. It can be defined as a process of organizing and submitting a confirmation offer for acceptance to accomplish a particular work for a price, transforming the estimation into a bid. Tendering is an act of inquiring many parties for the offer to execute a certain errand and objectively assessing the offers to choose the best contractor for that process [9]. Tender phase is where the client handles the whole acquirement process before handing the job to a selected party for execution. In this phase, the client recognizes a project that needs to be executed, seeking parties who help, sets requirements for selections, evaluates their suggestions to attain an agreement with a chosen party [10]. Tender documents should be indicated and can be separated into two parts; the primary part is the technical specifications, and the second part is the administrative conditions [11]. The technical specifications can include bills of quantities, descriptions, and general drawings. The authoritative portion mainly contains legally binding issues and other construction details related to the project [12]. Different academics use different risk factors regarding the tendering phase in construction projects because distinctive risk factors apply to distinctive neighboring regions. There are numerous most important risk factors for the tendering phase: technical, contractual, and managerial risks factors. So, the researcher prepares important risk factors from previous studies and field surveys used in this study. Table 1 shows the important risk factors in tendering phase. Table 1. Important risk factors from the previous study. No

Criteria

Sub-Criteria risk factors

Authors

1

Technical

1–1 Lack of experience of technical consultants

[13, 14]

1–2 Drawings delay

[15]

1–3 Exclusions and mistakes in the bills of quantities

[15]

1–4 Designer persistent of own idea

[16] (continued)

Identify the Critical Risk Factors at the Tendering Phase in Iraq

115

Table 1. (continued) No

2

3

Criteria

Contractual

Management

Sub-Criteria risk factors

Authors

1–5 No financial allocation for the consultant

[16]

1–6 Loss of preliminary designs

[17]

2-1Tender period

[18]

2–2 Inadequate Tender Documentation

[19]

2–3 Poor contract papers

[20]

2–4 Long period between design and bidding

[20]

2–5 Breach of contract between the contracting parties

[20]

2–6 Contractual arrangement

[21]

3–1 Loss of project files by the team

[22]

3–2 Wrong actions due to incorrect communication

[22]

3–3 Weakness in work of periodic meetings with heads of departments

[22]

3–4 Luck of knowledge in Project management techniques

[23]

3–5 Growth of better organization for the project

[24]

3 Research Methodology The methodology adopted in this study are: • Gathering the information required throughout theoretic investigation and field work regarding the anticipated theme according to a research strategy employed to catch the importance of the risk factors. • Using the process of the open survey to collect the information from experts’ collection in the fieldwork. • Using the process of closed questionnaire to assemble data from open survey and theoretical study to detect critical risk factors in tendering phase from the respondents. • Ranking both of the criteria and sub-criteria risk factors must be considered throughout the tendering process. 3.1 Distribution of Questionnaire Questionnaires that were distributed were (80) survey forms to take the sample, (69) of questionnaire forms had collected, and (11) samples have been excluded due to the lack of information. The (69) questionnaire forms have been considered and investigated based on closed questionnaire forms.

116

M. M. Dishar and M. R. Altaie

3.2 Analyzing Quantitative Data The collected quantitative data and examined by (IBM/SPSS, V23) statistical simulator. Factors have been measured for ranking by Relative Importance Index (RII) and Mean Score method. 3.2.1 Relative Importance Index (RII) Analyzing data acquired through a Relative Importance Index based on Eq. (1) to discover relative importance of significant risk factors for the tendering phase [25]. RII =

N (K=0)

((X 1 ∗ M 1 + X 2 ∗ M 2 + X 3 ∗ M 3+?Xn ∗ Mn)) (W ∗ Z)

(1)

where: RII: Relative Importance Index. RII: Equals range from (0 to 1). M: Weightgives to any factors by responders and will range (1 to 5) Here, (1) represents less importance, and (5) represents high importance. X: represent the frequency of each rating for each factor or option. W: Total amount of responses for that factor or option. Z: The maximum weights (i.e., in thiscase5). 3.2.2 Mean Score Ranking Technique The mathematical equation for computing means score for main risk criteria can be represented as [25]. n (X1 ∗ S1 + X2 ∗ S2 + X3 ∗ S3 . . . ..Xn ∗ Sn)/N (2) Ms = k=0

where: Ms = Mean Score (1 ≤ MS ≤ 5). 3.3 Validity and Reliability Test Validity and reliability consider as the most significant methodology conditions for research tools design [25]. Therefore, it must be provided the validity and reliability of the questionnaire before any statistical analyses of data. 3.3.1 Validity of Questionnaires Validity represents the degree to which an analysis action is applied to claim it easier. It is not established by solitary measurements apparatuses but via a figure of study that clarifies the interface between the test and the behavior it intends to measure. It is necessary that the test be useable for applying and interpreting the consequences precisely. Validity is equal to the (Square Root of the Coefficient of Reliability). The formula can find the validity of the questionnaire: √ (3) V= 2a

Identify the Critical Risk Factors at the Tendering Phase in Iraq

117

where: V: Validity; α: Reliability. 3.3.2 Questionnaires Reliability Reliability can be defined as measuring real outcomes and having stability and equality checks [26]. Reliability is essential but not a satisfactory constituents of tool viability; It specifies the capability of the instrument to measure the standards determinedly.

4 Discussion of Statistic Investigation IBM/SPSS-V23 statistical package is employed to test the main factors under study. For a basic and justifiable way, they have been given in tables. After allocating and gathering the questionnaire forms, the following step is to assign a stated method for statistical and the measures to accomplish calculations and data analysis. 4.1 Reliability of Questionnaires By means of (IBM/SPSS-V23), the reliability magnitude can be done by finding the value of (Cronbach’s Alpha). Most of the social study cases employ Cronbach’s alpha. The reliability coefficient of (0.70) or greater is considered "acceptable" for identifying the entire questionnaire [27]. Table 2 shows the reliability of the questionnaire. Table 2. Cronbach’s Alpha coefficient for questionnaire. No

Criteria

Cronbach’s Alpha {α}

Elements of sub-criteria

1

Technical

0.865

6

2

Contractual

0.810

6

3

Management

0.750

5

4.2 Validity of Questionnaire Table 3 show the validity of questionnaire. Table 3. Validity coefficient. No

Criteria

Cronbach’s Alpha {α}

Validity coefficient

1

Technical

0.865

0.930

2

Contractual

0.810

0.900

3

Management

0.750

0.866

118

M. M. Dishar and M. R. Altaie

4.3 Statistical Analysis and Results of Closed Questionnaire The closed questionnaire can be separated into 3 main parts. The 1st part stands for a depiction of the members of the general sample experience. The 2nd part is the criteria of risk factors, while the 3rd part of the sub-criteria risk factors. Respondents have been asked to grasp the criteria and sub-criteria of hazard factors that they are supposed to be suitable for their selection. The mean score technique and relative importance index were extracted in this research article. After questionnaire forms gathering from the respondents, the analysis and discussion of the results will be according to the parts mentioned, so each axis will be analyzed separately and discussed. 4.3.1 Part One Part one includes the personal information of the closed questionnaire. a) Job Work Title (Fig. 1 shows job work title of respondents).

15% Experts

4% Academic 6% Consults

22% Cheif Engineers

10% Contractors

43% Engineers

Fig. 1. The job work title of respondents

b) Academic Degree (Fig. 2 shows the academic degree for respondents).

PhD 13%

MSc 22% BSc 56% High Diploma 9%

Fig. 2. The academic degree for the respondents.

Identify the Critical Risk Factors at the Tendering Phase in Iraq

119

c) Engineering Field (Fig. 3 show the engineering field for respondents).

Mechnical 11%

Electrical 15%

Architect 18%

Civil 56%

Fig. 3. The engineering field for the respondents.

d) Years of Experience (Fig. 4 show the percentage years of experience).

(25-16) %43

(10-6) %22

(15-11) %10

Fig. 4. The years of experience for the respondents.

4.3.2 Part Two The 2nd part of the question is a request for assessing the three main criteria upon the tendering phase, which got from the hypothetical work. The assessment procedure can be done using the mean score technique by applying Eq. 2. Table 4 shows the ranking criteria.

120

M. M. Dishar and M. R. Altaie Table 4. Ranking criteria. No

Criteria of risk factors

Mean

1

Technical

3.50

2

Contractual

3.45

3

Management

3.30

4.3.3 Part Three Analysis of risk factors concurring to relative importance index (RII) using Eq. 1. Criteria risk factors will be divided into three sub-criteria mentioned above in Table 1. Tables 5, 6, and 7 show the final ranking (RII). Table 5. Ranking technical criteria of risk factors by RII. No

Criteria

Sub-criteria risk factors

RII

Ranking

1

Technical

1–1 Lack of experience of technical consultants

90%

1

1–2 Drawings delay

85%

2

1–3 Exclusions and mistakes in the bills of quantities

80%

3

1–4 Designer persistent of own idea

75%

4

1–5 No financial allocation for the consultant

73%

5

1–6 Loss of preliminary designs

70%

6

Table 6. Ranking contractual criteria of risk factors by RII. No

Criteria

Sub-criteria risk factors

RII

Ranking

2

Contractual

2–1 Poor contract papers

89%

1

2–2 Long period between design and bidding

84%

2

2–3 Breach of contract between the contracting parties

80%

3

2–4 Contractual arrangement

79%

4

2–5 Breach of contract between the contracting parties

75%

5

2–6 Inadequate Tender Documentation

73%

6

Identify the Critical Risk Factors at the Tendering Phase in Iraq

121

Table 7. Ranking management criteria of risk factors by RII. No Criteria 3

Sub-criteria risk factors

RII

Ranking

Management 3–1 Luck of knowledge in project management techniques 88% 1 3–2 Growth of better organization for the project

84% 2

3–3 Wrong actions due to incorrect communication

74% 3

3–4 Weakness in the work of periodic meetings with heads 72% 4 of departments 3–5 Loss of project files by the team

71% 5

5 Results Discussion and Summary In this study, critical risk factors in tendering phase rely on many criteria such as technical, contractual, and management. Each criterion has a sub-criteria risk factor. This study established three criteria and seventeen sub-criteria, compared with various similar studies, held in identical environmental and discovered that the risk factors have been exceptionally minimal. The investigation of the criteria conducts by cruel score technique (MS) and using the (SPSSV23) and ranking from higher to lower. The percentage of technical criteria was (3.50), which means these criteria are essential and related to the method of implementing the construction project and the technical plans, construction specifications, and details of each paragraph of the construction project. The percentage of contractual criteria was (3.45). This means that there is a lack in the method of documenting the contract, as well as the general and specific conditions of the tender, and the failure to choose the appropriate method for selecting the bid. The percentage of management (3.30) means there is a weakness in the organizational structure of government institutions, weak communication between the project team and senior departments, and delays in making decisions. The sub-criteria of the risk factors analysis by relative importance index (RII) and (SPSSV23) and ranking these factors from top to bottom. This study conducts a set of criteria of critical risks factors mentioned in earlier studies and field works. The main purpose of an existing study is to identify the critical risk factors at the tendering phase. As well as a weakness in tendering process in these authorities mentioned above was also illustrated.

References 1. Issa, U.H., Farag, M.A., Abdelhafez, L.M., Ahmed, S.A.: A risk allocation model for construction projects in Yemen. Civil Environ. Res. 7(3), 78–88 (2015) 2. Samson, S., Reneke, J.A., Wiecek, M.M.: A review of different perspectives on uncertainty and risk and an alternative modeling paradigm. Reliab. Eng. Syst. Saf. 94(2), 558–567 (2009) 3. Smith, N.J., Merna, T., Jobling, P.: Managing Risk in Construction Projects. John Wiley and Sons (2014) 4. Cooper, D.F., Grey, S., Raymond, G., Walker, P.: Project Risk Management Guidelines: Managing Risk in Large Projects And Complex Procurements. John Wiley & Sons, Inc. (2021)

122

M. M. Dishar and M. R. Altaie

5. Suherman, S.A.: Tips Jitu Menang Tender Menjadi Pemenang Sebelum Tender Dimulai. Cetakan 1. Yogyakarta: Media Pressindo (2010) 6. House, A.G., et al.: Identifying, assessing, and evaluating risks affecting the educational construction projects in Saudi Arabia. Int. J. Proj. Manage. 25(6), 105–116 (2013) 7. Augustine, I.E., Ajayi, J.R., Ade, B.A., Edwin, A.A.: Assessment of risk management practices in Nigerian construction industry: toward establishing risk management index. Int. J. Pure Appl. Sci. Technol. 16(2), 20 (2013) 8. Loosemore, M.: Essentials of Construction Project Management. UNSW Press (2003) 9. Ghahramanzadeh, M.: Managing Risk of Construction Projects: A Case Study of Iran (Doctoral Dissertation, University of East London) (2013) 10. Baloi, D., Price, A.D.: Modelling global risk factors affecting construction cost performance. Int. J. Project Manage. 21(4), 261–269 (2003) 11. Zou, P.X.W., Zhang, G., Wang, J.: Understanding the key risks in construction projects in China. Int. J. Proj. Manag. 25(6), 601–614 (2007) 12. Elhag, T.M.S., Boussabaine, A.H., Ballal, T.M.A.: Critical determinants of construction tendering costs: quantity surveyors standpoint. Int. J. Project Manage. 23(7), 538–545 (2005) 13. Dikmen, I., Birgonul, M.T., Han, S.: Using fuzzy risk assessment to rate cost overrun risk in international construction projects. Int. J. Project Manage. 25(5), 494–505 (2007) 14. Tah, J.H.M., Carr, V.: Towards a framework for project risk knowledge management in the construction supply chain. Adv. Eng. Softw. 32(10–11), 835–846 (2001) 15. Roger, F., George, N.: Risk management and construction. Blackwell Sci. (1993) 16. Wang, S.Q., Dulaimi, M.F., Aguria, M.Y.: Risk management framework for construction projects in developing countries. Constr. Manag. Econ. 22(3), 237–252 (2004) 17. Ghosh, S., Jintanapakanont, J.: Identifying and assessing the critical risk factors in an underground rail project in Thailand: a factor analysis approach. Int. J. Project Manage. 22(8), 633–643 (2004) 18. Enshassi, A., Mohamed, S., Abdel-Hadi, M.: Factors affecting the accuracy of pre-tender cost estimates in the Gaza Strip. J. Constr. Dev. Countries, 18(1) (2013) 19. El-Sayegh, S.M.: Risk assessment and allocation in the UAE construction industry. Int. J. Project Manage. 26(4), 431–438 (2008) 20. Kissi, E., Adjei-Kumi, T., Badu, E., Boateng, E.B.: Factors affecting tender price in the Ghanaian construction industry. J. Financ. Manag. Property Constr. (2017) 21. Mahamid, I.: Critical determinants of public construction tendering costs. Int. J. Archit. Eng. Constr. 7(1), 34–42 (2018) 22. Omran, A., Hooi, L.B.: Determining the critical factors in ensuring the accuracy of cost estimate in obtaining a tender. Acta Technica Corviniensis-Bull. Eng. 11(3), 23–26 (2018) 23. Bakr, G.A.: Identifying crucial factors affecting accuracy of cost estimates at the tendering phase of public construction projects in Jordan. Int. J. Civ. Eng. Technol. 10(1), 1335–1348 (2019) 24. Mohamed, K.L., Hesham, A.O.: Risk management for multinational collaborations: application on the case study of grand egyptian museum. emerging trends in construction organisational practices and project management knowledge areas. In: 9th cidb Postgraduate Conference (2016) 25. Mohammed, N., Saeed, N., Hasan, A.S.: The effect of total quality management on construction project performance. J. Sci. Technol 17(2), 11–30 (2012)

Identify the Critical Risk Factors at the Tendering Phase in Iraq

123

26. Frimpong, Y., Oluwoye, J., Crawford, L.: Causes of delay and cost overruns in construction of groundwater projects in a developing countries; Ghana as a case study. Int. J. Project Manage. 21(5), 321–326 (2003) 27. Princy, J.D., Shanmugapriya, S.: A probabilistic fuzzy logic approach to identify productivity factors in Indian construction projects. J. Constr. Eng. Proj. Manag. 7(3), 39–55 (2017)

Utilizing Delphi Technique and Bootstrap to Determine the Maximum Cost Reduction in Serial Tendering for School Construction Works Arshed A. Mohammed(B) and Kadhim R. Erzaij Civil Engineering Department, University of Baghdad, Baghdad, Iraq {a.mohammed1901m,kadhim69}@coeng.uobaghdad.edu.iq

Abstract. Construction prices and labor qualifications continue to rise, forcing the sector to innovate. The selection of the appropriate tenders for building contracts is one of the essential variables that determine the project’s success. This study was done for serial contracts in the building business. Several governmental institutions awarded similar projects through open tendering for the same specifications and design. Open tendering can be replaced with continuous and serial tendering, which will reduce expenses and hence the money required from the public state budget in the long run. Ten similar school construction projects in the Baghdad governorate were studied. The project had divided into eight main parts. After that, a comparison was made between the open tender and the serial tender process. There was a 13.7% reduction in the bid cost of the project value when using serial and continuous tenders. Also, the Delphi technique was used to present the study case results to fifteen experts to know the maximum possible reduction when using serial tenders. It was found that the maximum possible reduction is 17.5% of the project value. Finally, Monte Carlo simulation bootstrapped was used to produce random variables, where a thousand samples were created from the original samples using the (SPSS) program. The reliability of the results that support reducing the tender cost was proven when using serial tendering by comparing the averages of its variables with open bidding. Keywords: Continuity tendering · Delphi technology · Simulation · Bootstrap · Serial tendering · Repetitive projects · Construction management

1 Introduction Serial tendering is one of the types of tenders used for continuous and repeated construction projects, where the contracting is done with the contractor in the form of sequential stages. It is indicated to him in this tender that the contract will be made in several continuous and sequential stages, meaning that the subsequent stages are not started Except after completing the first phase and handing it over to the beneficiary and so on for the rest of the stages. The beneficiary party can cancel the later stages of the work chain when the contractor does not comply with the required requirements and specifications, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 124–138, 2023. https://doi.org/10.1007/978-981-19-7358-1_12

Utilizing Delphi Technique and Bootstrap to Determine

125

which motivates the contractor to improve the quality of work to win and continue the other stages. Continuity bidding saves us from losses and eventually reduces the tender cost [1]. Working with this tendering will improve the contractor’s ability to face and solve the problems of the project stages [2, 3]. This will support the construction industry by increasing the correct planning in repetitive projects, which will be reflected in shortening the completion period of the successive stages of work [4]. Recently, the analysis of project activities has been done using continuity and serial tendering instead of open tenders using the standard standardized guide of the Iraqi Ministry of Planning. This guide has been employed to analyze the work activities and the required quantities of materials, labor, and financing through open interviews with implementing contractors, banks, and all parties involved in the implementation of the work. In this study, Delphi technology was used by fifteen experts with more than 15 years of experience in the construction industry to review and match the results and determine the maximum amount to be reduced [5]. Furthermore, Delphi technology has been used to present the case study results and express an opinion on achieving the most significant reduction in the bid cost during the iteration of the same project using continuity and serial tendering [6]. The case study chose the schools in the province of Baghdad; Each primary school has 18 classes, which are similar in terms of design and bill of quantities. Simulation is a quantitative method for describing a system or process through the construction of a model that is exposed to a series of tests to ascertain its behavior over time. The SPSS program was utilized in our investigation. It may be employed as a support technique for mathematical solution approaches and for verifying the outcomes of mathematical analysis. A simulation model’s input variable’s underlying distribution is often unavailable. This might be due to the original distribution’s complex form (non-convex or multimodal), data scarcity (destructive tests or expensive data), etc. It may only have a few previous input parameter values [7]. If so, bootstrapped Monte Carlo simulation can be used to produce random variates [8]. Bootstrapping doesn’t make random variables. Considering that the bootstrap was just 20 samples, they are not significantly different from one another. The outcome would be better if we had more data points to bootstrap [9, 10]. The project’s components were divided into variables whose percentage of the project value changes with repetition. It is such as (designs, drawings, supervision, procurement tender documents, engineering insurance, amount reserve, banking services, administrative expenses, raw material supply and purchase, implementation, and profits). The set of constants with the percentage of the project value does not change with repetition. It comprises (taxes, engineering stamps, postage stamps).

126

A. A. Mohammed and K. R. Erzaij

2 Delphi Technical Procedures and Identification Experts Delphi technology has been used to focus on the elements and requirements that affect cost reduction when using serial tenders in the construction industry, as all the influencing factors were identified through the Interviews. Interviews and analysis of documents and data used in the case study have been examined accurately and in-depth. Then, the experts with more than 15 years of experience in contract management had selected. Experts have agreed to participate in expressing ideas, presenting their expertise and criticism, and improving the required percentages used in continuous and serial bids to reduce the cost of bidding. The results of the survey were distinguished via internal Delphi style into the following categories: a. Anonymity: A team of experts was selected to participate in a study that is not of a group nature, using the Delphi technique because they do not know each other. This is to enable experts to express their opinion voluntarily without being affected by the opinions of others. This feature can reduce bias effects by individuals with control and influence, group pressures, and irrelevant communications. b. Interaction: Although the experts were selected randomly, their responses were similar to the instrument through this technique. These techniques allow all comments, opinions, and stats to be briefly included in upcoming rounds. All experts had asked to respond or agree to other views and confirm their beliefs. This process is repeated until an agreement can be obtained from all experts. c. Moderated Comments (Controlled Feedback): This feature emphasizes the neutral nature of expert reviews. Every review should have been comprehensive, so there is no repetition of a more robust and influential specialized individual. d. Statistics Comments: Each expert’s responses are analyzed using statistical analysis that emphasizes general characteristics compared to scores. Thus, the statistic used an Arithmetic mean. All these features should be implemented as soon as possible to obtain expert approval. This agreement is unique in its efforts to incorporate various ideas, opinions, and expert opinions toward their consent. All the information was collected fairly and nonbiased to reach the most precise and realistic results possible. Experts had been contacted ahead of time, and their approval had been obtained. This is to ensure the sustainability of their commitment until the end of the last round of Delphi technology. 2.1 First Session According to Table 1, all requirements had been sent to the experts. They are allocated ten days. The researcher has collected and revised the answers in approximately ten days periods. However, the researcher has faced challenges for a small number of specialists due to the backlog. The data were then analyzed; The researcher paid attention to all the comments and recommendations from the experts, and then the content was revised for further review. There are some minor problems with the first round, especially some experts’ commitment. This issue has been resolved as there was a noticeable delay. In this session, the experts held slightly divergent viewpoints. There were several inquiries

Utilizing Delphi Technique and Bootstrap to Determine

127

regarding the details of the case study for the researcher, concerns about the locations of these schools and their distance from the governorate center, and they had other general questions.

No. 1 2 3 4

5 6 7 8 9 10

financial invoices

Table 1. Project activities that are affected by the using serial tendering. Activity Designs and drawings Supervision Purchasing tender documents Tax's Engineering insurance Engineering stamp Postage stamp Reserve amount (contingency) Banking services

Administrative expenses Cost of supply raw material and procurement Execution (implement) Profits Total cost Max. reduction in future

Serial tendering (%) 1.12% 1.25% 0.01% 3.3% 0.04% 0.05% 0.03% 8% 0.5% 6.5% 34% 23% 8.5% 86.3% 13.7%

After answering their inquiries, this session was conducted. The course and results of the session were as follows: a. Nine experts did not agree with the fourth paragraph about the financial invoices reserve amount (contingency). The objection was that the percentage of reserve amount orders would be less than (8%) as shown in Table 2. The subsequent works will be the same. The increase in the contractor’s experience will decrease the amount allocated to this paragraph. b. Eight experts are concerned about the fifth paragraph of Table 2 regarding administrative charges. The objection was that the contractor would take advantage of the requirements of these items in the mobilization to move them to other places, and the percentage of reduction would be greater. Therefore, the amount allocated to them would decrease. c. Two experts objected to the ninth paragraph of Table 2 regarding the percentage of profits. Their idea is that the contractor, no matter the volume of work increases, does not reduce. The project’s earnings rate is because of obligations regarding financing, project management requirements, and other conditions. Keeping the percentage of profits without reducing it when repeating the tender is recommended. 2.2 Second Session The process of the first round continues into the second round. However, each ethical content item is accompanied by a median aspect value of experts’ responses analysis

128

A. A. Mohammed and K. R. Erzaij

in the first round. To this end, the experts’ opinions can be compared with those of other experts. The time allotted for the second round is ten days. The researcher started collecting data that has been revised in the past ten days. In this session, the experts’ views on the first session’s findings were gathered, and they gave the stage’s average results, which were as follows: a. Based on the findings and expert opinion, the percentage in the fourth paragraph relating to the reserve amount for financial bills (emergency) has decreased from 8% to 6.2%. b. Based on the findings and expert opinion, the proportion in the fifth paragraph relating to administrative expenditures has decreased from 6.5% to 4%. c. Based on the findings and expert opinion, the percentage in the ninth paragraph related to profits has increased from 8.5% to 9%. At the end of the second session, the above results shown in Table 2 were presented to the experts on the results of the changes in this round. Table 2. Project activities that are affected by the using serial tendering. Activity

1 2

Designs and drawings Supervision Purchasing tender documents Tax's Engineering insurance Engineering stamp Postage stamp

3

4

5

financial invoices

No.

Reserve amount (contingency) Banking services

New serial Refusal able Serial tendering Acceptable and select what tendering (%) unanimity (%) is true 1.12% Accept 1.12% 1.25% Accept 1.25% 0.01%

Accept

0.01%

3.3%

Accept

3.3%

0.04%

Accept

0.04%

0.05%

Accept

0.05%

0.03%

Accept

8%

Non accept

0.5%

Accept

0.03% 6.2% Change after unanimity

6.2% 0.5%

4% Change after Non accept unanimity

Administrative expenses

6.5% 34%

Accept

34%

7

Cost of supply raw material and procurement Execution (implement)

23%

Accept

23%

8

Profits

8.5%

Non accept

9

Total cost percentage Maximum reduction for the future

86.3%

82.5%

13.7

17.5%

6

10

9% Change after unanimity

4%

9%

Utilizing Delphi Technique and Bootstrap to Determine

129

2.3 Third Session The method of the second round is repeated till the third round. However, each content component is accompanied by an element of mean value derived from the prior second round’s study of expert replies. To this end, experts’ opinions can be compared with those of other experts. The time allotted for the third round is ten days. Finally, the researcher collected the data reviewed in the past days. In this session, the experts gathered the results shown in Table 2 and stated that it is impossible to reduce the bid cost by more than 17.5% as a maximum. Thus, the sessions were closed, and the above results were drawn and approved. It was determined, as shown in Fig. 1. The new equation had derived under the final results of the Delphi technique. Table 3 shows the data for the case study up to 10 iterations, and by extracting the maximum reduction (17.5) using Delphi technique, the curve was drawn in Fig. 1, and from this curve, the equation was derived, the details of which are shown below: D = 6.2595 ln (N) − 1.1456 when 1 < N < 20

(1)

D = 17.5 When N > = 20, D = Reduction Cost Percentage, N = Repetitive tendering. 20

Reducon Cost percentage (D)

18 16 14 12 10 8 6 4

Reducon Cost percentage (D) when tender are Repeve for (N)

2

Reducon percentage (D) when tender are repeve for (N)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Repeve tendering

Fig. 1. Future results of serial tenders according to Delphi technology.

130

A. A. Mohammed and K. R. Erzaij

3 Simulation of Reduced Cost in Serial Tendering A simulation tool, the SPSS software version 25, was used to create the requisite data and to compare the usage of serial tendering versus open tendering, as shown below: a. It is a method to guarantee that the statistical findings are more accurate and dependable, especially if the data are not subject to a normal distribution. Substitute samples are taken from the original model and rotated with the SPSS (bootstrap) program to extract 1000 samples from the original sample [10]. The original sample was taken from twenty iterations (Ten iterations were calculated from the case study of ten schools and ten from Equation No. 1 extracted from Delphi technique), as shown in Table 3. The results have been tested based on 1000 samples. If the results of the substitution procedure are within 1000 times of the original sample’s findings, that indicates that the results can be accepted. However, if the findings are extremely erroneous and the standard error level is substantial, this verifies the program’s unreliability. b. One thousand new databases were created from the original data, and the probability value was compared to the level of significance (significance) using the (t) test. It was determined that the probability value is less than (significance), indicating the existence of statistically significant differences between serial and open tendering. Table 3. Serial bidding cost distribution to project activities. Number repetitive tendering

Designs and drawings

Supervision

Purchasing tender documents

Engineering insurance

Reserve amount and Contingency

Banking services

Administrative expenses

Supply raw material and procurement

Implement

Profits

1

50.000

31.250

0.250

0.636

127.149

12.715

108.077

445.022

317.873

134.142

2

80.000

62.500

0.468

1.238

247.690

23.778

209.545

886.740

629.138

261.511

3

103.333

93.750

0.662

1.817

363.498

33.657

306.281

1326.091

935.669

384.029

4

123.333

118.750

0.840

2.380

475.946

42.697

399.657

1763.762

1238.841

503.103

5

141.333

143.750

1.004

2.929

585.777

51.082

490.415

2200.125

1539.395

619.494

6

158.000

168.750

1.159

3.467

693.473

58.934

579.039

2635.421

1837.815

733.697

7

173.714

193.750

1.304

3.997

799.369

66.336

665.862

3069.816

2134.435

846.054

8

188.714

209.375

1.442

4.518

903.687

73.343

751.108

3503.422

2429.476

956.795

9

203.159

225.000

1.573

5.033

1006.631

80.006

834.980

3936.342

2723.144

1066.127

10

217.159

240.625

1.698

5.542

1108.351

86.364

917.627

4368.649

3015.587

1174.204

11

230.795

256.250

1.823

6.050

1197.222

92.721

980.374

4800.956

3308.030

1288.638

12

244.128

271.875

1.948

6.559

1284.589

99.079

1041.341

5233.263

3600.473

1403.072

13

257.205

287.500

2.073

7.068

1361.742

105.436

1100.670

5665.570

3892.916

1517.507

14

270.062

303.125

2.198

7.576

1446.445

111.794

1158.482

6097.877

4185.359

1631.941

15

282.729

318.750

2.323

8.085

1529.956

118.151

1214.882

6530.184

4477.802

1746.375

16

295.229

334.375

2.448

8.593

1612.352

124.509

1269.962

6962.491

4770.245

1860.809

17

307.582

350.000

2.573

9.102

1693.700

130.866

1323.800

7394.798

5062.688

1975.243

18

319.804

365.625

2.698

9.611

1774.060

137.224

1376.469

7827.105

5355.131

2089.678

19

331.910

381.250

2.823

10.119

1853.486

143.581

1428.031

8259.412

5647.574

2204.112

20

343.910

396.875

2.948

10.628

1932.318

149.939

1478.891

8691.719

5940.017

2318.546

Note that all numbers for the above activities are in million Iraqi dinars (MIQ).

Utilizing Delphi Technique and Bootstrap to Determine

131

c. As a result of the earlier data, we found that serial tendering is better than the open tendering method in reducing costs, as shown in Table 4.

Table 4. T-test to compare the two types of tenders (Serial tendering and Open tendering) using the Bootstrap method. Groups Open tendering Serial tendering

Bootstrap** Parameter

Statistic

Bias

Std. Error 95% Confidence interval of the differenceLower Lower

Upper

Designs and drawings (Open tendering) Designs and drawings (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 50.000 0.000 0.000

0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 17.195 8.939 1.998

0.036 – 0.835

1.984 3.258

14.399 2.961

21.016 12.887

Engineering insurance (Open tendering) Engineering insurance (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 31.250 0.000 0.000

0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 19.843 6.190 1.384

– 0.016 – 0.232

1.400 0.883

0.008 0.008

22.507 7.081

Supervision (Open tendering) Supervision (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 0.250 0.000 0.000

0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 0.147 0.036 0.008

0.000 – 0.001

0.008 0.0082

0.133 0.019

0.165 0.046

N Mean Std. Deviation Std. Error Mean

20 0.635 0.000 0.000

0.000 0.000

0.000 0.000

–

–

Engineering insurance (Open tendering) Engineering insurance (Serial tendering)

(continued)

132

A. A. Mohammed and K. R. Erzaij Table 4. (continued)

Groups Open tendering Serial tendering

Bootstrap** Parameter

Statistic

Bias

Std. Error 95% Confidence interval of the differenceLower Lower

Upper

N Mean Std. Deviation Std. Error Mean

20 0.531 0.036 0.0082

– 0.0002 0.0081 – 0.002 0.0088

0.517 0.018

0.547 0.047

Reserve amount and Contingence (Open tendering) Reserve amount and contingence (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 127.149 0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 96.615 15.864 3.547

– 0.0074 3.561 – .583 1.629

90.526 13.456

103.042 17.043

Banking services (Open tendering) Banking services (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 12.714 0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 7.496 1.837 0.410

– 0.0093 0.395 – 0.096 0.412

6.803 0.990

8.236 2.340

Administrative expenses (Open tendering) Administrative expenses (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 108.076 0.000 0.000

0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 73.944 19.341 4.324

– 0.218 – 0.593

3.966 1.638

66.666 16.735

81.214 20.686

Supply raw material and procurement (Open tendering) Supply raw material and procurement (Serial tendering)

N Mean Std. Deviation Std. Error Mean

20 445.021 0.000 0.000

0.000 0.000

0.000 0.000

–

–

0.000 0.000

0.000 0.000

(continued)

Utilizing Delphi Technique and Bootstrap to Determine

133

Table 4. (continued) Groups Open tendering Serial tendering

Implement (Open tendering) Implement (Serial tendering)

Profits (Open tendering) Profits (Serial tendering)

Total price with constants (Open tendering) Total price with constants (Serial tendering)

Bootstrap** Parameter

Statistic

Bias

Std. Error 95% Confidence interval of the differenceLower Lower

Upper

N Mean Std. Deviation Std. Error Mean

20 434.585 3.674 0.821

– 0.020 – 0.206

0.813 0.850

433.238 2.055

436.126 4.676

N Mean Std. Deviation Std. Error Mean

20 317.872 0.000 0.000

0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 297.000 7.349 1.643

– 0.039 – 0.428

1.591 1.661

294.148 4.139

300.364 9.336

N Mean Std. Deviation Std. Error Mean

20 134.142 0.000 0.000

0.000 0.000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 115.927 6.010 1.343

0.003 – 0.33

1.319 1.543

113.645 3.039

118.647 8.013

N Mean Std. Deviation Std. Error Mean

20 0.000 1271.491 0.000 .0000 .0000

0.000 0.000

–

–

N Mean Std. Deviation Std. Error Mean

20 – 0.650 1107.667 3.356 64.391 14.398

14.264 11.273

1079.777 1137.881 44.497 75.876

** Bootstrap results are based on 1000 bootstrap samples.

Note, that all number for the above activities are in million Iraqi dinars (MIQ).

3.1 Designs and Drawings As shown in Table 5, the probability value of the t-test is equal to (0.000), and this value is less than (0.05) limit of significance. This shows that statistically significant differences exist between (Serial tendering and Open tendering) in the cost of (Designs and drawings), as the arithmetic mean for (Designs and drawings) when using Serial tendering, it equals (17.195 MIQ), while in the case of using Open tendering, the average

134

A. A. Mohammed and K. R. Erzaij

Table 5. Using the bootstrap approach by t-test for equality of Means in cost distributions for open and serial tendering. Groups (Open tendering) (Serial tendering)

Independent samples test t

df

Sig.

Bootstrap for independent samples test**

Designs and drawings

16.411

19

0.000

32.804

1.998

28.620

36.988

– 0.036

1.984

0.001

27.465

36.142

Supervision

8.241

19

0.000

11.406

1.384

8.509

14.303

0.016

1.400

0.001

8.681

14.062

Purchasing tender documents

12.701

19

0.000

0.102

0.008

0.085

0.119

– 0.000

0.008

0.001

0.084

0.116

Engineering insurance

12.701

19

0.000

0.104

0.008

0.087

0.121

0.000

0.008

0.003

0.086

0.120

Reserve amount contingence

8.607

19

0.000

30.533

3.547

23.108

37.958

0.007

3.561

0.001

22.569

37.956

12.701

19

0.000

5.217

0.410

4.358

6.077

0.0093

0.395

0.001

4.370

5.966

7.892

19

0.000

34.132

4.324

25.080

43.184

0.218

3.966

0.001

24.837

42.695

Supply raw material and procurement

12.701

19

0.000

10.435

0.821

8.716

12.155

0.020

0.813

0.001*

8.554

12.017

Implement

12.701

19

0.000

20.871

1.643

17.432

24.311

0.039

1.591

0.001

17.290

23.881

Profits

13.554

19

0.000

18.215

1.343

15.402

21.027

– 0.003

1.319

0.001

15.284

20.615

Total price with constants

11.378

19

0.000

163.824

14.398

133.688

193.960

14.264

0.001

134.850

191.108

Mean difference

Std. Error difference

95% Confidence interval of the difference Lower

Banking services Administrative expenses

Bias

Std. Error

Sig

Upper

95% Confidence interval of the difference Lower

0.649

Upper

Note that all numbers for the above activities are in a million Iraqi dinars (MIQ).

is (50 MIQ) as shown in Table 4. From the prior information, we conclude that using the Serial tendering method is better than the open tendering method in reducing the cost of (Designs and drawings) by Very large. 3.2 Supervision The probability value of the t-test is equal to 0.000, as indicated in Table 5, which is less than the (0.05) level of significance. This demonstrates statistically significant variations in the cost of supervision between (Serial tendering and Open tendering). As the arithmetic mean of (Supervision) is equal to (19.843 MIQ) when serial bidding is used, and the average is equal to (31.250 MIQ) when open tendering is used, as shown in Table 4. Therefore, suggest that using the Serial tendering method is better than the Open tendering method in reducing the cost of the “Supervision” by a good percentage. 3.3 Purchasing Tender Documents As indicated in Table 5, the t-test has a probability value of 0.000, which is less than the (0.05) level of significance. This demonstrates that there are statistically significant

Utilizing Delphi Technique and Bootstrap to Determine

135

variations in the cost of procuring tender papers between (serial and open tendering) when serial tendering is used, as the arithmetic mean of (Purchasing tender documents) equals (0.147 MIQ). The average for those who use open tendering is (0.250 MIQ) as shown in Table 4. It was concluded that using the Serial tendering method is better than the Open tendering method in reducing the cost of (Purchasing tender documents). 3.4 Engineering Insurance As shown in Table 5, the probability value of the t-test is equal to 0.000, and this value is less than (0.05) limit of significance. This shows that statistically significant differences exist between (Serial tendering and Open tendering) in the cost of (Engineering Insurance) when serial tendering is used, since the arithmetic mean of (Engineering Insurance) equals (0.531 MIQ). When open tendering is used, the average is (0.635 MIQ) as shown in Table 4. It was concluded that using the Serial tendering method is better than the Open tendering method in reducing the (Engineering Insurance) cost. 3.5 Reserve Amount and Contingence Table 5 shows that the probability value of the t-test is 0.000 and this value is less than the (0.05) limit of significance. This means that there are statistically significant differences between (Serial tendering and Open tendering) in the cost of Reserve Amount Contingency between them, as the arithmetic mean of (Reserve Amount Contingence) while using Serial tendering is (96.615 MIQ), however, the average when utilizing Open tendering is (127.149 MIQ) as shown in Table 4. Hence, it was concluded that using the Serial tendering method is better than the Open tendering method in reducing the cost of the (Reserve Amount Contingence). 3.6 Banking Services As shown in Table 5, the probability value of the t-test is equal to 0.000, and this value is less than (0.05) limit of significance. This shows that statistically significant differences exist between (Serial tendering and Open tendering) in the cost of (Banking Services) when serial tendering is used since the arithmetic mean of (Banking Services) equals (7.496 MIQ). In comparison, when open tendering is used, the average is (12.714 MIQ) as shown in Table 4. It was concluded that utilizing the Serial tendering approach is superior to using the Open tendering method to decrease the cost of (Banking Services) by a large amount. 3.7 Administrative Expenses As shown in Table 5, t-test probability values of (0.000) and less than (0.05) suggest that there are statistically significant differences in administrative expenditures between serial tendering and open tendering when the arithmetic mean of (Administrative expenditures) is equal to (73.944 MIQ). When open tendering is used, the average is (108.076 MIQ) as shown in Table 4. Then, it was concluded that using the Serial tendering method is better than the Open tendering method in reducing the cost of the (Administrative expenses) by a good percentage.

136

A. A. Mohammed and K. R. Erzaij

3.8 Supply Raw Material and Procurement As shown in Table 5, the probability value of the t-test is 0.000, which is less than the (0.05) level of significance, indicating that statistically significant differences exist between the groups (Serial tendering and Open tendering) in the cost of (Supplying raw material and procurement), as the arithmetic mean of (Supply raw material and procurement) when using Serial tendering is equal to (434.585 MIQ), while in the case of using Open tendering, the average is (445.021 MIQ) as shown in Table 4. So, it was concluded that using the Serial tendering method is better than the Open tendering method in reducing the cost of the (Supply of raw material and procurement) by a respectable percentage. 3.9 Implement As revealed in Table 5, the probability value of the t-test is equal to 0.000, and this value is less than (0.05) limit of significance. This shows that statistically significant differences exist between (Serial tendering and Open tendering) in the cost of (Implement), as the arithmetic mean of (Implement) when using Serial tendering is equal to (297 MIQ), while in the case of using Open tendering, the average is (317.872 MIQ) as shown in Table 4. In conclusion, advocate that using the Serial tendering method is better than the Open tendering method in reducing the cost of the (Implement) by a decent percentage. 3.10 Profits As seen in Table 5, the probability value of the t-test is equal to 0.000, which is less than (0.05) limit of significance. This shows that statistically significant differences exist between (Serial tendering and Open tendering) in the cost of (Profits), as the arithmetic means of (Profits). When using Serial tendering is equal to (115.927 MIQ), while in the case of using Open tendering, the average is (134.142 MIQ), as shown in Table 4. So, it was concluded that using the Serial tendering method is better than the Open tendering method in reducing the (Profits) by a great percentage. 3.11 Total Price with Constants As displayed in Table 5, the probability value of the t-test is equal to 0.000. This value is less than (0.05) limit of significance. This shows that statistically significant differences exist between (Serial tendering and Open tendering) in the cost of (Total Price with constants). The arithmetic means of (Total Price with constants) when using Serial tendering is equal to (1107.667 MIQ), while in the case of using Open tendering, the average is (1271.491), as shown in Table 4. Hence, we conclude that using the Serial tendering method is better than the Open tendering method in reducing the cost of the (Total Price with constants) by a notable percentage.

Utilizing Delphi Technique and Bootstrap to Determine

137

4 Conclusions The results, it was concluded that the maximum allowable reduction is 17.5% in the total cost, as follows: First, the results were collected by examining studies of ten schools and using Delphi technology. It has been summarized that the decrease in the project’s total cost increases with the number of bids repeated up to ten times. Following that, the percentage decreases as the number of bids increases until it reaches the maximum reduction value, 17.5% of the bid value when bids have been repeated more than 20 times, as seen in Fig. 1. Second: Verification using simulation model the tool, the SPSS software, version 25, has used to create the requisite data and to compare the usage of serial tendering versus open tendering, as shown: a. It was determined that the probability value is less than (significant), indicating statistically significant differences between serial bidding and open bidding. b. As a result of the above, the study found serial tendering is better than the open tendering method in reducing costs. As shown in Table 5. c. As shown in Table 5, the probabilistic value of the t-test is equal to 0.000, and this value is less than the (0.05) limit of significance. This shows that statistically significant differences exist between (serial and open tender) in the cost of (total price with constants). The serial tendering approach outperforms the open tendering method in cost reduction (total price in constants). It had found that the serial tendering approach is superior to the open tendering method in lowering the cost (total price in constants) by a significant proportion. These points highlight the positive impact of using continuity and serial tendering to reduce bidding costs and support construction management.

References 1. Birchall, S., Ramus, J.W.: Contract Practice for Surveyors. Routledge (2007) 2. Murdoch, J., Hughes, W.: Construction Contracts: Law and Management. Routledge (2002) 3. RICS Professional Guidance, UK. Tendering strategies. 1st edition 4.8 Serial tendering p. 19.rics.org/guidance (2014) 4. Burhan, A.M., Erzaij, K.R., Hatem, W.A.: Developing a mathematical model for planning repetitive construction projects by using support vector machine technique. Civ. Environ. Eng. 17(2), 371–379 (2021) 5. Saka, A.B., Chan, D.W.: Knowledge, skills and functionalities requirements for quantity surveyors in building information modelling (BIM) work environment: an international Delphi study. Archit. Eng. Des. Manag. 16(3), 227–246 (2020) 6. Rantsatsi, N., Musonda, I., Agumba, J.: Factors that determine construction health and safety agent collaboration on construction projects: a Delphi study. Acta Structilia 28(2), 53–77 (2021) 7. Aguirre-Urreta, M.I., Rönkkö, M.: Statistical inference with PLSc using bootstrap confidence intervals. MIS Q. 42(3), 1001–1020 (2018) 8. Friedman, L.W., Friedman, H.H.: Analyzing simulation output using the bootstrap method. SIMULATION 64(2), 95–100 (1995)

138

A. A. Mohammed and K. R. Erzaij

9. Raychaudhuri, S.: Introduction to Monte Carlo simulation. In 2008 Winter Simulation Conference, pp. 91–100. IEEE (2008) 10. Vilela, M.J., Oluyemi, G.F.: Probabilistic evaluation of uncertainties: Monte Carlo method. In: Value of Information and Flexibility. PE, pp. 97–130. Springer, Cham (2022). https://doi. org/10.1007/978-3-030-86989-2_4

Geotechnical Risk Management. A Case Study of Nablus City, Palestine Fares Sami Hijjawi(B) Hijjawi Construction Labs, Nablus, Palestine [email protected]

Abstract. This paper presents a study of the geotechnical risk management in general and the Palestinian city of Nablus located in the north of the West Bank, in particular. This aim is pursued by reviewing the geotechnical risk management in general and studying the geotechnical situation (procedures challenges, impacts, mitigations, and ground conditions) in the city of Nablus. The methods used in order to meet the objectives are conducting interviews with local geotechnical experts in the study area, summarizing geotechnical aspects from site investigation reports in Nablus city, and reviewing the literature related to the topic. The collected data from the previous methods were analyzed and discussed critically. In conclusion, the paper focuses on applying a geotechnical risk management procedure, addresses the main geotechnical problems in Nablus, and proposes solutions for these problems. The study area needs improvement in geotechnical risk management to cope with the major projects which require high quality and standards and to reduce the projects’ overruns caused by geotechnical risks where the resources are limited in general. Also, it is well known that ground conditions are varied and comprise different kinds of problematic soil, which may cause a considerable risk if not appropriately recognized. Keywords: Geotechnical risk management · Nablus · Site investigation · Geotechnical map

1 Introduction Geotechnical risk is one of the major risks faced the construction projects globally. Ground conditions are uncertain and highly variable, so the nature of the soil on a specific site needs to be recognized and managed early. “No construction project is risk-free. Risk can be managed, minimized, shared, transferred or accepted. It cannot be ignored” [1]. Geotechnical risks are defined by Clayton [2] as: “the risk to building and construction work created by the site ground conditions” it can cause adverse effects on the construction process, such as impacts on cost, time, health, safety, and quality. Thus, the necessity to establish a geotechnical risk management (GRM) system has been recognized by many developed countries, such as the Netherlands, which have adopted a Geo-Impuls system to ensure that all geotechnical risks are identified, analyzed, and controlled. Further, the typical risk management system consists of three main parts: risk analysis, risk management, and risk modeling [2]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Karkush et al. (Eds.): ICGECI 2022, Current Trends in Geotechnical Engineering and Construction, pp. 139–150, 2023. https://doi.org/10.1007/978-981-19-7358-1_13

140

F. S. Hijjawi

Furthermore, the British department for transport published a standard for managing geotechnical risk under the design manual for roads and bridges. This Standard sets out the procedures to be followed and certificates to be used during the process of planning and reporting of all geotechnical works carried out on highways under the jurisdiction of the relevant overseeing organization in England, Wales and Northern Ireland to ensure that the geotechnical risk is correctly managed [3]. On the other hand, site investigation occupies a significant part of the geotechnical risk management, a study carried out by Maddah & Briaud [4] compares the requirements of minimum number and depth of boreholes recommended for buildings in seven countries based on the geographic representation and the availability of codes and standards, these countries are Eurocode (for European countries), Canada, Nigeria, Russia, Jordan, Egypt and Saudi Arabia. It is clear that the requirements vary between the involved countries. Additionally, it is important to know the types of geotechnical risks and recognize their anticipated sources as part of the geotechnical risk management process. In this light, Baynes [5] summarized the types of geotechnical risks, the associated hazards and the primary sources. The sources within the three types of geotechnical risk (project management, contractual & technical) can be divided into two groups; project staff responsible for the geoengineering process and geological conditions or processes that are difficult to investigate. Moreover, the construction industry in the West Bank is witnessing significant development. In 2014, the value of expenditure on constructing new buildings and additions to buildings in the West Bank was USD 550.2 million [6]. Nablus city was chosen as a case study due to several reasons. Firstly, its value in the West Bank is one of the three major cities in addition to Ramallah and Hebron cities, and it is recognized as the economic capital of Palestine. Secondly, it shares a significant part of the construction industry in the West Bank; during the second quarter of 2015, Nablus city recorded the highest number of residential building licenses compared to the other cities in the West Bank and Gaza Strip [7]. In general, the West Bank consists of two main geological strata; the weathered and fragmented limestone bedrock stratum and the sedimentary soils of different kinds of silty clay or marl soil stratum [8]. Finally, as it comprises most of the soil types of the West Bank, Nablus city provides a good representation of the general soil conditions. The objectives of the paper are: to review the geotechnical risk management in general, evaluate the current geotechnical procedures in the West Bank, including the guidelines and standards followed there, study the existing ground conditions in Nablus city and identify the common geotechnical risks, possible consequences and mitigations in the West Bank, particularly in Nablus. The paper framework started firstly with the literature review of the related published works on geotechnical risk management and geotechnical data for the study area. Then, describe the methodology used to fulfill the objectives of the paper. After that, the data collection for this paper relied on three main sources; literature review, interviews with local geotechnical experts and extraction data from site investigation reports. On the other hand, data analysis compiled the interpretation and discussion of the collected data from the different sources for the review of geotechnical risk management worldwide.

Geotechnical Risk Management. A Case Study of Nablus

141

2 Geotechnical Risk Management Several publications are talking about geotechnical risk management. This section addresses some of these publications to achieve this paper’s first objective. Clayton [2] explained why the geotechnical risks occur and provided practice guidance on how these risks can be managed to deliver construction work with high certainty. The roles of clients, designers, and contractors in managing geotechnical risk were identified. Clayton’s guide was prepared based on the experience of some of the UK’s leading construction parties; clients, designers, and contractor organizations as part of a research project carried out by ICE (Institution of Civil Engineers) under DETR (Department for Environment Transport and the Regions) partners in technology program. Furthermore, Van Staveren [9] analyzed and summarized ten reports prepared in order to develop his state-of-the-art international report on integrating geotechnical risk management in project risk management. Each report represents one of the following countries: Austria, China, Czech Republic, Finland, Germany, Japan, Netherlands, Sweden, Switzerland, and United Kingdom. Each report discussed the definition of GeoRM (Geotechnical Risk Management), the standards used for GeoRM, the hurdles that face GeoRM, and the solutions to overcome these hurdles. In Netherland, more than 40 Dutch organizations, including clients, contractors, designers, and knowledge institutes, work together from 2009 up to 2015 in a Dutch and industry-wide geotechnical development program called Geo-Impuls. Geo-Impuls aims to enhance the geotechnical community by significantly reducing geotechnical failures in all types of construction projects. The Geo-Impuls program consists of four elements: geo risk management process, geo risk management principles, geo risk management tools, and geo risk management implementation [10]. In Iraq, a study was made to assess the risk of soil contamination with oil products at a site near Al-Nassyriah oil refinery in Thi-Qar city [11]. Soil samples from the contaminated area were tested to find the influence of contamination on the geotechnical properties including chemical and physical properties. In order to classify the toxicity of the study area, risk assessment was used based on the exposure assessment and toxicity assessment. 2.1 Study Area Jardaneh [12] developed a geotechnical map for the city of Nablus (Fig. 1). The map divided the city into clayey soil regions, limestone regions, marl regions, and marl mixed with limestone boulders regions. Also, the main geotechnical properties were listed, the allowable bearing capacity and the proposed type of foundation for the four geotechnical regions. However, the map is the cornerstone for future works to develop a complete geotechnical map for Nablus since it represents the geotechnical properties generally for relatively large areas. A famous geotechnical incident in 1997 in Nablus, Landslides in the White Mountain, was a good example of the severe impacts that could happen unless proper geotechnical procedures were taken place. Jardaneh et al. [13] conducted geotechnical studies and engineering tests on the aforementioned case, and remedy measurements were suggested to overcome the sliding problems in the White Mountain area. Furthermore, one of the recommendations is to enforce the site investigation through specialized geotechnical

142

F. S. Hijjawi

Fig. 1. The geotechnical map for the city of Nablus—Palestine [12]

engineers for new structures. Moreover, a study was conducted to search the local site effects of the near-surface soil in Nablus city based on microtremor measurements to reduce the impact of potential earthquakes in Palestine [14]. The authors modified Jardaneh’s geotechnical map for Nablus to include geotechnical earthquake information at the near-surface and add the microtremor stations’ locations distributed at different geological formations. Among the three soil units areas based on local geology effects proposed in this study for the city of Nablus, the soft clay presents the worst soil condition for seismic hazards. Another effort has been made to develop the geotechnical map of Nablus using the GIS (Geographic Information System) software [15]. GIS was used to make a geotechnical map for the soil type of Nablus based on the bearing capacity of these types. GIS was used to build a database for geotechnical experts that is easy to use and retrieve the required data. ArcGIS software and its extensions were employed in applying interpolation methods and representing results. A recent case in Nablus was the slope instability at Nablus-Al Bathan road [16], where a large settlement occurred in the part of the road under maintenance, and more sliding and slope instability occurred in the road at station 2 + 100. It was found that the main causes of landslides were types of soils at the site, high slopes, groundwater recharged from rainfall, and changing of weights (cut and fill). Remedy measures were suggested to overcome the sliding problem based on available resources and local technology.

3 Methodology Three research methods were used to achieve the objectives of this paper. Literature review, conducting interviews with geotechnical experts, and summarizing site investigation reports for projects in Nablus city. The type of data used in this paper was

Geotechnical Risk Management. A Case Study of Nablus

143

qualitative data not quantitative. The reason for that is related to the nature of the theoretical topic rather than the practical topic or lab-based topic. The literature review comprised books, published papers in academic journals, and proceedings of conferences and maps. It helped in reaching all the objectives of this paper. Consequently, previous research on geotechnical risk management worldwide in addition to research related to the study area of Nablus city was reviewed in this regard. Moreover, it includes the site investigation code used in the West Bank and the main geotechnical map prepared for Nablus city. Section two above contains the main literature reviews and background information. Conducting interviews with professionals representing the various project parties; clients, designers, contractors, and geotechnical experts. The interviews aimed to gather as much information as possible about the existing geotechnical guidelines followed in West Bank. The geotechnical challenges faced by the engineering industry. The impacts of these challenges on the projects in terms of cost, time, health & safety, the current geotechnical mitigation measures, and the efficiency of the geotechnical map for Nablus city. The questions of the interviews were mostly the same for all interviewees to analyze and compare the outputs easily. Site investigation reports include a lot of information that can help achieve the third and fourth objectives of this paper, studying the existing ground conditions in Nablus city and identifying the common geotechnical risks, possible consequences, and mitigations in the West Bank, particularly in Nablus city. Also, the reports comprise information about ground conditions, laboratory test results, location of the groundwater level, allowable bearing capacity of the soil, proposed type of foundation, and suggested solutions for geotechnical problems. Further, the data were collected from site investigation reports of three geotechnical firms located in Nablus city for projects distributed in the city, including different ground conditions based on the geotechnical zones of Nablus city taken from the geotechnical map for Nablus city [12].

4 Analysis and Discussion 4.1 Geotechnical Hazard Identification Identifying the hazard is vital in any risk management process. Geotechnical hazard identification has a significant influence on geotechnical risk management. It is a fast and cost-effective process that employs a mix of existing data, expert opinion, and experience to identify the unsuitable conditions that might be possible on site. The stages of the geotechnical hazard identification include searching for existing information on the site through available sources such as geological maps and previous site investigation reports. Also, produce a ground model to estimate likely ground conditions and how much variation is possible and identify all hazards and the risks that might happen to all foreseeable types of construction. Finally, provide a report to communicate the findings to the client, designers, and contractors [2]. 4.2 Risk Registers Risk registers are essential in the risk assessment and analysis processes. Risk registers are the files where all the relevant risk data is saved. They usually contain a description

144

F. S. Hijjawi

of the risk, an assessment of their probability and impact, actions taken to manage them, and an explanation of who carries the financial consequences [2]. The severity or degree of risk can be estimated as Degree of risk = Likelihood x Effect. The likelihood or probability of the risk to occur can be estimated from Table 1 as an example. At the same time, the effect or impact of the risk can be known from Table 2 as an example. To get the severity of risk, the result of multiplying the likelihood and effect can be taken from Table 3 as an example. However, the risk register still has no agreed standard format to follow. Table 1. The likelihood of the risk [2]. Scale Likelihood

Chance, per section of work

5

Almost certain > 70%

4

Probable

50–70%

3

Likely

30–50%

2

Unlikely

10–30%

1

Negligible

< 10%

Table 2. The effect of the risk [17]. Scale

Impact

Increase of cost

Increase of time

5

Very high

>20%

>10 weeks on completion

4

High

5–20%

>1 week on completion

3

Medium

2–5%

>4 week: < 1 week on completion

2

Low

0.5–2%

1 – 4 weeks: none on completion

1

Very low