Table Of ContentFaris Elghaish · Farzad Pour Rahimian ·
Tara Brooks · Nashwan Dawood ·
Sepehr Abrishami
Blockchain
of Things and
Deep Learning
Applications in
Construction
Digital Construction Transformation
Blockchain of Things and Deep Learning
Applications in Construction
· ·
Faris Elghaish Farzad Pour Rahimian
· ·
Tara Brooks Nashwan Dawood
Sepehr Abrishami
Blockchain of Things
and Deep Learning
Applications in Construction
Digital Construction Transformation
Faris Elghaish Farzad Pour Rahimian
School of Natural and Built Environment School of Computing, Engineering &
Queen’s University Belfast Digital Technologies
Belfast, UK Teesside University
Middlesbrough, Northumberland, UK
Tara Brooks
School of Natural and Built Environment Nashwan Dawood
Queen’s University Belfast School of Computing, Engineering &
Belfast, UK Digital Technologies
Teesside University
Sepehr Abrishami Middlesbrough, Northumberland, UK
University of Portsmouth
Portsmouth, UK
ISBN 978-3-031-06828-7 ISBN 978-3-031-06829-4 (eBook)
https://doi.org/10.1007/978-3-031-06829-4
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Contents
1 Introduction ................................................... 1
Chapter 2: The Application of Blockchain of Things (BCoT)
in the Construction Industry ...................................... 3
Chapter 3: Employing Blockchain Towards Safer
and Interconnected Cost Management of Construction
Projects ........................................................ 3
Chapter 4: Developing a Conceptual Framework to Implement
Distributed Ledged Technology (DLT) in Construction Industry ........ 4
Chapter 5: Web-Based Management System to Share Risk/Reward
for IPD Projects ................................................. 4
Chapter 6: Deep Learning to Improve Construction Site
Management Tasks .............................................. 5
Chapter 7: Detecting Distresses in Buildings and Highway
Pavements-Based Deep Learning Technology ....................... 5
Chapter 8: An Automated 4D BIM Model Development
and Optimization ................................................ 6
Chapter 9: Deep Learning to Detect and Classify Highway
Distresses Based on Optimised CNN Model ......................... 6
References ..................................................... 7
2 The Application of Blockchain of Things (BCoT)
in the Construction Industry .................................... 9
Introduction .................................................... 9
Previous Review Papers, Summary and Gap ......................... 11
Research Methods ............................................... 13
Findings from the Data ........................................... 15
Trend of Publications ......................................... 15
Key Research Areas ........................................... 16
IoT and Blockchain Integration Network Visualisation ............. 17
v
vi Contents
Internet of Things (IoT) for the Construction Industry ................ 18
IoT for the Prefabricated Building Industry ....................... 18
IoT in Operational and Asset Management ....................... 20
The Implications of IoT for Measuring Project Progress
Parameters ................................................... 20
Blockchain and Smart Contracts ................................... 21
Blockchain/Distributed Ledger Technology (DLT) ................. 22
Blockchain/Smart Contracts in Construction ...................... 23
BIM and Blockchain Integration ................................ 26
Barriers to Implementing Blockchain/Smart Contracts
for Construction Project Delivery ............................... 27
IoT and Blockchain Integration Use Cases .......................... 28
Discussion on Findings .......................................... 28
Conclusion ..................................................... 32
References ..................................................... 33
3 Employing Blockchain Towards Safer and Interconnected Cost
Management of Construction Projects ............................ 41
Introduction .................................................... 41
Conceptual Background .......................................... 43
Financial Management Challenges for Construction Projects ........ 43
Blockchain in Construction .................................... 44
Blockchain and BIM .......................................... 44
Research Gap and Motivation ..................................... 45
Research Methodology ........................................... 47
Framework Development ......................................... 47
Formulation of Transaction-Based Smart Contract ................. 48
Main Contractor-to-Owner ..................................... 49
Contractor-to-Sub-contractor and Suppliers ....................... 50
Structure of the Chaincode-Based Construction Transactions ........ 51
Blockchain and Smart Contract Component Structure ................ 51
Endorsement Policy-Based BIM ................................ 51
Endorsement Policy Parameters ................................. 52
Construction Projects Hand Over-Based Hyperledger Fabric
System ...................................................... 53
Alignment of the Decentralised Financial System
with Construction Delivery Stages .............................. 54
Proof of Concept Development and Case Study ...................... 56
Developing a Blockchain Network .............................. 57
Developing a Chaincode-Based Construction Transaction .......... 57
Discussion, Limitation and Future Research ......................... 60
Conclusion ..................................................... 62
References ..................................................... 63
Contents vii
4 Developing a Conceptual Framework to Implement Distributed
Ledged Technology (DLT) in Construction Industry ............... 67
Introduction .................................................... 67
Overview of Blockchain and Smart Contracts ....................... 68
Implications of Blockchain/Smart Contracts in the Construction
Industry ........................................................ 71
Previous Research on Blockchain and Smart Contracts
in Construction Industry .......................................... 72
Methodology ................................................... 72
Framework Development ......................................... 72
Pre-construction Stage ......................................... 76
Construction Stage ............................................ 76
Closeout Stage ............................................... 77
Model Interoperability ......................................... 78
Research Significance and Conclusion .............................. 78
References ..................................................... 79
5 Web-Based Management System to Share Risk/Reward
for IPD Projects ................................................ 83
Introduction .................................................... 83
Information and Communication Technology (ICT) in Construction
Management ................................................... 85
Implications of Cost Management Within BIM and IPD .............. 86
Earned Value Management ....................................... 87
Activity-Based Costing .......................................... 87
Research Methodology ........................................... 87
Developing the Framework ....................................... 88
Formulating IPD’s Cost Structure Based on ABC .................. 88
Developing EVM Based ABC Extensions ........................ 89
The Integration of the EVM-Grid Web System and BIM ............ 92
Validation and Result Analysis .................................... 93
Determining the Risk/Reward Values ............................ 93
The Applicability and Integration of BIM and EVM-Web System .... 93
Conclusion and Future Directions .................................. 94
References ..................................................... 95
6 Deep Learning to Improve Construction Site Management
Tasks .......................................................... 99
Introduction .................................................... 99
Methodology and Logic .......................................... 101
Scientometric Analysis ........................................... 101
Objects and Information Detection on Site .......................... 105
Field Detection ............................................... 105
Automated Progress Monitoring ................................ 105
Analysing Projects Historical Records ........................... 105
viii Contents
Health and Safety Using Deep Learning ............................ 110
Safety Text Analysis .......................................... 110
Safety Monitoring ............................................ 111
Construction Site Safety ....................................... 111
Construction Workers Detections ............................... 113
Construction Machines Detections .............................. 116
Personal Protective Equipment’s ................................ 116
Internet of Things (IoT) and Deep Learning (DL) .................... 118
Deep Learning and IoT to Deliver Smart Cities and Structures ...... 120
Deep Learning and IoT for Construction Assessment .............. 120
Discussion on Findings .......................................... 121
Conclusion ..................................................... 122
References ..................................................... 124
7 Detecting Distresses in Buildings and Highway
Pavements-Based Deep Learning Technology ..................... 129
Introduction .................................................... 130
Methodology and Logic .......................................... 132
Deep Learning-Based Crack Detection Publications Per Year ....... 133
Progress of Deep Learning-Based Crack Detection Research
Per Countries ................................................ 133
Crack Detection-Based Deep Learning: A Conceptual Background ..... 134
Convolutional Neural Networks (CNN) for Crack Detection ........... 136
Relevant Studies for Deep Learning-Based Pavement Crack
Detection ...................................................... 138
Deep Learning-Based Concrete Cracks Detection .................... 138
Deep Learning-Based Health Structure Evaluation ................... 146
Deep Learning and Ground Penetrating Radar (GPR) to Detect
Cracks ......................................................... 146
Discussion, Significance and Limitation ............................ 146
Conclusion ..................................................... 151
References ..................................................... 152
8 An Automated 4D BIM Model Development and Optimization ..... 159
Introduction .................................................... 159
Methodology ................................................... 165
Research Approach ........................................... 165
Developing the Framework and Proposing Tools ..................... 166
Results and Analysis ............................................. 170
The Description of the Case Study .............................. 170
The Configuration of ABC Hierarchy Level ...................... 171
Building List of Activities from the Proposed Library .............. 171
Optimisation of Constructability Methods for Each Activity ........ 172
The Level of Contribution Based on the ABC Hierarchy Level ...... 173
Contents ix
Research Implications ........................................... 175
Conclusion and Future Directions .................................. 175
References ..................................................... 176
9 Deep Learning to Detect and Classify Highway Distresses
Based on Optimised CNN Model ................................. 181
Introduction .................................................... 181
Deep Learning-Based Crack Detection ............................. 183
Methodology ................................................... 184
Data Collection and Analysis ..................................... 185
Comparison of Pre-trained Deep Learning Models ................. 186
Proposing and Evaluating a New CNN Model .................... 187
Classification Accuracy of the Proposed Model ................... 188
Comparing Between Different Optimisation Algorithms
to Enhance the Accuracy ....................................... 189
Significance and Contribution ..................................... 190
Conclusion ..................................................... 192
References ..................................................... 192
About the Authors
Dr. Faris Elghaish is a lecturer in Construction Project Management at the School of
Natural and Built Environment, Queen’s University Belfast (QUB). Faris has carried
out extensive research in the field of BIM, Artificial Intelligence, cost optimisa-
tion/automation, and decentralised systems (i.e., Blockchain and smart contracts).
Faris has developed many workable solutions to automate processes and tasks based
on integrating emerging technologies into BIM. Faris has published more than 22
journal articles in top-ranked journals and served as a guest editor in many reputable
journals.
Prof. Farzad Pour Rahimian is Professor of Digital Engineering and Manu-
facturing, with a robust research track-record delivering more than 150 quality
publications and more than 1600 citations. His research career has been devel-
oped through (1) 10 years high-quality education in Architecture, Digital Design
and Innovative Construction Technologies (from 1996–2002+2007–2010), (2) 20
years’ academic work experience, including research-informed teaching and teaching
supported research on different areas related to Digital Architecture and IT inte-
grated design and construction (Since 2002), (3) 15 years intensive industry engage-
ment as a full member of Chartered Institute of Building (MCIOB-UK) and a First
Class Registered Architect (in Iran), and (4) variety of hands-on virtual modelling
and development skills and knowledge of different qualitative and quantitative
research methodologies. These support his research on various aspects of Construc-
tion 4.0 including Digital Twins, Smart Contracts, Building Information Manage-
ment/Modelling (BIM), Distributed Ledger Technology, Big Data, IA/Machine-
Learning, Integrated Project Delivery (IPD), Smart Homes, IoT, Cybernetics and
use of Smart Agents within the built environment, Virtual Prototyping and use of
VR/AR/MR interfaces for building simulation, automation and integration of design,
Offsite Construction, Lean and Six Sigma thinking and development of new busi-
ness models for integrated construction. Farzad has also successfully led several
high profile research projects—from InnovateUK, through to Arts and Humani-
ties Research Council (AHRC), European Regional Development Funds (ERDF),
Construction Scotland Innovation Centre (CSIC), Scottish Funding Council (SFC),
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