Blockchain of Things and Deep Learning Applications in Construction: Digital Construction Transformation

Description

This book significantly contributes the digital transformation of construction. The book explores the capabilities of deep learning to provide smart solutions for the construction industry, particularly in areas of managing equipment, design optimization, energy optimization and detect cracks for buildings and highways. It provides conceptual solutions but also practical techniques. A new deep learning CNN-based highway cracks detection is demonstrated, and its usefulness is tested. The resulting deep learning CNN model will enable users to scan long distance of highway and detect types of cracks accurately in a very short time compared to traditional approaches. The book explores the integration of IoT and blockchain to provide practical solutions to tackle existing challenges like the endemic fragmentation in supply chain, the need for monitoring construction projects remotely and tracking equipment on the site. The Blockchain of Things (BCoT) concept has been introduced to exploit the advantages of IoT and blockchain, and different applications were developed based on this integration in leading industries such as shared economy and health care. Workable potential use cases to exploit successful utilization of BCoT for the construction industry are explored in the book’s chapters. This book will appeal to researchers in providing a comprehensive review of related literature on blockchain, the IoT and construction identify gaps and offer a springboard for future research. Construction practitioners, research and development institutes and policy makers will also benefit from its usefulness as a reference book and collection of case studies on the application of these new approaches in construction. Contents About the Authors 1 Introduction Chapter 2: The Application of Blockchain of Things (BCoT) in the Construction Industry Chapter 3: Employing Blockchain Towards Safer and Interconnected Cost Management of Construction Projects Chapter 4: Developing a Conceptual Framework to Implement Distributed Ledged Technology (DLT) in Construction Industry Chapter 5: Web-Based Management System to Share Risk/Reward for IPD Projects Chapter 6: Deep Learning to Improve Construction Site Management Tasks Chapter 7: Detecting Distresses in Buildings and Highway Pavements-Based Deep Learning Technology Chapter 8: An Automated 4D BIM Model Development and Optimization Chapter 9: Deep Learning to Detect and Classify Highway Distresses Based on Optimised CNN Model References 2 The Application of Blockchain of Things (BCoT) in the Construction Industry Introduction Previous Review Papers, Summary and Gap Research Methods Findings from the Data Trend of Publications Key Research Areas IoT and Blockchain Integration Network Visualisation Internet of Things (IoT) for the Construction Industry IoT for the Prefabricated Building Industry IoT in Operational and Asset Management The Implications of IoT for Measuring Project Progress Parameters Blockchain and Smart Contracts Blockchain/Distributed Ledger Technology (DLT) Blockchain/Smart Contracts in Construction BIM and Blockchain Integration Barriers to Implementing Blockchain/Smart Contracts for Construction Project Delivery IoT and Blockchain Integration Use Cases Discussion on Findings Conclusion References 3 Employing Blockchain Towards Safer and Interconnected Cost Management of Construction Projects Introduction Conceptual Background Financial Management Challenges for Construction Projects Blockchain in Construction Blockchain and BIM Research Gap and Motivation Research Methodology Framework Development Formulation of Transaction-Based Smart Contract Main Contractor-to-Owner Contractor-to-Sub-contractor and Suppliers Structure of the Chaincode-Based Construction Transactions Blockchain and Smart Contract Component Structure Endorsement Policy-Based BIM Endorsement Policy Parameters Construction Projects Hand Over-Based Hyperledger Fabric System Alignment of the Decentralised Financial System with Construction Delivery Stages Proof of Concept Development and Case Study Developing a Blockchain Network Developing a Chaincode-Based Construction Transaction Discussion, Limitation and Future Research Conclusion References 4 Developing a Conceptual Framework to Implement Distributed Ledged Technology (DLT) in Construction Industry Introduction Overview of Blockchain and Smart Contracts Implications of Blockchain/Smart Contracts in the Construction Industry Previous Research on Blockchain and Smart Contracts in Construction Industry Methodology Framework Development Pre-construction Stage Construction Stage Closeout Stage Model Interoperability Research Significance and Conclusion References 5 Web-Based Management System to Share Risk/Reward for IPD Projects Introduction Information and Communication Technology (ICT) in Construction Management Implications of Cost Management Within BIM and IPD Earned Value Management Activity-Based Costing Research Methodology Developing the Framework Formulating IPD’s Cost Structure Based on ABC Developing EVM Based ABC Extensions The Integration of the EVM-Grid Web System and BIM Validation and Result Analysis Determining the Risk/Reward Values The Applicability and Integration of BIM and EVM-Web System Conclusion and Future Directions References 6 Deep Learning to Improve Construction Site Management Tasks Introduction Methodology and Logic Scientometric Analysis Objects and Information Detection on Site Field Detection Automated Progress Monitoring Analysing Projects Historical Records Health and Safety Using Deep Learning Safety Text Analysis Safety Monitoring Construction Site Safety Construction Workers Detections Construction Machines Detections Personal Protective Equipment’s Internet of Things (IoT) and Deep Learning (DL) Deep Learning and IoT to Deliver Smart Cities and Structures Deep Learning and IoT for Construction Assessment Discussion on Findings Conclusion References 7 Detecting Distresses in Buildings and Highway Pavements-Based Deep Learning Technology Introduction Methodology and Logic Deep Learning-Based Crack Detection Publications Per Year Progress of Deep Learning-Based Crack Detection Research Per Countries Crack Detection-Based Deep Learning: A Conceptual Background Convolutional Neural Networks (CNN) for Crack Detection Relevant Studies for Deep Learning-Based Pavement Crack Detection Deep Learning-Based Concrete Cracks Detection Deep Learning-Based Health Structure Evaluation Deep Learning and Ground Penetrating Radar (GPR) to Detect Cracks Discussion, Significance and Limitation Conclusion References 8 An Automated 4D BIM Model Development and Optimization Introduction Methodology Research Approach Developing the Framework and Proposing Tools Results and Analysis The Description of the Case Study The Configuration of ABC Hierarchy Level Building List of Activities from the Proposed Library Optimisation of Constructability Methods for Each Activity The Level of Contribution Based on the ABC Hierarchy Level Research Implications Conclusion and Future Directions References 9 Deep Learning to Detect and Classify Highway Distresses Based on Optimised CNN Model Introduction Deep Learning-Based Crack Detection Methodology Data Collection and Analysis Comparison of Pre-trained Deep Learning Models Proposing and Evaluating a New CNN Model Classification Accuracy of the Proposed Model Comparing Between Different Optimisation Algorithms to Enhance the Accuracy Significance and Contribution Conclusion References