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

Faris Elghaish, Farzad Pour Rahimian, Tara Brooks, Nashwan Dawood, Sepehr Abrishami

Research output: Book/ReportBook


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.
Original languageEnglish
Place of PublicationLondon, UK
Number of pages132
ISBN (Electronic) 9783031068287
ISBN (Print)978-3-031-06829-4
Publication statusPublished - 2023


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