Blockchain of things and deep learning applications in construction : digital construction transformation / Faris Elghaish, Farzad Pour Rahimian, Tara Brooks, Nashwan Dawood, Sepehr Abrishami.
Elghaish, Faris, author.; Rahimian, Farzad Pour, author.; Brooks, Tara, author.; Dawood, Nashwan, author.; Abrishami, Sepehr, author.
2023
HD9715.A2
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Blockchain of things and deep learning applications in construction : digital construction transformation / Faris Elghaish, Farzad Pour Rahimian, Tara Brooks, Nashwan Dawood, Sepehr Abrishami.
Author Elghaish, Faris, author.
ISBN 9783031068294 (electronic bk.)
3031068297 (electronic bk.)
9783031068287
3031068289
DOI https://doi.org/10.1007/978-3-031-06829-4
Published Cham : Springer, [2023]
Copyright ©2023
Language English
Description 1 online resource : illustrations (chiefly color)
Item Number 10.1007/978-3-031-06829-4 doi
Call Number HD9715.A2
Dewey Decimal Classification 624.0285
Summary 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 books 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Rahimian, Farzad Pour, author.
Brooks, Tara, author.
Dawood, Nashwan, author.
Abrishami, Sepehr, author.
Available in Other Form Blockchain of things and deep learning applications in construction.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Blockchain applications in Construction: A comprehensive review
Scientometric analysis of blockchain uses in construction
Internet of Things (IoT) applications in Construction: A comprehensive review
Blockchain of Things (BCoT): Beyond the concept
Integrated Project Delivery with Blockchain: Sharing risk/reward system
The feasibility of blockchain for Integrated Project Delivery (IPD): An exploratory Study
A decentralised financial system-based blockchain: Towards digitalised construction
A comprehensive solution for financial challenges in the construction industry: Blockchain-based solution
Smart Common Data Environment (SCDE) based blockchain: An automated collaboration platform
Deep learning applications in the construction industry: A critical analysis
Developing a new deep learning CNN model to detect and classify highway cracks
An optimized CNN model to detect irregular pavement distresses: Face recognition-based solution
Integrating Artificial intelligence into immersive and drones technologies: A conceptual framework and practical use cases.

Browse Subjects

Show more subjects...

Statistics

from
to
Export