國立高雄大學圖資館 |

Language: English

Back

Blockchain of things and deep learning applications in constructiondigital construction transformation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Blockchain of things and deep learning applications in constructionby Faris Elghaish ... [et al.].
Reminder of title:	digital construction transformation /
other author:	Elghaish, Faris.
Published:	Cham :Springer International Publishing :2023.
Description:	xiii, 193 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Construction industryTechnological innovations.
Online resource:	https://doi.org/10.1007/978-3-031-06829-4
ISBN:	9783031068294$q(electronic bk.)

Blockchain of things and deep learning applications in constructiondigital construction transformation /
Blockchain of things and deep learning applications in constructiondigital construction transformation /[electronic resource] :by Faris Elghaish ... [et al.]. - Cham :Springer International Publishing :2023. - xiii, 193 p. :ill. (chiefly color), digital ;24 cm.

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.

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.

ISBN: 9783031068294$q(electronic bk.)

Standard No.: 10.1007/978-3-031-06829-4doiSubjects--Topical Terms:

611646
Construction industry
--Technological innovations.

LC Class. No.: HD9715.A2

Dewey Class. No.: 624.0285

Blockchain of things and deep learning applications in constructiondigital construction transformation /
LDR:03875nmm a2200325 a 4500 001 644858
003 DE-He213
005 20220709165150.0
006 m d
007 cr nn 008maaau
008 231208s2023 sz s 0 eng d
020 $a 9783031068294$q(electronic bk.)
020 $a 9783031068287$q(paper)
024 7 $a 10.1007/978-3-031-06829-4 $2 doi
035 $a 978-3-031-06829-4
040 $a GP $c GP
041 0 $a eng
050 4 $a HD9715.A2
072 7 $a TNK $2 bicssc
072 7 $a TEC005050 $2 bisacsh
072 7 $a TNK $2 thema
082 0 4 $a 624.0285 $2 23
090 $a HD9715.A2 $b B651 2023
245 0 0 $a Blockchain of things and deep learning applications in construction $h [electronic resource] : $b digital construction transformation / $c by Faris Elghaish ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xiii, 193 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Construction industry $x Technological innovations. $3 611646
650 0 $a Blockchains (Databases) $3 774478
650 0 $a Internet of things. $3 670954
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Building Construction and Design. $3 761394
650 2 4 $a Internet of Things. $3 913131
650 2 4 $a Blockchain. $3 918895
650 2 4 $a Construction Management. $3 286691
700 1 $a Elghaish, Faris. $3 953586
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-06829-4
950 $a Engineering (SpringerNature-11647)