Computer vision-based construction progress monitoring

Reja, VK; Varghese, K; Ha, QP

Computer vision-based construction progress monitoring

Reja, VK Varghese, K Ha, QP

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Automation in Construction, 2022, 138, pp. 104245
Issue Date:: 2022-06-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0926580522001182-main.pdf	Published version	7.24 MB		View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Reja, VK
dc.contributor.author	Varghese, K
dc.contributor.author	Ha, QP
dc.date.accessioned	2022-05-10T04:30:03Z
dc.date.available	2022-05-10T04:30:03Z
dc.date.issued	2022-06-01
dc.identifier.citation	Automation in Construction, 2022, 138, pp. 104245
dc.identifier.issn	0926-5805
dc.identifier.uri	http://hdl.handle.net/10453/157202
dc.description.abstract	Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Automation in Construction
dc.relation.isbasedon	10.1016/j.autcon.2022.104245
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering, 12 Built Environment and Design
dc.subject.classification	Building & Construction
dc.title	Computer vision-based construction progress monitoring
dc.type	Journal Article
utslib.citation.volume	138
utslib.for	09 Engineering
utslib.for	12 Built Environment and Design
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-10T04:30:00Z
pubs.publication-status	Accepted
pubs.volume	138

Abstract:

Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157202