Vision-Based Quantification of Stiffness Degradation in Reinforced Concrete Shear Walls Using Graph-Based Surface Crack Features and Machine Learning

dc.contributor.author	Bazrafshan, Pedram
dc.contributor.author	Osan, Rhythm
dc.contributor.author	Ebrahimkhanlou, Arvin
dc.date.accessioned	2026-08-20T12:29:01Z
dc.date.available	2026-08-20T12:29:01Z
dc.date.copyright	2026/02/27
dc.date.issued	2026
dc.identifier.other	JSENDH.STENG-15932.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313523
dc.description.abstract	AbstractThe rapid and accurate assessment of structural damage in reinforced concrete shear walls (RCSWs) following seismic events remains a critical yet unresolved challenge in structural engineering. Current methods rely heavily on subjective visual ...
dc.publisher	American Society of Civil Engineers
dc.title	Vision-Based Quantification of Stiffness Degradation in Reinforced Concrete Shear Walls Using Graph-Based Surface Crack Features and Machine Learning
dc.type	Journal Article
dc.journal.volume	152
dc.journal.issue	5
dc.journal.title	Journal of Structural Engineering
dc.identifier.doi	10.1061/JSENDH.STENG-15932
dc.journal.fristpage	04026047-1
dc.journal.lastpage	04026047-14
dc.page	14
dc.tree	Journal of Structural Engineering:;2026:;Volume ( 152 ):;issue: 005
dc.contenttype	Fulltext