Deep Learning-Based Rapid 3D Reconstruction and Spatial Localization of Leakage in Shield Tunnels

dc.contributor.author	Qian, Jinhua
dc.contributor.author	Wu, Songhua
dc.contributor.author	Xue, Fei
dc.contributor.author	Xiao, Kaifeng
dc.contributor.author	Wang, Tianzuo
dc.date.accessioned	2026-08-20T21:27:27Z
dc.date.available	2026-08-20T21:27:27Z
dc.date.copyright	2025/11/22
dc.date.issued	2026
dc.identifier.other	JCCEE5.CPENG-7105.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314484
dc.description.abstract	AbstractTo address the inefficiencies and limitations of traditional three-dimensional (3D) reconstruction methods for shield tunnels, particularly in visualization and leakage localization, this study proposes a deep learning–based framework that ...
dc.publisher	American Society of Civil Engineers
dc.title	Deep Learning-Based Rapid 3D Reconstruction and Spatial Localization of Leakage in Shield Tunnels
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	2
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-7105
dc.journal.fristpage	04025145-1
dc.journal.lastpage	04025145-15
dc.page	15
dc.tree	Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002
dc.contenttype	Fulltext