Causality Constrained Deep Learning for Explainable Risk Analysis of Bridge Pier Settlement

dc.contributor.author	Qi, Yafei
dc.contributor.author	Liu, Wenli
dc.contributor.author	Qu, Yao
dc.contributor.author	Liu, Tianxiang
dc.contributor.author	Shao, Yixiao
dc.contributor.author	Liu, Fenghua
dc.date.accessioned	2026-08-20T21:28:26Z
dc.date.available	2026-08-20T21:28:26Z
dc.date.copyright	2025/11/27
dc.date.issued	2026
dc.identifier.other	JCCEE5.CPENG-7330.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314513
dc.description.abstract	AbstractDeep learning (DL) has significantly enhanced risk management in highway bridge operation. Although DL models exhibit strong predictive capabilities, their outputs derive from “black box” processes and cannot characterize uncertainty, leading to ...
dc.publisher	American Society of Civil Engineers
dc.title	Causality Constrained Deep Learning for Explainable Risk Analysis of Bridge Pier Settlement
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-7330
dc.journal.fristpage	04025156-1
dc.journal.lastpage	04025156-20
dc.page	20
dc.tree	Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002
dc.contenttype	Fulltext