A Deep Learning–Based Approach for the Evaluation of Tunneling-Induced Ground Surface Settlement Considering Spatial Variability of the Soil

dc.contributor.author	Niu, Gang
dc.contributor.author	He, Xuzhen
dc.date.accessioned	2026-08-20T12:09:40Z
dc.date.available	2026-08-20T12:09:40Z
dc.date.copyright	2026/01/07
dc.date.issued	2026
dc.identifier.other	IJGNAI.GMENG-11938.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313170
dc.description.abstract	Abstract Tunneling-induced ground surface settlement (GSS) is a critical concern in urban areas as it can lead to differential settlement of surface constructions and cracking of underground pipelines. While numerical methods and machine learning ...
dc.publisher	American Society of Civil Engineers
dc.title	A Deep Learning–Based Approach for the Evaluation of Tunneling-Induced Ground Surface Settlement Considering Spatial Variability of the Soil
dc.type	Journal Article
dc.journal.volume	26
dc.journal.issue	3
dc.journal.title	International Journal of Geomechanics
dc.identifier.doi	10.1061/IJGNAI.GMENG-11938
dc.journal.fristpage	04026010-1
dc.journal.lastpage	04026010-17
dc.page	17
dc.tree	International Journal of Geomechanics:;2026:;Volume ( 026 ):;issue: 003
dc.contenttype	Fulltext