Void Detection in a 3D Semi-Infinite Solid Using Elastic Waves and a Proximity Function to Address Class Imbalance in CNN-Powered Grid-Wise Classification

dc.contributor.author	Tajmiri, Sina
dc.contributor.author	Kim, Boyoung
dc.contributor.author	Gharti, Hom Nath
dc.contributor.author	Jeong, Chanseok
dc.date.accessioned	2026-08-20T10:51:30Z
dc.date.available	2026-08-20T10:51:30Z
dc.date.copyright	2026/04/09
dc.date.issued	2026
dc.identifier.other	JENMDT.EMENG-8742.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311360
dc.description.abstract	AbstractThis study presents a deep learning framework, based on wave responses and a three-dimensional convolutional neural network (3D-CNN), for detecting subsurface cavities in a 3D homogeneous semi-infinite domain. In the data generation process, the ...
dc.publisher	American Society of Civil Engineers
dc.title	Void Detection in a 3D Semi-Infinite Solid Using Elastic Waves and a Proximity Function to Address Class Imbalance in CNN-Powered Grid-Wise Classification
dc.type	Journal Article
dc.journal.volume	152
dc.journal.issue	6
dc.journal.title	Journal of Engineering Mechanics
dc.identifier.doi	10.1061/JENMDT.EMENG-8742
dc.journal.fristpage	04026021-1
dc.journal.lastpage	04026021-22
dc.page	22
dc.tree	Journal of Engineering Mechanics:;2026:;Volume ( 152 ):;issue: 006
dc.contenttype	Fulltext