Identification of Fluid-Filled Inclusions of Arbitrary Quantities and Shapes in Solids from Measured Elastodynamic Responses Using CNN

dc.contributor.author	Kim, Boyoung
dc.contributor.author	Hahn, Jinho
dc.contributor.author	Abdelgawad, Salma
dc.contributor.author	Jeong, Chanseok
dc.date.accessioned	2026-08-20T21:23:02Z
dc.date.available	2026-08-20T21:23:02Z
dc.date.copyright	2026/03/03
dc.date.issued	2026
dc.identifier.other	JCCEE5.CPENG-6273.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314370
dc.description.abstract	AbstractThis study presents a data-informed convolutional neural network (CNN) for identifying fluid-filled (e.g., water) inclusions in two-dimensional (2D) solids. To generate training data, we simulate the propagation of elastic waves in structures ...
dc.publisher	American Society of Civil Engineers
dc.title	Identification of Fluid-Filled Inclusions of Arbitrary Quantities and Shapes in Solids from Measured Elastodynamic Responses Using CNN
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	3
dc.journal.title	Journal of Computing in Civil Engineering
dc.identifier.doi	10.1061/JCCEE5.CPENG-6273
dc.journal.fristpage	04026029-1
dc.journal.lastpage	04026029-19
dc.page	19
dc.tree	Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
dc.contenttype	Fulltext