Structural Identification by Bayesian Neural Network with the Most Probable Architecture

dc.contributor.author	Yang, Jia-Hua
dc.contributor.author	Liu, Yi
dc.contributor.author	Lam, Heung-Fai
dc.contributor.author	Tan, Sui
dc.date.accessioned	2026-08-20T21:18:29Z
dc.date.available	2026-08-20T21:18:29Z
dc.date.copyright	2025/11/25
dc.date.issued	2026
dc.identifier.other	AJRUA6.RUENG-1710.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314264
dc.description.abstract	AbstractIn this work, neural networks (NNs) are used for structural identification by constructing the relationship between experimental data and structural properties. One problem is how to choose an appropriate NN architecture. It is not ideal to ...
dc.publisher	American Society of Civil Engineers
dc.title	Structural Identification by Bayesian Neural Network with the Most Probable Architecture
dc.type	Journal Article
dc.journal.volume	12
dc.journal.issue	1
dc.journal.title	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi	10.1061/AJRUA6.RUENG-1710
dc.journal.fristpage	04025105-1
dc.journal.lastpage	04025105-14
dc.page	14
dc.tree	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
dc.contenttype	Fulltext