Prediction of Failure Mode and Buckling Strength of Ring-Stiffened Cylindrical Shells Using Machine-Learning Techniques

dc.contributor.author	Tabish, F. N. U.
dc.contributor.author	Mamaghani, Iraj H. P.
dc.contributor.author	Ullah, Shafqat
dc.contributor.author	Mwaura, Njiru
dc.contributor.author	Karim, Muhammad Hamza
dc.date.accessioned	2026-08-20T12:08:35Z
dc.date.available	2026-08-20T12:08:35Z
dc.date.copyright	2025/06/20
dc.date.issued	2025
dc.identifier.other	JSDCCC.SCENG-1685.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313142
dc.description.abstract	AbstractThis study utilizes the concept of machine learning (ML) to predict the failure mode and associated buckling strength of ring-stiffened cylindrical storage tanks under uniform external pressure. A database comprising 180 ring-stiffened cylindrical ...
dc.publisher	American Society of Civil Engineers
dc.title	Prediction of Failure Mode and Buckling Strength of Ring-Stiffened Cylindrical Shells Using Machine-Learning Techniques
dc.type	Journal Article
dc.journal.volume	30
dc.journal.issue	4
dc.journal.title	Journal of Structural Design and Construction Practice
dc.identifier.doi	10.1061/JSDCCC.SCENG-1685
dc.journal.fristpage	04025068-1
dc.journal.lastpage	04025068-13
dc.page	13
dc.tree	Journal of Structural Design and Construction Practice:;2025:;Volume ( 030 ):;issue: 004
dc.contenttype	Fulltext