Prediction of Rapid Chloride Penetration Resistance of Metakaolin-Based High Strength Concrete: Critical Comparison between Natural and Evolutionary Algorithm–Optimized Artificial Neural Networks

dc.contributor.author	Khatti, Jitendra
dc.contributor.author	Mashaly, Asmaa A.
dc.date.accessioned	2026-08-20T12:11:03Z
dc.date.available	2026-08-20T12:11:03Z
dc.date.copyright	2025/07/16
dc.date.issued	2025
dc.identifier.other	JSDCCC.SCENG-1843.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313197
dc.description.abstract	AbstractThis research presents a significant hybrid artificial neural network (ANN) model by comparing the performance of six optimization techniques: artificial bee colony (ABC), genetic algorithm (GA), grey wolf optimizer, Harris hawks optimization (HHO)...
dc.publisher	American Society of Civil Engineers
dc.title	Prediction of Rapid Chloride Penetration Resistance of Metakaolin-Based High Strength Concrete: Critical Comparison between Natural and Evolutionary Algorithm–Optimized Artificial Neural Networks
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-1843
dc.journal.fristpage	04025090-1
dc.journal.lastpage	04025090-18
dc.page	18
dc.tree	Journal of Structural Design and Construction Practice:;2025:;Volume ( 030 ):;issue: 004
dc.contenttype	Fulltext