Development of an ANN Model for Predicting Optimum Mix Proportion of Fiber-Reinforced Geopolymer Concrete and Assessment of the Model’s Prediction Efficiency

dc.contributor.author	Kadarkarai, Arunkumar
dc.contributor.author	Joseph, Karishma
dc.contributor.author	Boomibalan, Sankar
dc.contributor.author	Vagestan, Prem Kumar
dc.contributor.author	Sadasivam, Savitha
dc.contributor.author	Rameshkumar, Deivasigamani
dc.date.accessioned	2026-08-20T12:14:24Z
dc.date.available	2026-08-20T12:14:24Z
dc.date.copyright	2026/03/31
dc.date.issued	2026
dc.identifier.other	JSDCCC.SCENG-2032.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4313266
dc.description.abstract	AbstractThe environmental footprint of ordinary portland cement production is responsible for nearly 8% of global CO2 emissions, which has raised significant concerns in recent decades. In pursuit of sustainable alternatives, geopolymer concrete produced by ...
dc.publisher	American Society of Civil Engineers
dc.title	Development of an ANN Model for Predicting Optimum Mix Proportion of Fiber-Reinforced Geopolymer Concrete and Assessment of the Model’s Prediction Efficiency
dc.type	Journal Article
dc.journal.volume	31
dc.journal.issue	3
dc.journal.title	Journal of Structural Design and Construction Practice
dc.identifier.doi	10.1061/JSDCCC.SCENG-2032
dc.journal.fristpage	04026044-1
dc.journal.lastpage	04026044-9
dc.page	9
dc.tree	Journal of Structural Design and Construction Practice:;2026:;Volume ( 031 ):;issue: 003
dc.contenttype	Fulltext