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contributor authorHammoud, Ahmad
contributor authorKaraki, Ayman
contributor authorDujovic, Milos
contributor authorBattalgazy, Bekassyl
contributor authorArroyave, Raymundo
contributor authorMasad, Eyad
date accessioned2026-08-20T21:25:21Z
date available2026-08-20T21:25:21Z
date copyright2026/03/19
date issued2026
identifier otherJCCEE5.CPENG-6748.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314425
description abstractAbstractDeveloping concrete mix designs that enhance structural performance while reducing environmental impact is vital for achieving sustainable construction. This study utilizes multiobjective optimization and machine learning (ML) that aim to reduce ...Practical ApplicationsThis study provides a practical framework for engineers and project teams to design concrete that balances strength with a reduced carbon footprint. Using more than one thousand real mix records, machine learning models predict ...
publisherAmerican Society of Civil Engineers
titleSustainable Concrete Mix Designs: Multiobjective Optimization through Machine Learning Approaches
typeJournal Article
journal volume40
journal issue4
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-6748
journal fristpage04026033-1
journal lastpage04026033-15
page15
treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
contenttypeFulltext


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