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dc.contributor.authorKalimuthu, Bavithra
dc.contributor.authorRajendran, Mohana
dc.date.accessioned2026-08-20T21:25:39Z
dc.date.available2026-08-20T21:25:39Z
dc.date.copyright2026/05/15
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-6792.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314435
dc.description.abstractAbstractEmerging infrastructure development demands the use of advanced construction materials such as nanomaterials and high-performance concrete (HPC). Simultaneously, assessing the environmental impact (EI) of these materials is critically important ...Practical ApplicationsFrom a practical standpoint, the developed ANN-based framework serves as a decision-support tool for engineers, researchers, and policymakers. Unlike conventional LCA tools that require extensive manual data entry and provide single-...
dc.publisherAmerican Society of Civil Engineers
dc.titleANN-Based Machine Learning Approach for Predicting the Environmental Impacts of Emerging Nano Impregnated High-Performance Concrete
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue4
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6792
dc.journal.fristpage04026049-1
dc.journal.lastpage04026049-24
dc.page24
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
dc.contenttypeFulltext


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