Show simple item record

Optimized Generative Modeling and Interpretable Machine Learning for Predicting the Compressive Strength of High-Performance Concrete

dc.contributor.authorWang, Yufei
dc.contributor.authorSun, Junbo
dc.contributor.authorZou, Zefeng
dc.contributor.authorWang, Xiangyu
dc.contributor.authorLi, Shengping
dc.contributor.authorZhao, Hongyu
dc.contributor.authorShang, Jiajie
dc.date.accessioned2026-08-20T11:46:38Z
dc.date.available2026-08-20T11:46:38Z
dc.date.copyright2026/04/19
dc.date.issued2026
dc.identifier.otherJMCEE7.MTENG-22159.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312654
dc.description.abstractAbstractThis study provides a comprehensive analysis of ultrahigh-performance concrete (UHPC) compressive strength, focusing on data augmentation, predictive modeling, and model interpretability. The research utilized 808 experimental data points with 16 ...
dc.publisherAmerican Society of Civil Engineers
dc.titleOptimized Generative Modeling and Interpretable Machine Learning for Predicting the Compressive Strength of High-Performance Concrete
dc.typeJournal Article
dc.journal.volume38
dc.journal.issue7
dc.journal.titleJournal of Materials in Civil Engineering
dc.identifier.doi10.1061/JMCEE7.MTENG-22159
dc.journal.fristpage04026168-1
dc.journal.lastpage04026168-16
dc.page16
dc.treeJournal of Materials in Civil Engineering:;2026:;Volume ( 038 ):;issue: 007
dc.contenttypeFulltext


Files in this item

Thumbnail
Name:
JMCEE7.MTENG-22159.pdf
Size:
3.478Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record