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dc.contributor.authorWang, Jiehui
dc.contributor.authorChen, Ziyang
dc.contributor.authorUeda, Tamon
dc.contributor.authorDai, Jian-Guo
dc.date.accessioned2026-08-20T21:28:45Z
dc.date.available2026-08-20T21:28:45Z
dc.date.copyright2026/03/18
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7482.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314522
dc.description.abstractAbstractWith the rapid advancement of deep learning, automated inspection of reinforced concrete (RC) structures has become increasingly viable. However, existing models are typically task-specific, limiting their utility across diverse scenarios. This ...Practical ApplicationsThis study presents a new, all-in-one artificial intelligence framework designed to help engineers and inspectors quickly and accurately identify multiple types of damage in concrete structures. Unlike many existing tools that can ...
dc.publisherAmerican Society of Civil Engineers
dc.titleDevelopment and Evaluation of a Task-Adaptive and Hierarchical Deep Learning Framework for Multitype Damage Detection in Reinforced Concrete Structures
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue4
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7482
dc.journal.fristpage04026032-1
dc.journal.lastpage04026032-18
dc.page18
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
dc.contenttypeFulltext


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