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contributor authorWang, Jiehui
contributor authorChen, Ziyang
contributor authorUeda, Tamon
contributor authorDai, Jian-Guo
date accessioned2026-08-20T21:28:45Z
date available2026-08-20T21:28:45Z
date copyright2026/03/18
date issued2026
identifier otherJCCEE5.CPENG-7482.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314522
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 ...
publisherAmerican Society of Civil Engineers
titleDevelopment and Evaluation of a Task-Adaptive and Hierarchical Deep Learning Framework for Multitype Damage Detection in Reinforced Concrete Structures
typeJournal Article
journal volume40
journal issue4
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-7482
journal fristpage04026032-1
journal lastpage04026032-18
page18
treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 004
contenttypeFulltext


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