首页|Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack detection.Twelve deep-learning models are trained on 192 crack images.This research aims to provide up-to-date detecting techniques to solve dam crack problems.The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91％accuracy.The study's pre-trained designs help to identify and to determine the specific locations of cracks.

concrete damborehole closed-circuit televisiondeep learning modelscrack detectionwater resources management

Umer Sadiq KHAN、Muhammad ISHFAQUE、Saif Ur Rehman KHAN、Fang Xu、Lerui CHEN、Yi LEI

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School of Computer and Information Science,Hubei Engineering University,Xiaogan 432000,China

Institute for AI Industrial Technology Research,Hubei Engineering University,Xiaogan 432000,China

College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China

School of Computer Science and Engineering,Central South University,Changsha 410083,China

College of Aviation,Zhongyuan University of Technology,Zhengzhou 451191,China

School of Civil Engineering,Central South University,Changsha 410083,China

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2024

10.1007/s11709-024-1090-2

结构与土木工程前沿
高等教育出版社

结构与土木工程前沿

CSTPCDEI
影响因子：0.082
ISSN：2095-2430
年,卷(期)：2024.18(10)