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

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-learnin...

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Bibliographic Details
Published in:Frontiers of Structural and Civil Engineering 2024-10, Vol.18 (10), p.1507-1523
Main Authors: Khan, Umer Sadiq, Ishfaque, Muhammad, Khan, Saif Ur Rehman, Xu, Fang, Chen, Lerui, Lei, Yi
Format: Article
Language:English
Subjects:
Cities
Civil Engineering
Countries
Engineering
Regions
Research Article
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Summary: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.
ISSN:2095-2430
2095-2449
DOI:10.1007/s11709-024-1090-2