Details
| Original language | English |
|---|---|
| Article number | 107761 |
| Number of pages | 18 |
| Journal | Computers and Structures |
| Volume | 315 |
| Early online date | 25 Apr 2025 |
| Publication status | E-pub ahead of print - 25 Apr 2025 |
Abstract
Rapid assessment of building damage levels has become very important and has received considerable attention in structural engineering. Traditional methods for this work involve manual inspection, which is often tedious and time-consuming. Deep learning technology in computer vision has developed rapidly in recent years and has proven its superiority. This paper aims to develop an efficient approach to recognize quick post-earthquake structural damage levels. First, we develop a feature extraction with seven pre-trained CNN models (Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, NASNetMobile) on a small dataset of 2000 images. The CNN models are then trained by five fold cross-validation. The performance of the models is compared on a testing set, the MobileNet model demonstrated the best classifier performance with an accuracy of 90.89 %. Second, the Bayesian optimization method and the fine-tuning strategy are used to find the optimal hyperparameters of the MobileNet model. The results revealed that the performance of the MobileNet model increased significantly with an accuracy of 96.11 %. Third, Gradient-weighted class activation mapping (Grad-CAM) is used to highlight crucial regions on structural damage images for CNN's prediction. Finally, the generalizability of the MobileNet model is improved by training it on an extended dataset of 3600 images. The proposed approach demonstrates the feasibility and potential uses of deep learning in image-based structural damage level recognition.
Keywords
- Bayesian optimization, Convolutional neural network, Damage level recognition, Deep learning, Image classification, Transfer learning
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Mathematics(all)
- Modelling and Simulation
- Materials Science(all)
- General Materials Science
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Computers and Structures, Vol. 315, 107761, 08.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Deep learning-based post-earthquake structural damage level recognition
AU - Zhuang, Xiaoying
AU - Tran, Than V.
AU - Nguyen-Xuan, H.
AU - Rabczuk, Timon
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/4/25
Y1 - 2025/4/25
N2 - Rapid assessment of building damage levels has become very important and has received considerable attention in structural engineering. Traditional methods for this work involve manual inspection, which is often tedious and time-consuming. Deep learning technology in computer vision has developed rapidly in recent years and has proven its superiority. This paper aims to develop an efficient approach to recognize quick post-earthquake structural damage levels. First, we develop a feature extraction with seven pre-trained CNN models (Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, NASNetMobile) on a small dataset of 2000 images. The CNN models are then trained by five fold cross-validation. The performance of the models is compared on a testing set, the MobileNet model demonstrated the best classifier performance with an accuracy of 90.89 %. Second, the Bayesian optimization method and the fine-tuning strategy are used to find the optimal hyperparameters of the MobileNet model. The results revealed that the performance of the MobileNet model increased significantly with an accuracy of 96.11 %. Third, Gradient-weighted class activation mapping (Grad-CAM) is used to highlight crucial regions on structural damage images for CNN's prediction. Finally, the generalizability of the MobileNet model is improved by training it on an extended dataset of 3600 images. The proposed approach demonstrates the feasibility and potential uses of deep learning in image-based structural damage level recognition.
AB - Rapid assessment of building damage levels has become very important and has received considerable attention in structural engineering. Traditional methods for this work involve manual inspection, which is often tedious and time-consuming. Deep learning technology in computer vision has developed rapidly in recent years and has proven its superiority. This paper aims to develop an efficient approach to recognize quick post-earthquake structural damage levels. First, we develop a feature extraction with seven pre-trained CNN models (Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, NASNetMobile) on a small dataset of 2000 images. The CNN models are then trained by five fold cross-validation. The performance of the models is compared on a testing set, the MobileNet model demonstrated the best classifier performance with an accuracy of 90.89 %. Second, the Bayesian optimization method and the fine-tuning strategy are used to find the optimal hyperparameters of the MobileNet model. The results revealed that the performance of the MobileNet model increased significantly with an accuracy of 96.11 %. Third, Gradient-weighted class activation mapping (Grad-CAM) is used to highlight crucial regions on structural damage images for CNN's prediction. Finally, the generalizability of the MobileNet model is improved by training it on an extended dataset of 3600 images. The proposed approach demonstrates the feasibility and potential uses of deep learning in image-based structural damage level recognition.
KW - Bayesian optimization
KW - Convolutional neural network
KW - Damage level recognition
KW - Deep learning
KW - Image classification
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=105003377306&partnerID=8YFLogxK
U2 - 10.1016/j.compstruc.2025.107761
DO - 10.1016/j.compstruc.2025.107761
M3 - Article
AN - SCOPUS:105003377306
VL - 315
JO - Computers and Structures
JF - Computers and Structures
SN - 0045-7949
M1 - 107761
ER -