Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning
Than V. TRAN1, H. NGUYEN-XUAN2(), Xiaoying ZHUANG1,3()
1. Institute of Photonics, Leibniz University Hannover, Hannover 30167, Germany 2. CIRTech Institute, HUTECH University, Ho Chi Minh City 700000, Vietnam 3. College of Civil Engineering, Tongji University, Shanghai 200092, China
Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures. Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time. To address this issue, we explore the potential of deep learning (DL) to increase the efficiency of crack detection and forecasting crack growth. However, there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary. In the paper, we present DL models for identifying cracks, especially on concrete surface images, and for predicting crack propagation. Firstly, SegNet and U-Net networks are used to identify concrete cracks. Stochastic gradient descent (SGD) and adaptive moment estimation (Adam) algorithms are applied to minimize loss function during iterations. Secondly, time series algorithms including gated recurrent unit (GRU) and long short-term memory (LSTM) are used to predict crack propagation. The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results. For evaluation of crack propagation, GRU and LSTM are used as DL models and results show good agreement with the experimental data.
Corresponding Author(s):
H. NGUYEN-XUAN,Xiaoying ZHUANG
引用本文:
. [J]. Frontiers of Structural and Civil Engineering, 2024, 18(4): 516-535.
Than V. TRAN, H. NGUYEN-XUAN, Xiaoying ZHUANG. Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning. Front. Struct. Civ. Eng., 2024, 18(4): 516-535.
60 mm × 50 mm × 12.5 mm (length × width × thickness)
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–
initial crack length
15 mm
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–
loading type
tension-tension, constant amplitude
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–
Tab.5
Fig.22
Model
MAE
MSE
RMSE
R2
GRU
0.7636
0.7119
0.8437
0.9588
LSTM
0.9012
1.3454
1.1599
0.9221
Tab.6
Fig.23
Fig.24
Fig.25
Model
MAE
MSE
RMSE
R2
GRU
0.0435
0.0020
0.0449
0.9823
LSTM
0.0920
0.0086
0.0926
0.9247
Tab.7
Fig.26
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