Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
Abstract
:1. Introduction
2. Methodology
2.1. The Equation of Motion
2.2. Data Preprocessing
2.3. Dual-Channel Pseudo-Supervised (DCPS) Learning
2.4. Conformer Module
2.5. The Process of the Algorithm
3. Validation by a Numerical Model
3.1. IASC-ASCE SHM Benchmark Dataset
3.2. Network Models and Settings
- One-dimensional CNN: This model can be used to process acceleration response data, where the convolution layer can capture the local features of the signal. In this paper, two convolutional kernels of size 5 were used for convolution operations.
- LSTM: The LSTM model can capture long-range dependencies in sequence data through the gate-control mechanism, enabling the learning of crucial long-range dependency features. This model was constructed as a two-layer bidirectional LSTM with the dimension of 128 in this paper.
- Multi-head CNN: By introducing multiple parallel convolutional branches (heads), this model can learn features at different scales. Each branch focuses on different time- or frequency-domain information, and the multiple branches are eventually fused to provide a comprehensive description of the structural damage.
- CNN-LSTM: This model, in this paper, first used a 1D CNN with a convolutional kernel of 15 to extract spatial features. Then, a two-layer LSTM model with 256 dimensions was used for long-sequence modeling.
- Transformers: Through the self-attention mechanism, the transformer model can effectively capture global dependencies in sequences. In structural damage detection, it models the acceleration response data collected by sensors to learn features related to identifying damage. In this paper, we constructed a four-layer Transformer block with 8 heads and with a dimension of 512.
3.3. Comparative Experimental Results
3.4. Ablation Study
3.5. Comparative Analysis at Different Noise Levels
4. Experimental Verification on a Four-Story Steel-Frame Structure
4.1. Experimental Description
4.2. Experimental Procedure
4.3. Experimental Results
5. Conclusions and Discussion
- Existing methods focus on either local information [19,20,21,22,23] or global information [24,25,26]. Although some works combine the advantages of CNNs and LSTM [27,28,29], the weak performance of LSTM in processing long-term dependencies of signals is still inevitable. The proposed model uses a Conformer as the backbone network, effectively combining the local feature extraction capability of CNNs with the long-term sequence modeling capability of Transformers to fully describe the local and global information of the response signal.
- The proposed models, CNN+Conformer and Conformer+Conformer, achieve average identification accuracies of 95.42% and 96.15% in numerical simulations and 93.05% and 96.64% in laboratory experiments, markedly outperforming baseline models. This high accuracy underscores the potential of the proposed model as an effective solution for structural damage detection and demonstrates its robust to different scenarios.
- The proposed method achieves average accuracies of 96.3% and 93.0% at 20% and 50% noise levels, respectively, significantly outperforming baseline models. These results demonstrate the ability of the proposed model to learn noise-robust features without manual denoising. This method greatly simplifies the application process and improves detection accuracy in noisy environments.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Damage Mode | Mode Description |
---|---|
D.P.0 | No damage |
D.P.1 | No stiffness in the braces of the first story |
D.P.2 | No stiffness in any of the braces of the first and third stories |
D.P.4 | No stiffness in one brace in the first story+No stiffness in one brace in the third story |
Model | 1D CNN | LSTM | Multi-Head CNN | CNN-LSTM | Transformer | Conformer |
---|---|---|---|---|---|---|
ACC | 89.10 | 88.22 | 92.39 | 92.55 | 94.07 | 95.19 |
Time(s) | 0.57 × 10−3 | 3.2 × 10−3 | 1.0 × 10−3 | 2.9 × 10−3 | 1.0 × 10−3 | 1.6 × 10−3 |
Model | 1D CNN | Conformer | CNN+CNN | CNN+Conformer | Conformer+Conformer |
---|---|---|---|---|---|
ACC | 89.10 | 95.19 | 94.23 | 95.99 | 96.96 |
Model | CNN+CNN | CNN+Conformer | Conformer+Conformer | |||
---|---|---|---|---|---|---|
Noise handling of the training set | Noisy | Noiseless | Noisy | Noiseless | Noisy | Noiseless |
ACC | 94.23 | 93.43 | 95.99 | 95.91 | 96.96 | 96.88 |
Noise Level (%) | Direction | Floor | CNN | Multi-Head CNN | Conformer | CNN+Conformer * | Conformer+Conformer * |
---|---|---|---|---|---|---|---|
0 | x | 1 | 89.10 | 92.39 | 95.19 | 95.99 | 96.96 |
0 | x | 2 | 84.62 | 94.15 | 96.79 | 97.04 | 97.68 |
0 | x | 3 | 86.30 | 93.83 | 95.99 | 97.76 | 98.16 |
0 | x | 4 | 68.19 | 92.63 | 96.15 | 96.07 | 97.12 |
0 | y | 1 | 86.70 | 94.47 | 95.99 | 95.99 | 96.39 |
0 | y | 2 | 75.80 | 96.15 | 96.88 | 97.52 | 97.68 |
0 | y | 3 | 75.64 | 94.55 | 95.43 | 96.23 | 97.36 |
0 | y | 4 | 67.87 | 96.31 | 97.04 | 97.44 | 97.60 |
Average ACC at 0% noise level | 79.28 | 94.31 | 96.18 | 96.76 | 97.37 | ||
20 | x | 1 | 85.02 | 90.79 | 92.55 | 94.23 | 95.11 |
20 | x | 2 | 81.89 | 94.15 | 96.39 | 96.63 | 97.44 |
20 | x | 3 | 85.18 | 92.95 | 94.23 | 95.35 | 96.71 |
20 | x | 4 | 71.07 | 91.99 | 95.51 | 96.23 | 97.12 |
20 | y | 1 | 81.89 | 94.63 | 94.47 | 96.07 | 96.47 |
20 | y | 2 | 67.71 | 95.19 | 96.63 | 96.88 | 97.36 |
20 | y | 3 | 68.67 | 93.59 | 95.19 | 95.51 | 96.31 |
20 | y | 4 | 67.63 | 96.31 | 97.04 | 97.52 | 97.84 |
Average ACC at 20% noise level | 76.13 | 93.70 | 95.25 | 96.05 | 96.80 | ||
50 | x | 1 | 75.72 | 85.98 | 86.54 | 88.94 | 90.87 |
50 | x | 2 | 70.51 | 91.19 | 93.27 | 94.87 | 95.27 |
50 | x | 3 | 72.92 | 90.06 | 92.39 | 93.75 | 94.31 |
50 | x | 4 | 64.74 | 90.14 | 94.39 | 94.55 | 95.35 |
50 | y | 1 | 74.28 | 91.51 | 91.59 | 93.35 | 93.75 |
50 | y | 2 | 60.02 | 89.82 | 92.63 | 93.19 | 94.39 |
50 | y | 3 | 60.58 | 89.66 | 92.39 | 92.79 | 93.59 |
50 | y | 4 | 61.30 | 95.03 | 95.35 | 96.15 | 96.79 |
Average ACC at 50% noise level | 67.51 | 90.42 | 92.32 | 93.45 | 94.29 | ||
Average | 74.31 | 92.81 | 94.58 | 95.42 | 96.15 |
Damage Case | Description |
---|---|
D.P.0 | Without damage (the columns at the southeast corner of floors 1–4 all have a diameter of 16 mm). |
D.P.1 | Replaced the column on the first floor with a 14 mm-diameter column. |
D.P.2 | Replaced the column on the second floor with a 14 mm-diameter column. |
D.P.3 | Replaced the column on the third floor with a 14 mm-diameter column. |
D.P.4 | Replaced the column on the fourth floor with a 14 mm-diameter column. |
D.P.5 | Replaced the columns on the first and second floors with 14 mm- and 12 mm-diameter columns, respectively. |
Floor/ Direction | CNN | LSTM | Multi-Head CNN | CNN-LSTM | Transformer | Conformer | CNN+Conformer | Conformer+Conformer |
---|---|---|---|---|---|---|---|---|
First floor, x direction. | 80.98 | 84.45 | 87.44 | 89.36 | 89.52 | 91.18 | 93.53 | 94.33 |
First floor, y direction. | 77.78 | 82.10 | 85.73 | 83.17 | 87.01 | 93.37 | 94.28 | 98.93 |
Second floor, x direction. | 76.01 | 86.59 | 84.45 | 83.49 | 86.43 | 91.93 | 92.41 | 94.12 |
Second floor, y direction. | 82.05 | 84.93 | 88.46 | 88.35 | 87.23 | 93.48 | 94.92 | 99.57 |
Third floor, x direction. | 81.20 | 83.60 | 84.72 | 84.82 | 84.61 | 88.67 | 89.63 | 92.84 |
Third floor, y direction. | 76.33 | 86.37 | 84.50 | 87.60 | 85.95 | 91.72 | 92.78 | 98.77 |
Fourth floor, x direction. | 74.47 | 79.22 | 82.63 | 86.53 | 88.19 | 91.93 | 94.28 | 95.56 |
Fourth floor, y direction. | 78.63 | 84.13 | 88.40 | 89.74 | 91.82 | 91.40 | 92.57 | 98.98 |
Average | 78.43 | 83.92 | 85.79 | 86.63 | 87.60 | 91.71 | 93.05 | 96.64 |
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Hu, T.; Ma, K.; Xiao, J. Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning. Appl. Sci. 2024, 14, 7377. https://doi.org/10.3390/app14167377
Hu T, Ma K, Xiao J. Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning. Applied Sciences. 2024; 14(16):7377. https://doi.org/10.3390/app14167377
Chicago/Turabian StyleHu, Tianjie, Kejian Ma, and Jianchun Xiao. 2024. "Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning" Applied Sciences 14, no. 16: 7377. https://doi.org/10.3390/app14167377