# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 5 Conclusions and Discussion  
In conclusion, this study introduces a novel method for structural damage detection, combining the DCPS learning strategy and the Conformer network. The DCPS learning strategy ensures consistent classification results for the same input signal at different noise levels. By utilizing parallel channels at varying noise levels to supervise each other, this novel learning strategy enhances the learning of noise-robust features. The Conformer network, which merges the advantages of CNN and Transformer networks, captures both local and global characteristics of acceleration response signals. The effectiveness of the proposed method was validated by using the IASC-ASCE SHM benchmark structure numerical model and a four-story single-span steel-frame experiment. Notably, this is the first successful application of combining the DCPS learning strategy with the Conformer model for structural damage detection. The following conclusions can be drawn:  
1. 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. 2. The proposed model uses the DCPS learning strategy to make the model learn the consistency of different noise signals. Compared with the manual denoising method [34; 35], the proposed model improves the noise robustness of the model and is more suitable for engineering practices. 3. 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. 4. 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. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Floor/** & **CNN** & **LSTM** & **Multi-Head** & **CNN-LSTM** & **Transformer** & **Conformer** & **CNN+Con-** & **Conformer+** \\
**Direation** & & & & & & & & **Conformer** \\ \hline Third floor, & 76.33 & 86.37 & 84.50 & 87.60 & 85.95 & 91.72 & 92.78 & 98.77 \\ y direction. & 74.47 & 79.22 & 82.63 & 86.53 & 88.19 & 91.93 & 94.28 & 95.56 \\ Fourth floor, & 78.63 & 84.13 & 88.40 & 89.74 & 91.82 & 91.40 & 92.57 & 98.98 \\ \hline
**Average** & **78.43** & **83.92** & **85.79** & **86.63** & **87.60** & **91.71** & **93.05** & **96.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._In future work, to improve the understanding and representation of node features by the model and to learn more robust node representations, we will explore how to model the correlations between nodes of different structures through graph neural networks. Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON], [PERSON] and J.X. ; software, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON].