## 4 Conclusions  
The data set for the results of this paper consists of a large number of numerical simulations. Although not as convincing as the actual experimental data, the method of this study is equally applicable in the future when a sufficient database of pile foundation defects is available. This paper uses Python script to develop the finite element software, and the multi-defect pile foundation model realized adaptive meshing. The script can quickly simulate six multi-defect pile foundations' velocity-time-domain curves. The velocity-time domain curve of the pile foundation is also a critical database source for the TC-CNN model. The LSPIT signal is a crucial feature source for the TC-CNN model. Therefore, firstly, the LSPIT signal needs to be denoised by a wavelet packet and then used as the input signal for the 1D-CNN model and wavelet transform, together with the original signal. Secondly, feature extraction and feature fusion are performed on the LSPIT signal and wavelet time-frequency map, respectively, by using two-channel CNN. Finally, this model used the softmax layer to classify multi-defective piles. The data set is 200 signal samples for each defective pile, totaling 1200. The training set (840), validation set (240), and test set (120) were divided into 7:2:1, and the TC-CNN model correctly classified 359 signals in 360 validation and test sets. The conclusions are as follows. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Learning** & \multirow{2}{*}{**Epochs**} & \multirow{2}{*}{**Batch Size**} & **Training** & **Validation** & **Test Set** \\  & **Rate** & & & **Set (\%)** & **Set (\%)** & **(\%)** \\ \hline
1D-CNN & 0.005 & 200 & 64 & 99.38 & 92.50 & 85.00 \\
2D-CNN & 0.005 & 200 & 64 & 100.00 & 97.08 & 92.50 \\ TC-CNN & 0.005 & 200 & 64 & 100.00 & 100.00 & 99.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of defect recognition accuracy. Figure 13: The mapping characteristics of input data and output results. (**a**) Input data; (**b**) Output results. (1) Due to the influence of environmental noise and rebound waves, the LSPIT signal waveforms of multi-defect pile foundations are more complex than those of single-defect pile foundations. The accuracy of identifying the defect types only manually and from experience is low. Therefore, denoising the signal and then using computer techniques for feature extraction will help eliminate these factors' effects. (2) Using the time-frequency analysis method can convert the one-dimensional time-domain data, which CNN is not good at processing, into two-dimensional image data, which increases the diversity of features and complements the defect information. (3) Compared with the recognition effect of the traditional single-channel CNN model, the dual-channel CNN model shows good robustness and stability by fusing the features of a single channel, which also improves the accuracy of defect recognition. (4) This study only denoises the original time-domain signal. Theoretically, if the input 2D-CNN image is also denoised, the feature differences between various multi-defect piles may be more prominent. It can reduce the training time of the TC-CNN model while maintaining high recognition accuracy. Therefore, researchers can combine advanced image-denoising technology in subsequent research to achieve further improvement. Conceptualization, C.-S.W. ; data curation, C.-S.W. and J.-Q.Z. ; formal analysis, C.-S.W. ; funding acquisition, D.-B.Z. ; investigation, M.G. ; methodology, M.G. ; project administration, C.-S.W. ; resources, D.-B.Z. ; software, T.-Q.H. ; supervision, L.-L.Q. and Y.-X.P. ; validation, J.-Q.Z., D.-B.Z. ; visualization, T.-Q.H. ; writing--original draft, C.-S.W. ; writing--review and editing, L.-L.Q. All authors have read and agreed to the published version of the manuscript. This research was supported by the Fund of National-local Joint Engineering Laboratory for Road Engineering and Disaster Prevention and Mitigation Technology in Mountainous Areas (No.