## 4 Conclusions and Future Work  
To boost the effectiveness of pile recognition work, this research studies the reflected wave signal characteristics of foundation piles in layered soils using the traditional low-strain reflected wave method. In order to automatically classify and identify pile flaws, the signal is processed to extract feature indicators from three dimensions: time domain, frequency domain, and time-frequency domain. This is done in conjunction with a neural network. The following are the primary conclusions. 1. When the ICEEMDAN technique is utilized to decompose reflected wave signals from foundation piles under paved soil conditions, it lays the foundation for the accuracy of CNN identification. The correlation coefficient criterion filters the valuable components, removing the redundant components. 2. The convolutional neural network was used to classify and identify each group, and the feature sets extracted from the three dimensions were fed into the network with an accuracy of more than 90%, proving the excellent reliability of the CNN model for the detection of the foundation pile problem. 3. When single-domain features for foundation pile recognition were fed into the CNN model, the results were 88.89% for time domain features, 77.22% for frequency domain features, and 86.11% for time-frequency domain features. The accuracy of the multi-feature extraction method in conjunction with CNN is 97.8%. This method has high  
Figure 16: PNN model classification prediction results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Number of Test**} & \multicolumn{2}{c}{**Correct Recognition**} & \multirow{2}{*}{**Accuracy**} \\  & **Samples** & **Number** & \\ \hline
1-G & 40 & 60 & 45.5\% \\ H-2 & 40 & 1 & 2.5\% \\
2-I & 40 & 9 & 25.0\% \\ H-3 & 40 & 0 & 0 \\
3-I & 40 & 0 & 0\% \\ H-4 & 40 & 0 & 0\% \\
4-I & 40 & 0 & 0\% \\ H-5 & 40 & 33 & 84.6\% \\
5-I & 40 & 0 & 0\% \\ \multicolumn{3}{c}{Average accuracy 28.6\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: PNN ’s accuracy for different scenarios. accuracy and effectively distinguishes the kind of foundation pile when the soil surrounding the pile is stratified, increasing the inspectors' effectiveness. Due to the irregular geometries of foundation pile flaws in real projects and the complexity of the geological environment, practical engineering necessitates more advanced detecting tools and data processing approaches. The accuracy of foundation pile identification utilizing the low-strain reflection wave method in layered soils may be significantly improved, and foundation pile flaws in layered soils can be automatically diagnosed in this study. These findings are significant for technical applications and have some theoretical importance. Although this paper's pile defect identification model achieves good identification results based on supervised learning, the data sample cannot contain all the pile defect data in the project, and the pile defects are unknown. As a result, it is necessary to explore how to achieve high accuracy in the small sample and apply unsupervised learning algorithms in pile defects to improve the theoretical significance and practical value. Conceptualization, C.-S.W. ; Data curation, C.-S.W. and J.-Q.Z. ; Formal analysis, C.-S.W. ; Funding acquisition, D.-B.Z. and Z.-Y.F. ; Investigation, T.-Q.H. ; Methodology, T.-Q.H. ; Project administration, C.-S.W. ; Resources, D.-B.Z. ; Software, Y.-X.P. ; Supervision, L.-L.Q. and Y.-X.P. ; Validation, T.-Q.H., D.-B.Z., and Z.-Y.F. ; Visualization, J.-Q.Z. ; 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.