## 5 Intelligent Diagnostic Technology for Pile Inspection
### Feature-Based Machine Learning
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **NDT Method** & **AI Technique** & **Key Findings** & **Recommendations** \\ \hline \multirow{3}{*}{[PERSON] [43]} & \multirow{3}{*}{IR} & \multirow{3}{*}{Extreme Learning Machines (ELM)} & Proposed CARI methodology to interpret low-strain signals, enabling rapid screening of field piles for expert review. & Refine wavelet feature selection for micro-defects; expand datasets with multi-layer soil-pile interaction cases. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[PERSON] [44]} & \multirow{3}{*}{IR} & Two-Channel Convolutional Neural Network (CNN) fusing 1D/2D features & Classified 16 types of multi-defects with 99.17\% accuracy; leveraging wavelet-denoised 1D signals and 2D time-frequency maps. & Build low-SNR (signal-to-noise ratio 10 dB) datasets; integrate wave propagation physics to reduce overfitting. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[PERSON] [101]} & \multirow{3}{*}{SE} & Artificial Neural Network (ANN) with back-propagation & Developed ANN models for defect classification and damage severity assessment & Enhance training datasets with adversarial protection protocols for multi-site compatibility. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[PERSON] [102]} & \multirow{3}{*}{SE} & Neural network optimized by island genetic algorithm & Identified structural defects using FT signal features, achieving high reliability in simulated scenarios. & Integrate field-measured signals to bridge simulation-reality gaps; optimize network topology for multi-defect scenarios. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{Zhang [103]} & \multirow{3}{*}{SE} & \multirow{3}{*}{CWT + machine learning} & Achieved \textgreater{}99\% accuracy in ultrasonic signal onset detection, eliminating interference from waveform distortion via CWT preprocessing. & Replace manual feature engineering with deep learning; establish cross-device signal standardization. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[PERSON] [104]} & \multirow{3}{*}{SE} & \multirow{3}{*}{ANN classifier} & Automated defect identification in cast-in-place piles using FT spectral features, with high reliability for necking/bulging. & Incorporate multi-sensor fusion to reduce ambiguity \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[PERSON] [105]} & \multirow{3}{*}{AE} & Backpropagation Neural Network (BPNN) & Simulated damage locations in plugs using field AE data, enabling dynamic tracking of damage evolution. & Suppress ambient noise via adaptive denoising. \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{[PERSON] [106]} & \multirow{3}{*}{SE} & Multi-feature fusion + CNN & Achieved \textgreater{}97.8\% defect recognition accuracy under layered soil conditions by integrating multi-domain features. & Develop physics-guided explainable AI validate with diverse geological datasets. \\ \cline{3-5}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Recent studies on NDT-AI integration in pile integrity testing. ing numerical pile modeling. This methodology enables efficient screening of entire test piles while flagging a limited subset requiring expert manual interpretation. [PERSON] [102] simulated low-strain integrity tests using a combination of the finite element method (FEM) and the scaled boundary finite element method (SBFEM), thereby obtaining an adequate data set. The time-domain signal is pre-processed via FT, with features extracted by signal subtraction. Input-vector formation is depicted in Figure 13. Finally, an island genetic algorithm optimizes network topology to identify pile defects while ensuring high accuracy in necking or bulging detection. Figure 11: Architecture of networks. (**a**) Model I: for pile defect classification; (**b**) Model II: for pile defect degree [101]. Figure 12: Overall structure of the proposed CARI methodology [43].