Abstract: With the continuous expansion of building scale, the structural safety of foundation piles, as key load-bearing components, has received increasing attention. To improve the defect recognition ability under complex working conditions, this study first uses the whale optimization algorithm to perform hyperparameter optimization on the long short-term memory network model, achieving efficient classification of the defect and non-defect samples. Subsequently, the signals identified as having defects are subjected to wavelet packet decomposition to extract multi-scale energy features, and combined with the local peak finding method to accurately locate key reflection peaks, achieving further identification of defect types. The results showed that the classification accuracy, recognition precision, recall rate, and F1 value of the new method were the highest at 96.7%, 95.16%, 93.87%, and 94.51%, respectively, and the average recognition time was the shortest at 0.97 seconds. Especially for the defect identification errors of drilled cast-in-place piles and prefabricated piles, the lowest were 0.19 and 0.23, and the lowest complexity could reach 65.28%, demonstrating high precision and stability in defect identification. This model has strong robustness and accuracy in various types of defect scenarios, and has good generalization ability and engineering application potential, which can provide certain technical references for the construction monitoring of road and bridge engineering in the future.

Xiaolin Li and Xinyi Chen, “Intelligent Identification of Pile Defects Based on Improved LSTM Model and Wavelet Packet Local Peaking Method” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160562

@article{Li2025,
title = {Intelligent Identification of Pile Defects Based on Improved LSTM Model and Wavelet Packet Local Peaking Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160562},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160562},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Xiaolin Li and Xinyi Chen}
}