Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology

Abstract

The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data. First, the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments. In particular, the damage location is identified using two parameters: the pile location (P_L) and the distance from the pile cap (D_S). This study investigates the influences of various acoustic emission parameters, numbers of sensors, sensor installation locations, and the time difference on the prediction accuracy of P_L and D_S. In addition, correlations between the damage location and acoustic emission parameters are investigated. Second, the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments. Finally, a new damage detection and evaluation method for pile foundations is proposed. This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.

Key words： pile foundations; damage location; acoustic emission; deep learning; damage step

Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2019R1G1A1100517), the Fundamental Research Funds for the Central Universities (N170108029), the National Natural Science Foundation of China (Grant Nos. U1602232 and 51474050), and China Government Scholarship (201806080061); all of the above-mentioned funding sources and kind help are gratefully acknowledged.

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