Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks

Abstract

As an essential component of urban infrastructure construction, Polyethylene (PE) pipelines face the challenging task of underground detection due to the complex and dynamic nature of the subsurface environment, diverse installation paths, and the inherent insulating properties of PE materials. In order to address the non-excavation detection of buried PE pipelines, this paper proposes an acoustic method based on the Long Short-Term Memory (LSTM) neural network. The study begins by analyzing the propagation and reflection mechanisms of elastic waves in the pipe-soil coupling system, and a impact excitation source is designed to generate the excitation signal. After establishing the experimental environment and collecting experimental data, a comprehensive analysis is conducted, and the LSTM neural network is employed for data classification to determine the presence of buried PE pipelines. Through neural network training, accurate identification of the PE pipeline's existence and prediction of its burial depth are achieved, providing an efficient and reliable solution for buried PE pipeline detection. The practical results demonstrate the significant application prospects of the combined acoustic method and LSTM neural network in buried PE pipeline detection. This research contributes a novel solution to the field of non-destructive PE pipeline detection, with both theoretical and practical implications.