Tunnel lining quality detection technology based on impulse echo acoustic method from fine management perspective

Ensuring tunnel linings’ quality is crucial for tunnel structures’ safety and long-term functionality. This study focuses on exploring tunnel lining quality detection technology based on the impulse echo acoustic method to enhance detection efficiency and accuracy. Initially, a series of tunnel lining samples are collected, and acoustic tests are conducted to obtain the acoustic characteristics of the lining materials. Subsequently, the impulse echo acoustic method is employed to detect the tunnel lining, followed by frequency spectrum analysis and waveform analysis of the acoustic signals. A comparison is made between the analyzed signals and simulated data or standard quality requirements. The proposed method involves the generation of impact on the lining and recording the resulting acoustic echo signal. Signal processing techniques and machine learning algorithms are applied to evaluate and classify the lining quality based on the distinctive features of the acoustic signals. Experimental results reveal that the tunnel lining quality detection technology achieves a remarkable accuracy rate of over 96% from the perspective of fine management. This approach enables the swift identification of defects, cracks, and damages in lining materials while providing quantitative quality assessment. The application potential of the tunnel lining quality detection technology based on the impulse echo acoustic method in fine management has been substantiated, as it effectively assesses the quality of tunnel linings and offers robust support for the safe operation of tunnels.