Study on the Method of Detecting Voids Behind Subway Tunnel Lining

Data Segmentation of Continuous Impact Echo Signal

Abstract

When using the impact echo method to detect the void behind the tunnel lining, the continuous impact detection scheme helps to improve the detection efficiency, but it also brings a new challenge: multiple impact echo signals are superimposed and mixed during the recording process, which makes it difficult to independently process each impact event in the subsequent time-frequency analysis. In order to improve the accuracy and efficiency of signal processing, this paper proposes and develops a signal segmentation algorithm based on peak feature extraction, and introduces time window constraints to divide the entire signal into several independent sub-segments, thereby effectively reducing the degree of signal mixing. In practical applications, the data processing time of this segmentation method at the same measuring point is about 10 minutes, which is only 30%~50% of the processing time of the entire data segment; and the segmented multiple signal segments can be processed in parallel, and its segmentation processing efficiency is about 2 to 3 times that of the entire signal segment. By independently analyzing each segment of the signal, not only the subsequent time-frequency conversion and other processing procedures are simplified, the signal processing efficiency is improved, but also the accuracy of signal analysis is significantly improved, which provides theoretical and technical support for the application of the impact echo method in the analysis of tunnel segment void inspection data in the future.