Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels

Abstract

The surface defects on a shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise operation safety. To effectively detect multiple surface defects, this study uses a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting an inspection of the shield tunnel on a metro line section, various surface defects are identified with the TIT, including water leakage defects, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, association rules between different defects are calculated using an improved Apriori algorithm. The results show that: (i) there are significant differences in different association rules for various surface defects on the shield tunnel; (ii) the average confidence of the association rule “dislocation & spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; (iii) the weakest rule appears at “water leakage → spalling”, with an average confidence of 13%. The association analysis can be used for predicting the critical defects influencing structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for a shield subway tunnel.

Invisible track bed defect classification method based on distributed optical fiber sensing system and FFT Attention Transformer model

“…The association rules of different defects were calculated by improving the prior algorithm to predict the key defects affecting structural reliability and operational safety. 1 To address conspicuous abnormal conditions, such as track cracks, foreign objects on subway sleepers, and train bearing defects, scholars frequently employ image processing technology to enable detection. This is achieved through the extraction of image edge information, 2 the integration of deep learning technology, 3 the enhancement of deep feature fusion algorithms, 4 and the proposal of novel network architectures.…”

“…proposed a tunnel inspection trolley based on mobile laser scanning technology to detect shield tunnels on subway lines. The association rules of different defects were calculated by improving the prior algorithm to predict the key defects affecting structural reliability and operational safety 1 . To address conspicuous abnormal conditions, such as track cracks, foreign objects on subway sleepers, and train bearing defects, scholars frequently employ image processing technology to enable detection.…”