Machine learning-based identification of segment joint failure in underground tunnels

Abstract

Shield tunnels that reside deep within soft soil are subject to longitudinal differential settlement and structural deformation during long-term operation. Longitudinal deformation can be classified into two modes: bending and dislocation deformation. The failure of bolts and engineering treatment techniques differ between these two modes. Therefore, it is imperative to accurately identify the tunnel's longitudinal deformation mode to determine the validity of the segment joint and implement appropriate engineering treatment. Traditional methods for detecting dislocation or opening suffer from high labour costs. To address this issue, this study presents an innovative identification method using a back-propagation neural network (BPNN) to detect segment joint failure in underground tunnels. First, this study collects the tunnel settlement curves of various subways located in the East China soft soil area, and it calculates tunnel settlement-dislocation and settlement-opening datasets using the equivalent axial stiffness model. A corresponding BPNN regression model is subsequently established, and the new settlement curve is the input to this regression model to predict the dislocation and opening, thereby determining the validity of the segment joint. The efficiency of this method is demonstrated through its successful application to the Hangzhou Metro Tunnel. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm

“…The Back-Propagation (B-P) neural network, a prevalent deep learning neural network algorithm, has demonstrated substantial applicability (Wei et al, 2020). Incorporating machine learning techniques in biomedical research has facilitated the screening of novel biomarkers and the creation of more sophisticated diagnostic models (Chen and Wei, 2023;Gao et al, 2023;Jin et al, 2023). This methodological advancement appears promising in elucidating biomarkers critical for effective ATB management and diagnosis.…”

Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'

“…The prediction performance of BP was verified by comparing the TOPSIS prediction model and the cloud model. Jin et al [10] conducted a machine learning-based identification of segment joint failure in underground tunnels. The traditional method of detecting dislocation or opening has the problem of high labour costs.…”

“…Jin et al . [ 10 ] conducted a machine learning-based identification of segment joint failure in underground tunnels. The traditional method of detecting dislocation or opening has the problem of high labour costs.…”