Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

Authors

  • Guixing Kuang
    Affiliation
    Department of Architecture and Environment, Faculty of Civil Engineering, Sichuan University, Chengdu 610065, China
  • Bixiong Li
    Affiliation
    Department of Architecture and Environment, Faculty of Civil Engineering, Sichuan University, Chengdu 610065, China
  • Site Mo
    Affiliation
    Department of Electrical Engineering, Sichuan University, Chengdu 610065, China
  • Xiangxin Hu
    Affiliation
    Department of Architecture and Environment, Faculty of Civil Engineering, Sichuan University, Chengdu 610065, China
  • Lianghui Li
    Affiliation
    Department of Architecture and Environment, Faculty of Civil Engineering, Sichuan University, Chengdu 610065, China
https://doi.org/10.3311/PPci.19859

Abstract

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected.

Keywords:

shield tunnel, defect detection, machine learning, crack, water leakage

Citation data from Crossref and Scopus

Published Online

2022-06-30

How to Cite

Kuang, G., Li, B., Mo, S., Hu, X., Li, L. “Review on Machine Learning-based Defect Detection of Shield Tunnel Lining”, Periodica Polytechnica Civil Engineering, 66(3), pp. 943–957, 2022. https://doi.org/10.3311/PPci.19859

Issue

Section

Review Article