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Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest

    Yang Liu Affiliation
    ; Hongyu Chen Affiliation
    ; Limao Zhang Affiliation
    ; Xianjia Wang Affiliation

Abstract

Water seepage (WS) is a paramount defect during tunnel operation and directly affects the operational safety of tunnels. Effectively predicting and diagnosing WS are problems that urgently need to be solved. This paper presents a standard and an evaluation index system for WS grades and constructs a sample dataset from monitoring recoreds for demonstration purposes. First, we use bootstrap resampling to build a random forest (RF) seepage risk prediction model. Second, the optimal branch and parameters are selected by the 5-fold cross-validation method to establish the RF prediction training model. Additionally, to illustrate the effectiveness of the method, the operational stage of Wuhan Metro Line 3 in China is taken as a case study. The results conclude that the segment spalling area, crack width, and loss rate of the rebar cross-section have a strong influence on WS. Finally, the test data are predicted, and the prediction result error index is calculated. Compared with the predictions of some traditional machine learning methods, such as support vector machines and artificial neural networks, RF prediction has the highest accuracy and is the closest to the true value, which demonstrates the accuracy of the model and its application potential.

Keyword : operational tunnels, water seepage (WS), random forest (RF), risk prediction, risk diagnosis

How to Cite
Liu, Y., Chen, H., Zhang, L., & Wang, X. (2021). Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest. Journal of Civil Engineering and Management, 27(7), 539-552. https://doi.org/10.3846/jcem.2021.14901
Published in Issue
Oct 11, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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