Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153884
Title: Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest
Authors: Liu, Yang
Chen, Hongyu
Zhang, Limao
Wang, Xianjia
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: 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://dx.doi.org/10.3846/jcem.2021.14901
Journal: Journal of Civil Engineering and Management
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.
URI: https://hdl.handle.net/10356/153884
ISSN: 1392-3730
DOI: 10.3846/jcem.2021.14901
Rights: © 2021 The Author(s). Published by Vilnius Gediminas Technical University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unre-stricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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