RISK PREDICTION AND DIAGNOSIS OF WATER SEEPAGE IN OPERATIONAL SHIELD TUNNELS BASED ON RANDOM FOREST

Yang Liu,Limao Zhang,Xianjia Wang,Hongyu Chen

doi:10.3846/jcem.2021.14901

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.

Highlights

With the increase in urbanization and population influx around the world, urban problems such as insufficient urban space capacity and frequent traffic jams are constantly emerging (Zho et al, 2019; He et al, 2019; Zhang et al, 2020a, 2020b)
From the prediction results for the three prediction models, random forest (RF), support vector machines (SVMs), and artificial neural network (ANN), we find that the root-mean-square error (RMSE) values are 0.047, 0.244, and 0.219 and that the R2 values are 0.991, 0.969, and 0.955
Because of the specific structure of the shield tunnel, many defects can occur during the operation of metro tunnels

Summary

Introduction

With the increase in urbanization and population influx around the world, urban problems such as insufficient urban space capacity and frequent traffic jams are constantly emerging (Zho et al, 2019; He et al, 2019; Zhang et al, 2020a, 2020b). Cheng and Huang (2014) found a total of approximately 20,000 WS occurrences by investigating all operational metro routes in Shanghai, and WS is an important risk factor for other defects. Dong et al (2017) investigated shield tunnels in Beijing and found that approximately 77% of the defects were related to WS This is due to the special structure of the shield tunnel, such as the use of segment splicing and grouting holes on each segment, which increases the possibility of metro tunnel leakage (Li et al, 2019).

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Results

Conclusion