Open AccessRISK PREDICTION AND DIAGNOSIS OF WATER SEEPAGE IN OPERATIONAL SHIELD TUNNELS BASED ON RANDOM FOREST
Author(s) -
Yang Liu,
Hongyu Chen,
Limao Zhang,
Xianjia Wang
Publication year - 2021
Publication title -
journal of civil engineering and management
Language(s) - English
Resource type - Journals
eISSN - 1822-3605
pISSN - 1392-3730
DOI - 10.3846/jcem.2021.14901
Subject(s) - random forest , artificial neural network , resampling , support vector machine , spall , cross validation , rebar , computer science , engineering , reliability engineering , artificial intelligence , structural engineering
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.