Prediction of Metro Train‐Induced Tunnel Vibrations Using Machine Learning Method

Abstract

The tunnel vibration level is usually employed as a vibration source intensity of the empirical prediction method. Currently, the analogy test and data base are two main means to determine the vibration source intensity. To improve the accuracy efficiency, the machine learning (ML) method was introduced to predict the tunnel vibration responses. To acquire model training samples, the measurements were performed in 80 different running tunnel sections of Beijing metro lines. Two types of method, back propagation neural network (BPNN) and generalised regression neural network (GRNN) were employed, which can make full use of characteristics of measured samples and reduce the data noise. The results indicate that the prediction efficiency is high and the mean square errors of the two ML methods are acceptable. Accordingly, both of the ML methods can be used as the reference of vibration source intensity in metro train‐induced environmental impact evaluation. GRNN has relatively better predicting ability than BPNN.