Application and Comparison of Machine Learning Algorithms for Predicting Rock Deformation in Hydraulic Tunnels

Abstract

Prediction of tunnel surrounding rock deformation is important for tunnel construction safety evaluation. In this paper, machine learning algorithms are used to carry out a comparative study of the surrounding rock deformation prediction. The applications of Gaussian process regression (GPR), support vector machine (SVM), and long short-term memory network (LSTM) in the prediction of surrounding rock deformation sequences are compared and analyzed. The actual data of a diversion tunnel in a southwest region are used as an example to evaluate and compare the single-step prediction model and multistep prediction model established by the above algorithm. The results show that the machine learning algorithm has good operation effect on the prediction of surrounding rock deformation. Overall, the SVM model has the best prediction effect and outperforms the other two algorithms in terms of tracking the trend of data changes and the degree of data fit.