Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine

Abstract

Multi-step-ahead prediction of tunnel surrounding rock displacement is an effective way to ensure the safe and economical construction of tunnels. This paper presents a multi-step-ahead prediction model, which is based on support vector machine (SVM), for tunnel surrounding rock displacement prediction. To improve the training efficiency of SVM, shuffled complex evolution algorithm (SCE-UA) is also performed through some exponential transformation. The data from the Chijiangchong tunnel are used to examine the performance of the prediction model. Results show that SVM is generally better than artificial neural network (ANN). This indicates that SVM is a feasible and effective multi-step method for tunnel surrounding rock displacement prediction.