Scientia Iranica

Scientia Iranica

Improved Support Vector Machine Regression in Multi-Step-Ahead Prediction for Tunnel ‎Surrounding Rock Displacement ‎

Authors
Baozhen YAO ‎ 1
Baozhen YAO ‎ 1
Baozhen YAO ‎ 1
Jinbao YAO ‎ 2
Jinbao YAO ‎ 2
Jinbao YAO ‎ 2
Mingheng ZHANG ‎ 1
Lan YU ‎ 3
Lan YU ‎ 3
Lan YU ‎ 3
1 School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, P.R.China ‎
2 School of Civil Engineering & Architecture, Beijing Jiaotong University, Beijing, 100044, P.R.China
3 Yanching Institute of Technology, Beijing, 100044, 065201 P.R.China
Abstract
A dependable long-term prediction of tunnel surrounding rock displacement is an effective way to predict the rock displacement values into the future. A multi-step-ahead prediction model, which is based on support vector machine (SVM), is proposed for tunnel surrounding rock displacement prediction. To improve the performance of SVM, parameter identification is used for SVM. In addition, to treat with the time-varying features of tunnel surrounding rock displacement, a forgetting factor is introduced to adjust the weights between new and old data.At last, the data from the Chijiangchong tunnel are selected to examine the performance of the prediction model. Comparative results were presented between SVMFF (SVM with a forgetting factor) and artificial neural network with a forgetting factor (ANNFF) show that SVMFF is generally better than ANNFF. This indicates that a forgetting factor can effectively improve the performance of SVM, especially for the time-varying problems.
Keywords
Multi-Step-Ahead Prediction
Tunnel
Surrounding Rock Displacement
SVM
Forgetting Factor
Scientia Iranica
Volume 21, Issue 4
Transactions on Civil Engineering (A)
July and August 2014
Pages 1309-1316

  • Receive Date 27 November 2013
  • Accept Date 09 July 2017