Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine

DOI

10.2991/ijcis.2010.3.6.14How to use a DOI?

Keywords

Prediction; Tunnel; Surrounding Rock Displacement; SVM; SCE-UA; Machine Learning

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.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Jian Sun
AU  - Bao-Zhen Yao
AU  - Cheng-Yong Yang
AU  - Jin-Bao Yao
PY  - 2010
DA  - 2010/12/01
TI  - Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine
JO  - International Journal of Computational Intelligence Systems
SP  - 843
EP  - 852
VL  - 3
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.6.14
DO  - 10.2991/ijcis.2010.3.6.14
ID  - Sun2010
ER  -