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Mathematical Problems in Engineering
Volume 2014, Article ID 351496, 6 pages
http://dx.doi.org/10.1155/2014/351496
Research Article

Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

1Shandong Luqiao Group CO., Ltd, Jinan 250021, China
2Transportation Management College, Dalian Maritime University, Dalian 116026, China
3China Academy of Civil Aviation Science and Technology, Beijing 100028, China
4School of Automotive Studies, Tongji University, Shanghai 201804, China
5School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Received 5 May 2014; Revised 13 July 2014; Accepted 14 July 2014; Published 23 July 2014

Academic Editor: Rui Mu

Copyright © 2014 Qingdong Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data of Fangtianchong tunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.