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

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  • Qingdong Wu
  • Bo Yan
  • Chao Zhang
  • Lu Wang
  • Guobao Ning
  • B. Yu

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.

Suggested Citation

  • Qingdong Wu & Bo Yan & Chao Zhang & Lu Wang & Guobao Ning & B. Yu, 2014. "Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, July.
    • Handle: RePEc:hin:jnlmpe:351496
    DOI: 10.1155/2014/351496
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    Cited by:

    1. Jie Hu & Yi Peng & Xueliang Chen & Hangyan Yu, 2021. "Differentiating the learning styles of college students in different disciplines in a college English blended learning setting," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-26, May.

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