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Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study

Author

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  • Min Hu
  • Wei Li
  • Ke Yan
  • Zhiwei Ji
  • Haigen Hu

Abstract

Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms.

Suggested Citation

  • Min Hu & Wei Li & Ke Yan & Zhiwei Ji & Haigen Hu, 2019. "Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, April.
    • Handle: RePEc:hin:jnlmpe:7057612
    DOI: 10.1155/2019/7057612
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    Cited by:

    1. Qu, Pengfei & Zhang, Limao & Zhu, Qizhi & Wu, Maozhi, 2023. "Probabilistic reliability assessment of twin tunnels considering fluid–solid coupling with physics-guided machine learning," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Lin, Penghui & Zhang, Limao & Tiong, Robert L.K., 2023. "Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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