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Prediction of the Stability of Various Tunnel Shapes Based on Hoek–Brown Failure Criterion Using Artificial Neural Network (ANN)

Author

Listed:
  • Thira Jearsiripongkul

    (Department of Mechanical Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12120, Thailand)

  • Suraparb Keawsawasvong

    (Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12120, Thailand)

  • Chanachai Thongchom

    (Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12120, Thailand)

  • Chayut Ngamkhanong

    (Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

Abstract

In this paper, artificial neural network (ANN) models are presented in order to enable a prompt assessment of the stability factor of tunnels in rock masses based on the Hoek–Brown (HB) failure criterion. Importantly, the safety assessment is one of the serious concerns for constructing tunnels and requires a reliable and accurate stability analysis. However, it is challenging for engineers to construct finite element limit analysis (FELA) algorithms with the HB failure criterion for tunnel stability solutions in rock masses. For the first time, a machine-learning-aided prediction of tunnel stability based on the HB failure criterion is proposed in this paper. Three different shapes of tunnels, i.e., heading tunnel, dual square tunnels, and dual circular tunnels, are considered. The inputs include four dimensionless parameters for the heading tunnel including the cover-depth ratio, the normalized uniaxial compressive strength, the geological strength index ( GSI ), and the m i parameter. Moreover, dual square and circular tunnels include one more additional parameter namely the distance ratio. The results present the best ANN models for each tunnel shape, providing very reliable solutions for predicting the tunnel stability based on the HB failure criterion.

Suggested Citation

  • Thira Jearsiripongkul & Suraparb Keawsawasvong & Chanachai Thongchom & Chayut Ngamkhanong, 2022. "Prediction of the Stability of Various Tunnel Shapes Based on Hoek–Brown Failure Criterion Using Artificial Neural Network (ANN)," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4533-:d:791118
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    References listed on IDEAS

    as
    1. Mohammad Khajehzadeh & Suraparb Keawsawasvong & Moncef L. Nehdi, 2022. "Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations," Sustainability, MDPI, vol. 14(3), pages 1-20, February.
    2. Sayan Sirimontree & Thira Jearsiripongkul & Van Qui Lai & Alireza Eskandarinejad & Jintara Lawongkerd & Sorawit Seehavong & Chanachai Thongchom & Peem Nuaklong & Suraparb Keawsawasvong, 2022. "Prediction of Penetration Resistance of a Spherical Penetrometer in Clay Using Multivariate Adaptive Regression Splines Model," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
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