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A machine learning regression approach for predicting basal heave stability of braced excavation in non-homogeneous clay

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Abstract

This study employs a two-dimensional plane strain finite element limit analysis method to examine the stability of fully-braced excavations in non-homogeneous clay. The excavation has a width (B), a depth (H), and an embedment depth of the rigid wall (D). The clay is considered to obey the Tresca failure criterion in the simulation of the finite element limit analysis. The rigid wall is set to be fully braced and simulated using rigid plates by disallowing the rotation of the wall. A comprehensive parametric analysis is executed to derive the non-dimensional stability number (N) in terms of the maximum unit weight of soil (γ), the depth of excavation (H), and the undrained shear strength of the clay at the ground surface (su0) as a ratio of γH/su0. This stability number can be expressed as a function of the normalized width of excavation (B/H), the normalized depth of embedment (D/H), and the strength gradient ratio (ρH/su0). The relationship between these parameters to the stability number is investigated and the influence of these parameters on the potential failure mechanisms is discussed in detail. Moreover, a design equation for predicting this stability problem is developed through a machine learning regression approach called multivariate adaptive regression splines (MARS) model which can be extensively employed by practitioners in the field.

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Data availability

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The first author would like to thank the Thammasat School of Engineering, Thammasat University, for the graduate scholarship. We would like to thank Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for the support of time and facilities for this study. This research was supported by the Thammasat University Research Unit in Structural and Foundation Engineering, Thammasat University.

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Authors and Affiliations

  1. Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand

    Wittawat Yodsomjai, Rungkhun Banyong, Chanachai Thongchom & Suraparb Keawsawasvong

  2. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Van Qui Lai

  3. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Van Qui Lai

  4. Civil Engineering Department, Madan Mohan Malaviya University of Technology, Gorakhpur, 273010, India

    Vinay Bhushan Chauhan

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  1. Wittawat Yodsomjai
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  2. Van Qui Lai
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  3. Rungkhun Banyong
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  4. Vinay Bhushan Chauhan
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  5. Chanachai Thongchom
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  6. Suraparb Keawsawasvong
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Correspondence to Suraparb Keawsawasvong.

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Yodsomjai, W., Lai, V.Q., Banyong, R. et al. A machine learning regression approach for predicting basal heave stability of braced excavation in non-homogeneous clay. Arab J Geosci 15, 873 (2022). https://doi.org/10.1007/s12517-022-10161-y

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