Abstract
The aim of this study is to investigate the influence of rock variability on the failure mechanism and bearing capacity of strip footings. A probabilistic analysis of the bearing capacity of footings on rock masses is conducted in this paper, where random adaptive finite-element limit analysis (RAFELA) with the Hoek‒Brown yield criterion and the Monte Carlo simulation technique are combined. The stochastic bearing capacity is computed by considering various parameters, such as the mean values of the uniaxial compressive strength of intact rock, Hoek‒Brown strength properties, coefficient of variance, and correlation lengths. In addition to the RAFELA, this study introduces a novel soft-computing approach for potential future applications of bearing capacity prediction by employing a machine learning model called the eXtreme Gradient Boosting (XGBoost) approach. The proposed XGBoost model underwent thorough verification and validation, demonstrating excellent agreement with the numerical results, as evidenced by an impressive R2 value of 99.99%. Furthermore, Shapley's analysis revealed that the specified factor of safety (FoS) has the most significant influence on the probability of failure (PoF), whereas the geological strength index (GSI) has the most significant effect on the random bearing capacity (μNran). These findings could be used to enhance engineering computations for strip footings resting on Hoek‒Brown rock masses.
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No datasets were generated or analysed during the current study.
Abbreviations
- B :
-
Width of strip footing
- N :
-
Bearing capacity
- N det :
-
Deterministic bearing capacity
- N ran :
-
Random bearing capacity
- Q :
-
Ultimate vertical force
- q u :
-
Ultimate vertical pressure
- \(\sigma\) ci :
-
Uniaxial compressive strength of intact rock
- GSI :
-
Geological strength index
- m i :
-
Hoek and Brown yield parameter
- D :
-
Disturbance factor
- \(\mu\) :
-
Mean value
- \(\sigma\) :
-
Standard deviation
- COV :
-
Coefficient of variation
- \(\mathit\Theta\) :
-
Spatial correlation length
- r s :
-
Subsample ratio
- \(\Omega ({f}_{k})\) :
-
Regularization term
- \({\widehat{y}}_{k}\) :
-
XGBoost model's prediction
- \(\nu\) :
-
Learning rate
- \({Obj}_{(k)}\) :
-
Loss function
- λ :
-
Regularization factor
- R2 :
-
Coefficient of determination
- RMSE:
-
Root mean squared error
- SHAP:
-
SHapley Additive exPlanations
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Funding
This research budget was allocated by the National Science, Research and Innovation Fund (NSRF) and King Mongkut’s University of Technology North Bangkok (Project no. KMUTNB-FF-67-A-05).
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Thanachon Promwichai: Software, Validation, Data curation, Investigation, Writing–original draft.
Duy Tan Tran: Conceptualization, Investigation, Methodology, Writing–original draft.
Thanh Son Nguyen: Software, Validation, Data curation, Investigation, Writing–original draft.
Suraparb Keawsawasvong: Formal analysis, Investigation, Software, Methodology, Writing–original draft.
Pitthaya Jamsawang: Writing—review and; editing, Supervision, Resource, Project administration.
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Communicated by: Hassan Babaie.
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Promwichai, T., Tran, D.T., Nguyen, T.S. et al. Probabilistic analysis of the bearing capacity of spatially random Hoek‒Brown rock masses by integrating finite element limit analysis, random field theory, and XGBoost models. Earth Sci Inform 18, 33 (2025). https://doi.org/10.1007/s12145-024-01634-7
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DOI: https://doi.org/10.1007/s12145-024-01634-7