Stability prediction of footings on slopes with dense sand using Bolton model, FELA, XGBoost, Random Forest, and Evolutionary Polynomial Regression

Information from the abstract

This study investigates the bearing-capacity factor ( Nγ ) of rigid strip footings placed on dense sand slopes by integrating finite element limit analysis (FELA) with machine learning and a symbolic regression approach. Two-dimensional FELA was performed under plane-strain conditions using Bolton model, and the numerical results were compared with established results from previous studies to verify the consistency of the predicted trends. Six input parameters were examined: footing width ( B ), particle crushing strength ( Q ), relative density ( D R ), critical-state friction angle ( ϕ cv ), slope angle ( β ), and slope-height ratio ( H/B ). The FELA-generated database was used to develop three predictive models: XGBoost, Random Forest (RF), and Evolutionary Polynomial Regression with Multi-Objective Genetic Algorithm (EPR Moga-XL). The EPR Moga-XL model provided explicit and interpretable equations for preliminary design, RF served as an ensemble-based benchmark, and XGBoost achieved the highest predictive accuracy, with testing R 2 values of 0.994, 0.943, and 0.953 for β = 15°, 30°, and 45°, respectively. The parametric and SHAP analyses showed that Q , D R , B , and ϕ cv , are the most influential factors controlling Nγ , while the influence of H/B is strongly dependent on slope angle and becomes more pronounced under steeper slope conditions. Larger B reduces Nγ through stress-level-dependent suppression of dilatancy, while steeper slopes restrict passive-zone development and promote localized failure along the slope face. A supplementary FELA assessment showed that the footing-to-slope crest distance ratio ( L/B ) strongly affects Nγ near the slope crest, with the response approaching level-ground behavior when L/B = 6. Overall, the proposed FELA–ML framework provides a physics-informed and computationally efficient tool for predicting the bearing capacity of rigid strip footings on dense sand slopes within the adopted applicability boundaries.

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Related topics: Geotechnical Engineering and Analysis · Landslides and related hazards · Hydrological Forecasting Using AI