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    Developing advanced datadriven framework to predict the bearing capacity of piles on rock
    (2025-12-01)
    Onyelowe, Kennedy C.
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    Hanandeh, Shadi
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    Kamchoom, Viroon
    ;
    Ebid, Ahmed M.
    ;
    Reyes Silva, Fabián Danilo
    Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. A comprehensive dataset comprising key influencing factors such as pile dimensions, geological characteristics, and penetration resistance was utilized to train and validate various models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Trees. The Taylor diagram and statistical evaluations demonstrated the superiority of the proposed models in capturing complex nonlinear relationships, with high correlation coefficients and low root mean square errors indicating robust predictive capabilities. Sensitivity analyses using Hoffman and Gardener’s approach and SHAP values identified the most influential parameters, revealing that penetration resistance, pile embedment depth, and geological conditions significantly impact pile capacity. The findings underscore the effectiveness of machine learning in geotechnical engineering applications, offering a reliable and efficient alternative to traditional empirical and analytical methods. The developed framework provides engineers and practitioners with a powerful tool for improving pile design accuracy, reducing uncertainties, and optimizing construction practices. Future research should focus on expanding the dataset with diverse geological conditions and exploring hybrid modeling techniques to enhance prediction accuracy further.
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    Integrating Multiple Linear Regression Analysis and Machine Learning Models to Predict the Bearing Capacity of Strip Footings on Sandy Clay Slopes
    (2025-02-01)
    Mase, Lindung Zalbuin
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    Misliniyati, Rena
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    Muharama, Nia Afriantialina
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    Supriani, Fepy
    ;
    Ahmad, Debby Ariansyah
    This paper presents Multiple Linear and Machine Learning models of bearing capacity for strip footings at sandy clay slopes subjected to vertical loads. Several parameters are considered in the analysis, including footing width, embedment depth, unit weight, slope angle, internal friction angle, and soil cohesion. A finite element analysis is conducted to assess the impact of these factors. Additionally, an empirical prediction for bearing capacity is proposed. Machine learning techniques utilising various models are employed to analyse performance outcomes, with the Shapley Additive Explanations (SHAP) method used to quantify the contribution of each parameter. The results show that the empirical formulation for predicting ultimate bearing capacity can be effectively applied in engineering practice. Significantly, the findings indicate that the XGBoost model yields the most precise predictions of bearing capacity. The primary parameters influencing bearing capacity include embedded depth, width, unit weight, and internal friction angle, whereas vertical load and unit weight have a minimal impact.
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    Comparative Study of Evaluations of Bearing Capacity Using Conventional Method and Finite Element Method
    (2024-01-01)
    Manandhar, Suman
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    Karmacharya, Sunny
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    Vootripruex, Panich
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    Chaiyaput, Salisa
    Kathmandu Clay, also called the black clay, found near the sub-surface of the valley sediments, incorporating with its low strength and high compressibility. In this research, the vertical bearing capacity of strip footing on Kathmandu Clay was analyzed using Mohr–Coulomb soil model both in drained and undrained conditions through finite elements. The analyses were focused on the failure patterns of the footing for both drained and undrained conditions. The failure analyses showed a varying degree of effects of the friction angle of the soil both in drained and undrained conditions. The results were further compared with Vesic’s and Prandtl’s methods of evaluating bearing capacities. Hence, obtained results of simulated failure plane and the conventional one preceded by Prandtl show a good agreement between each other and validated the results.