ArticleScientific reports2025
A comparative machine and deep learning approach for predicting ultimate bearing capacity of shallow foundations in cohesionless soil.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Data-driven prediction of micro-piled raft load-settlement using machine learning and Monte Carlo simulation.Scientific reports · 2026Article
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Abstract
This study developed a Python-based framework to predict the ultimate bearing capacity of shallow foundations on cohesionless soil, employing machine learning (ML) and deep learning (DL) techniques. Utilizing a comprehensive dataset of 116 footing experiments, Eleven ML models (Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF), Categorical Boosting (CatBoost) etc.) and five DL models (Artificial Neural Network (ANN), Deep Neural Network (DNN), etc.) trained and compared against traditional methods. Input parameters included foundation dimensions and soil properties. Results demonstrated that ML and DL models significantly outperformed traditional equations, achieving higher accuracy. Ensemble methods like GPR, XGBoost, GBM, RF, and CatBoost exhibited superior performance, with a Coefficient of Determination (R
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