Machine Learning Framework for Reliability-Based Design of Screw Piles

Machine Learning–Aided Prediction of Shaft Resistance and Bearing Capacity Factors for Pile Foundation Design Using CPT Data

This study presents a data-driven framework for predicting the shaft resistance ( β ) and end bearing capacity ( N t ) factors of piles using cone penetration test (CPT) data. Traditional design methods often rely on empirical values that oversimplify soil–pile interaction by neglecting the influen...

Probabilistic and Interpretable Machine Learning Framework for Predicting Pile Unit Base Resistance in Soft Soil

Accurate prediction of pile base resistance is essential for the safe and economical design of deep foundations, particularly in soft soils where load-transfer mechanisms are highly nonlinear and uncertain. This study develops a comparative, probabilistic, and interpretable machine learning framework for predicting pil...

Explainable machine learning surrogates for lateral pile response prediction in soft clay

Bayesian-Optimized Ensemble Machine Learning for Predicting Settlement of Cohesionless Soil Under Loads

Predicting Buckling Load of Slender Hollow Rods Using Machine Learning: Model Comparison and Input Sensitivity Analysis

Rod length and diameter were consistently identified as the dominant parameters governing buckling resistance, jointly accounting for the majority of predictive importance; when length was held constant, diameter alone emerged as the leading parameter, followed by comparable contributions from wall thickness and Young’...

Machine Learning Prediction of the Ground Reaction Curve in Sand with the MATLAB GUI Platform

The proposed framework combined a curated database, neural network-based curve prediction, and hyperparameter optimization, providing a robust approach for evaluating the soil arching effect, providing a robust approach for evaluating the soil arching effect.