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

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...

Machine Learning Framework for Reliability-Based Design of Screw Piles

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’...

Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches

This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soi...

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

Integrating Finite Element Analysis and Machine Learning to Predict the Bearing Capacity of Strip Footings on Slopes

The results confirm the accuracy, interpretability, and computational efficiency of the integrated FEA-ML approach as an alternative to traditional bearing capacity analysis.