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

Tree Based Machine Learning Framework for Stability Evaluation of Square Tunnels in Sloping Rock

The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.

Pseudo-static bearing capacity of skirted footings on sandy slopes using artificial neural network and random forest regression models

This study investigates the pseudo-static bearing capacity of skirted strip footings on cohesionless slopes using finite-element limit analysis and data-driven prediction models. A total of 216 numerical simulations were performed by varying soil strength, slope angle, seismic coefficient, footing location and skirt de...

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...

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

Machine Learning–Based Predictive Modeling of Time-Dependent Deformation in Braced Excavations in Soft Alluvial Clay

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