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

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

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 influence of pile geometry. To address this, this research employs an evolutionary polynomial regression with a multiobjective genetic algorithm (EPR-MOGA) to develop design equations. A two-step methodology was used: first, robust models for shaft capacity ( Q s ) and end-bearing capacity ( Q t ) of drilled piles were developed from databases of 54 and 31 load tests, respectively. Validated using fivefold cross-validation, these base models demonstrated high predictive accuracy, achieving an average coefficient of determination of 0.92 for Q s and 0.97 for Q t . Explicit equations for the β and N t factors were then derived from these validated models. These equations inherently account for complex soil–pile interactions, showing increases with soil friction angle and reductions with pile slenderness ratio ( L / D ) at a diminishing rate. This eliminates the need for the additional caps on effective vertical stress required by conventional methods. Finally, the models were extended to driven piles by calibrating modification factors on an independent 36-test database, yielding values of 1.46 for Q s and 2.38 for Q t .

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

Bayesian-optimised machine learning for predicting aggressive environment resistance and service life of recycled aggregate geopolymer concrete

Developing reliable computational tools for durability and service-life assessment of concrete structures in aggressive environments is essential for advancing predictive modeling in structural engineering. This study introduces machine learning (ML)–based models for forecasting the sulfate and acid resistance of recycled aggregate geopolymer concrete (RGPC), produced with untreated and surface-treated recycled concrete aggregates (RCAs) through two mixing approaches. Three algorithms, i.e. Gaussian process regression (GPR), LSBoost ensemble, and Neural Network, were trained using nine input parameters related to material composition and exposure conditions, with durability indicators, namely mass loss rate (Kw) and compressive strength retention index (Kf), as outputs. A dataset of 336 experimentally tested RGPC specimens was used, applying Bayesian Optimisation for hyperparameter tuning and 5-fold cross-validation for generalisation. Among the models, the optimized GPR achieved the highest accuracy, confirmed by the lowest objective value. Feature importance analysis highlighted environmental cations, sulfate concentration, RCA replacement level, initial compressive strength, and exposure duration as the most influential factors governing degradation. The proposed Bayesian-optimized ML framework demonstrates a robust and generalizable method for predicting durability and service life of sustainable concretes, providing a valuable tool for simulation-driven design and durability-based performance assessment in mechanics and structural engineering.

Advancing safety assessment of granular pile supported embankments: a comparative study of state-of-the-art machine learning predictive models