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

Physics-informed neural networks for predicting ultimate bearing capacity of layered soils under complex loading conditions

Machine Learning Framework for Reliability-Based Design of Screw Piles

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 soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and K -stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient ( R = 0.918 ), and the highest reference index ( RI = 0.951 ). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.

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

Sensitivity-Driven Evolutionary Polynomial Regression for Tropical Subgrade Permanent Deformation

This study developed an interpretable Evolutionary Polynomial Regression framework to predict permanent deformation of tropical soil subgrades in semi-rigid pavement structures. The database combined repeated-load triaxial test parameters reported in Brazilian studies with controlled mechanistic-empirical simulations representing traffic demand, structural thickness, subgrade Poisson’s ratio, and soil properties. Candidate equations were generated through a hybrid evolutionary search combining Genetic Algorithm and Differential Evolution, and final models were selected by jointly considering statistical performance, parsimony, and Monte Carlo sensitivity consistency. Six predictors were retained: number of axle-load repetitions, percentage passing the No. 200 sieve, optimum moisture content, clayeyness coefficient, laterization index, and equivalent pavement thickness. Model assessment used three repeated random train-test partitions. The selected equations contained three polynomial terms and achieved testing coefficient-of-determination values from 0.943 to 0.951, root mean square errors from 0.214 to 0.238 mm, and mean absolute errors from 0.163 to 0.169 mm. Sensitivity and Shapley analyses showed physically consistent trends, including increased deformation with traffic loading and laterization index, and reduced deformation with equivalent pavement thickness.

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