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

Machine learning-driven modeling of soil plasticity and strength parameters with interpretability insights

This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index ( LI ) and Undrained Shear Strength ( Su ). Over 1,550 LI and 950 Su measurements from global research were employed. Individual regressors—Multilayer Perceptron (MLP), Random Forest (RF), and AdaBoost—were evaluated alongside ensemble strategies, including Simple Averaging, Weighted Averaging, and stacking using a Support Vector Regression (SVR) meta-learner. Hyperparameter tuning was performed using both Bayesian Optimization (BO) and Particle Swarm Optimization (PSO). AdaBoost achieved the best results for LI prediction, while RF yielded the highest accuracy for Su . Weighted Averaging ensemble methods produced outstanding predictive performances, achieving R 2 values of 0.9913 (BO) and 0.9961 (PSO) for Su , and 0.9624 (BO) and 0.9338 (PSO) for LI. BO proved superior for LI models, while PSO excelled for Su models. The results highlight the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.

Application of an artificial neural network model for predicting unconfined compressive strength of expansive soils

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

Refined analytical frameworks for enhancing friction angle prediction in fiber-reinforced soil through advanced computational methodologies

Traditional analytical and empirical techniques often fail to accurately forecast the friction angle of fiber-reinforced soil (FRS) due to the complex, non-linear dynamics of soil-fiber interactions. To address these limitations, this study employs machine learning (ML) to enhance predictive accuracy. Bagging Regression (BR) and Lasso Regression (LR) are selected for their ability to handle diverse datasets and reduce model complexity, respectively. These models are hybridized with bio-inspired optimizers, specifically Attack Leave Optimizer (ALO) and Chaos Game Optimization (CGO), to develop four frameworks: BRAL (BR + ALO), BRCG (BR + CGO), LRAL (LR + ALO), and LRCG (LR + CGO). The objective is to optimize model parameters for superior precision in estimating the friction angle. Performance is evaluated using R², RMSE, and MAE metrics in training, validation, and testing phases. Results demonstrate that the LRAL model exhibits the highest predictive capability, achieving an R² of 0.995 and the lowest RMSE of 0.634 in the testing phase, significantly outperforming the standalone Bagging model. The developed hybrid models provide a robust tool for FRS shear strength prediction, facilitating more efficient geotechnical design.

Physics-guided ensemble machine learning framework for slope failure prediction

Purpose. This research aims to provide a physics-based ensemble machine learning framework that can reliably predict slope stability and distinguish stable from unstable slopes in static and seismic conditions. Standard analytical and numerical methods have significant processing costs and oversimplified assumptions that limit their usefulness. Methods. The study analyzed 700 slope stability samples, including geotechnical and seismic factors such as slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The proposed model now includes physics-based engineering elements, such as tan ϕ, c/H, and PGA/g, to account for geotechnical interactions. A linear meta-learner and Random Forest and Gradient Boosting regressors were used to develop a stacked ensemble framework. We assessed model strength and reliability. Findings. The devised framework showcased exceptional prediction performance with an (R2) of 0.982, a mean absolute error of 0.02 and a root mean square error of nearly 0.03. Cross-validation showed consistent generalization. Random Forests classified slope stability conditions with 97.4% accuracy. The most important parameters for slope stability predictions were the internal friction angle and cohesiveness. Furthermore, the inclusion of carefully crafted physics-based features demonstrably improved robustness, accuracy and consistency. Originality. This work introduces a pioneering, combined framework that fuses the mechanics of geotechnical soil with ensemble machine learning techniques, enhancing both interpretability and the certainty of slope stability predictions. Practical implications. The framework may support rapid, cost-effective, and interpretable preliminary geotechnical risk assessment and design screening. However, its use in slope monitoring or early-warning applications requires further field validation and integration with monitoring data.

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.