Jul 2026· Journal of engineering and applied sciences· Vol 73· 0 citations· 34 references
Abstract
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.
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.
Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou· Journal of computing in civi...· 0 citations
This work presents a systematic multi-model comparison within a unified hyperparameter optimization framework and highlights that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.
Remzi Gürfidan, K. Erten· Bitlis Eren Üniversitesi Fen...· 0 citations
The current study combines numerical modelling and machine learning to identify the stability of applications in nail reinforced slope study. PLAXIS LE was used to develop different slopes having different soil properties including various values for cohesion (5, 10, 15 kPa), angle of internal friction (20°, 25°, 30°), unit weight (17, 18, 19 N/m³), and slope angle (30°, 35°, 40°, 45°, 50°, 60°, 70°). Safety Factors (FOS) prediction models such as Random Forest (RF), Linear Regression (LR), and K-Nearest Neighbors (KNN) have been developed using the parameters included in the study. The Random Forest model has shown a superior performance among the other models with the lowest Mean Absolute Error (MAE: 0.053) and Mean Squared Error (MSE: 0.006), taking into consideration the highest value of R² (0.957) and Adjusted R² (0.951) to indicate a better predictive accuracy. With R² values of 0.903 and 0.920, respectively, Linear Regression and KNN also showed considerable strength of results. The results mentioned above show the bright future of machine learning models with Random Forest in predicting slope stability and contribute to refining nail reinforcement strategies. It shall also provide an input for developing cost-effective and robust slope rehabilitation measures in a geotechnically unfriendly environment.
A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.
Qaim Shah, Waheed Ali Khoso, Fawad Iqbal et al.· Discover Artificial Intellig...· 0 citations
This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index and Undrained Shear Strength and highlights the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.
Giovanni Spagnoli, Mohammadreza Mahmoudi, S. Shimobe et al.· E3S Web of Conferences· 0 citations
ABSTRACT This study establishes four popular data-driven techniques – Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) for predicting the flexural strength (FS) of high-performance steel fiber-reinforced cementitious composites (FRCCs). A database containing 156 experimental records was used to train and test predictive models with nine feasible input variables. The results demonstrated that the GB model was the best predictor for estimating the FS of FRCCs. The GB model maintained satisfactory predictive accuracy after 10-fold cross-validation, achieving an average R2 of 0.834 on the validation folds generated from the training dataset. The predictive capability of GB model remained stable at 600 Monte Carlo simulations. The Shapley Additive Explanations (SHAP) method and partial dependence plots (PDP) indicated that fiber volume content was the most influential factor affecting FS predictions. To validate the accuracy of the GB model, a single-point case study was conducted on three specimens subjected to a three-point bending load. The discrepancy between the experimental values and GB predictions corresponded to an absolute prediction error of 1.21%, highlighting the accuracy of the developed model. Finally, a cloud-based web application was developed to provide a convenient tool for practical FS prediction of FRCCs.
Duy-Liem Nguyen, Tan-Duy Phan· Journal of Structural Integr...· 0 citations