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.
Yu Xin (Wed,) studied this question.