ArticleScientific reports2025
Advanced hybrid machine learning models with explainable AI for predicting residual friction angle in clay soils.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurately estimating the residual strength friction angle of clay soils is vital for the design and stability evaluation of geotechnical structures such as slopes, retaining walls, and foundations, especially in regions susceptible to landslides and ground instability. Traditional methods for determining the residual strength friction angle are often labor-intensive, time-consuming, and costly. This study explores three advanced hybrid machine learning models: Gradient Boosting Neural Network (GrowNet), Reinforcement Learning Gradient Boosting Machine (RL-GBM), and a Stacking Ensemble to predict the residual friction angle of clay soils, addressing a critical gap in current predictive methodologies. The research utilized a carefully harmonized dataset of 400 samples from global studies, considering various soil parameters (liquid limit, plasticity index, change in plasticity index, and clay fraction) as inputs for the prediction models. Various evaluation metrics, including coefficient of determination (R
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