A data-driven model for predicting shear strength indexes of normally consolidated soils

The accurate determination of shear strength indexes is crucial for engineering soils. Currently, the strength indexes determined by the test method depend too much on engineering experience, which leads to the uncertainty of the final value. The ensemble learning is a subclass of machine learning that exhibits strong performance when dealing with complex data and tasks. To enhance the precision of the shear strength indexes, based on the normally consolidated soils, a model is established using the diverse ensemble learning algorithms for predicting the shear strength indexes of soils. Various models are assessed for their generalization capability using the root mean square error (RMSE), coefficient of determination (R²) and absolute value error (MAE). The Adaboost algorithm is employed for the sensitivity analysis of input parameters. The findings indicate that the Adaboost algorithm yields the best generalization for the shear strength the RF for the internal friction angle, and the Adaboost algorithm for the cohesion, achieving respective test-set R² values of 0.925, 0.965 and 0.942. The sensitivity analyses reveal that the dry density, moisture content and normal stress exert the most significant influence on the shear strength, while the key factors for the internal friction angle are the coefficient of curvature, viscous grain content and water content. The water content, dry density and liquid limit are identified as the primary influencers on the cohesion. The data-driven model established herein offers guidance for selecting the shear strength indexes in engineering and investigating strength parameters of soils through the machine learning methods.