Predicting peak deviatoric stress of gravels with a novel segmented feature transformation approach.

The assessment of peak deviatoric stress (q peak) in gravels is crucial for engineering stability and safety. Due to the various influencing factors and the limited available samples, present empirical methods and conventional machine learning algorithms have limitations in generalization capability and achieving high performance in this complex relationship. This paper proposes a novel deep learning-based model termed Seft-Net. The model utilizes a segmented feature transformation (Seft) approach to improve convergence and robustness by leveraging prior knowledge that grouping gravel properties into three categories: particle (e.g., shape, hardness), soil mass (e.g., gradation, void ratio), and external factors (e.g., confining pressure). Subsequently, it applies a feature transformation module using depthwise separable convolutions to learn and fuse hierarchical representations across multiple layers. This enables Seft-Net to automatically extract high-level features from gravel properties that capture intricate nonlinear relationships with q peak. The model is trained and validated using a large-scale practical dataset from triaxial test results. Ablation studies confirmed the superiority of the proposed segmented feature transformation approach over the unsegmented approach, and the feature transformation module demonstrated optimal feature extraction capabilities. Comparative evaluations with three machine learning methods further validated the superior performance of Seft-Net. [ABSTRACT FROM AUTHOR]