Enhancing stability and interpretability in the study of strength behavior for coarse-grained soils

Abstract

Coarse-grained soils have been commonly employed as filling materials in engineering projects, and comprehending the mechanical properties of these soils is crucial for ensuring project safety. Currently, artificial intelligence (AI) plays a significant role in advancing these fields. However, the inherent lack of stability and interpretability of AI models constrains their potential for providing substantial assistance. To solve these shortcomings, this study investigates the mechanical properties of coarse-grained soils and proposes an approach that integrates a stable learning module with neural networks to enhance the stability of an AI model. The proposed method addresses the challenge of relaxing the strong assumption of independent and identically distributed (i.i.d.) data. In addition, it improves the stability of the prediction of a relationship between the deviatoric stress and axial strain. The proposed method is verified by experiments. The experimental results confirm that the proposed method performs well on real-world data samples that deviate from the i.i.d. assumption. Moreover, the proposed method enhances the interpretability of the AI model and facilitates the identification of crucial features for assessing the strength behavior of coarse-grained soils. This feature of interpretability is invaluable for practical engineering applications.