The modelling and simulation of cyclic sand ratcheting is tackled by means of a plasticity model formulated within the well-known critical state, bounding surface SANISAND framework. For this purpose, a third locus – termed the ‘memory surface’ – is cast into the constitutive formulation, so as to phenomenologically capture micro-mechanical, fabric-related processes directly relevant to the cyclic response. The predictive capability of the model under numerous loading cycles (‘high-cyclic’ loading) is explored with focus on drained loading conditions, and validated against experimental test results from the literature – including triaxial, simple shear and cyclic loading by oedometer test. The model proves capable of reproducing the transition from ratcheting to shakedown response, in combination with a single set of soil parameters for different initial, boundary and loading conditions. This work contributes to the analysis of soil–structure interaction under high-cyclic loading events, such as those induced by environmental and/or traffic loads.

This work presents a critical state plasticity model for predicting the response of sands to cyclic loading. The well-known bounding surface SANISAND framework by Dafalias and Manzari is enhanced with a memory surface to capture micromechanical, fabric-related processes directly affecting cyclic sand behavior. The resulting model, SANISAND-MS, was recently proposed by Liu et al. and successfully applied to the simulation of drained sand ratcheting under thousands of loading cycles. Herein, novel ingredients are embedded into Liu et al.’s formulation to better capture the effects of fabric evolution history on sand stiffness and dilatancy. The new features enable remarkable accuracy in simulating undrained pore pressure buildup and cyclic mobility behavior in medium-dense to dense sand. The performance of the upgraded SANISAND-MS is validated against experimental test results from the literature—including undrained cyclic triaxial tests at varying cyclic loading conditions and precyclic consolidation histories. The proposed modeling platform will positively impact the study of relevant cyclic and dynamic problems, for instance, in the fields of earthquake and offshore geotechnics.