journal article·2023-12Open access

An improved damage-plasticity material model for concrete subjected to dynamic loading

Yu Rong, Huilan Ren, Xiangzhao Xu

Abstract

Appropriate material model will provide more accurate predictions for the mechanical response and damage mode of concrete structures, and thus developing a material model that is more consistent with the dynamic behavior of concrete has an important significance to obtain better numerical results. In this paper, an improved damage-plasticity material model for concrete is presented to predict its mechanical response subjected to dynamic loading. Based on the current extent of damage, the failure strength surface is modified through linearly interpolating between the maximum and yield strength surfaces or the maximum and residual strength surfaces. The shear, compacted and tensile damage are separately defined, and a unified hardening/softening function associated with the shear and compacted damage is introduced as the prediction of strain hardening and softening behaviors under compression. The Lode angle effect is considered for describing the reduction of shear strength on the compressive meridian, and the strain rate effect is considered by the radial enhancement method. The calibration method of material parameters is suggested according to the existing experimental data and empirical equations. The feasibility and accuracy of this improved concrete model are verified by the single element validation, and its performance improvement is discussed by comparing with the popular material models for concrete. The experiments and numerical simulations of split Hopkinson pressure bar (SHPB) are conducted for concrete and cement to further verify the validity and accuracy of this improved concrete model.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: High-Velocity Impact and Material Behavior · Structural Response to Dynamic Loads · Rock Mechanics and Modeling

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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