Characterizing fatigue damage behaviors of concrete beam specimens in varying amplitude load
Source abstract
Abstract
In this paper, the random and nonlinear mechanical characteristics of the composition distribution of concrete in each phase are investigated to solve the difficulty of fatigue damage test of concrete beam specimens under variable amplitude loading. A three-dimensional (3D) concrete composite random aggregate meso-model of concrete beam specimens is built with Digimat as a composite modeling tool. Subsequently, the bending-tensile strength and fatigue life of the model under four-point bending loading is analyzed and calculated using Abaqus finite element analysis model based on the concrete elastic-plastic damage criterion. The mechanical behaviors of concrete materials under static load failure, multistage fatigue loading, and random fatigue loading are studied. Finally, compared with the test results, the following conclusions are obtained. The random aggregate model and elastoplastic damage mechanics model of concrete composite constructed by concrete in Abaqus and Digimat can better describe the geometric and distribution characteristics of composite structure and aggregate in concrete beams and the mechanical behavior of fatigue and static load failure. The difference is 4.08% with the experimental results, which verifies the validity and reliability of the numerical simulation model, and the fatigue life of concrete beams under random loading Sra and Srb is 1622 and 164 times, respectively. The research results have certain guiding significance and reference value for accurate calculation of bending and tensile strength of concrete beams under specified raw material and mix ratio and prediction of fatigue damage cracking in airport cement concrete pavement structure.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Structural Response to Dynamic Loads · Rock Mechanics and Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (54)
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