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A micromechanics-based computational framework for predicting the elastic modulus and compressive strength of normal and high-performance concrete
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DOI：10.1016/j.compstruc.2025.108055.png)
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Abstract
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This study presents a computational framework that integrates micromechanics, a phase-field damage model, and genetic programming-based symbolic regression to predict the elastic modulus and compressive strength of both normal and high-strength concrete. The micromechanical formulations are developed using an extended generalized self-consistent scheme, incorporating a quasi-elasto-plastic brittle behavior governed by a von Mises yield criterion. A simplified phase-field damage model is proposed, introducing a compressive phase-field variable along with an analytical approximation that links fracture energy to the macroscopic behavior of concrete. The proposed framework is validated through strong agreement among numerical simulations, experimental observations, and theoretical predictions, supporting the development of a robust theoretical database for elastic modulus, compressive strength, and associated material properties. Based on this dataset, an efficient computational strategy is developed and examined to generate simple and practical symbolic regression expressions within the Genetic Programming-based framework to derive predictive equations for elastic modulus as a function of compressive strength and aggregate characteristics. These equations are validated against established standards and experimental data, confirming their accuracy and practical relevance for structural design applications.
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