Parametric identification hybrid metaheuristic-based approach for steel fiber-reinforced concrete using finite element modeling: a comparative study

Abstract

Determining constitutive parameters is essential in nonlinear finite element analysis for structural design. Steel fiber-reinforced concrete (SFRC) is widely used due to its residual strength after cracking. While analytical closed-form solutions are common, inverse analysis using standardized tests provides greater accuracy in capturing the post-peak behavior. This study proposes a hybrid strategy to automate inverse analysis for deriving the tensile stress-strain response of SFRC using four-point bending tests. Hybrid metaheuristic methods enhance search capabilities by combining the strengths of different optimization techniques, often leading to more robust and efficient solutions than traditional methods. By integrating a global search metaheuristic with a local search refiner, for instance, a hybrid approach can effectively explore a vast solution space and then precisely pinpoint the optimal solution within a promising region. Three metaheuristic algorithms were integrated with nonlinear finite element models: Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE). An experimental program with two SFRC mixtures (0.5% and 1.5% steel fiber) tested under flexural and direct tensile loading validated the method. Among the algorithms, the hybrid GA approach achieved the lowest mean square error (MSE). For SFRC with 1.5% fibers (flexural-hardening behavior), the predicted tensile strength aligned well with experimental results. For the 0.5% mixture (flexural-softening behavior), predictions underestimated tensile strength by 27–39% due to crack localization. The proposed hybrid approach improved accuracy in the force-deflection response compared to standalone metaheuristic methods. Overall, the strategy effectively identifies SFRC tensile strength and enhances modeling precision in structural applications.