Computational forensics framework for material property identification of reinforced structures leveraging DIC-based FEM updating via metaheuristic optimization

Abstract

Identification of the material properties of reinforced concrete (RC) systems is a challenging but crucial part of the strength analysis and condition assessment. However, the heterogeneous nature of concrete poses complexities in determining its material properties accurately. A combination of finite element model updating with metaheuristic optimization algorithms has emerged as one method to determine the material properties of structures. However, the previous works are limited to homogenous materials such as steel and use simple constitutive laws for the finite element model. Thus, the authors proposed a Computational Forensics (CF) framework to investigate the material property identification of RC beams using full-field surface deformation data obtained from the Digital Image Correlation technique. The proposed framework successfully determined the elastic modulus and compressive strength with high accuracy. Both synthetic and real experiments were performed to show the applicability and practicality of the framework for material property identification and SHM purposes.