Structural performance assessment of RC flexural members through crack-based fibre beam-column model updating

Abstract

For reinforced concrete (RC) structures in service, original finite element models (FEMs) based on design information usually deviate from actual structures due to structural damage such as cracks, displacement and material degradation. Although conventional FEMs can be updated to give results that match the response data used during the updating process, they can hardly predict the subsequent structural performance. In this study, an innovative fibre beam-column model updating method based on static deflection and crack width is proposed. First, the self-developed fibre beam-column finite element program COMPONA-MARC is modified to efficiently simulate the nonlinear cracking behaviour of RC flexural members. Second, sensitivity studies using the discrete central difference method based on the Lagrange interpolation function are conducted to select updating parameters. Third, with the combination of modified COMPONA-MARC and an automation program written in Python, plenty of sample data are obtained, and artificial neural networks (ANNs) are trained by Bayesian Regularization Backpropagation (BRB). Finally, the updated parameters are acquired by inputting the measured static deflections and crack widths. Typical RC beams and RC slabs from the literature are utilized to validate the proposed methodology and explore potential implementation scenarios. The results show that updated FEMs can satisfactorily predict mechanical behaviour and assess structural performance. The proposed method offers enlightening insights about combining structural health monitoring and inspection (SHMI) results with model updating, which can promote the applications of digital twins in performance assessments of RC structures.