Corrosion-related parameter estimation for RC structures using UKF-based Bayesian nonlinear finite element model updating with seismic data

Abstract

Model updating based on field measurements (e.g., from ambient vibration) has proven to be an efficient approach for assessing the condition of reinforced concrete (RC) structures. Despite existing studies in the literature, no effort has been devoted to corrosion-related parameter estimation for RC structures based on Bayesian nonlinear finite element model updating (FEMU) with seismic data. To bridge this gap, this research work extends the application of nonlinear FEMU to corroded RC structures and provides a methodology to estimate the corrosion-related parameters from seismic measurement of RC structures with high nonlinearity. The essential idea of this approach is to integrate recursive Bayesian inference (i.e., unscented Kalman filter) with advanced finite element (FE) modeling of corroded RC structures to estimate corrosion-affected properties. The advanced FE modeling approach used in this study is an efficient modeling strategy that accounts for different aspects of the corrosion, particularly corroded bond-slip in RC structures. The capability of the approach in evaluating the corrosion-affected model parameters is examined by 13 case studies using an example RC column with simulated seismic data due to lack of real-word measurements. These study cases consider different corrosion levels, measurement noise levels, corroded features, seismic intensity levels, and types of measurement. The successful estimation of the corrosion-affected properties for the corroded column under various scenarios indicates that the proposed methodology can be employed to identify the unknown corroded properties of RC structures using recorded seismic response data. The updated FE model of a corroded RC structure can potentially be utilized for reliable seismic performance assessment, which assists in future decision-making for repair or retrofit of existing structures.