An innovative continuous Bayesian model updating method for base-isolated RC buildings using vibration monitoring data

•An innovative continuous Bayesian model updating method specific for base isolated buildings is proposed.•The Bayesian functions related to the isolation system are periodically updated to account for monitoring data.•The effectiveness of a subdivision of the monitoring period into reduced time intervals is pointed out.•A surrogate model is constructed and calibrated using the FEM in order to reduce the computational cost.•A damage scenario is simulated in order to validate the procedure. Base isolation is the most widespread passive control strategy for seismic protection of low- and medium-rise Reinforced Concrete (RC) buildings. The excellent performance of base-isolated structures just after construction is well recognized but their seismic behavior after a few operation years can be significantly modified, due to isolators’ aging. In this perspective, the availability of long-term vibration data allows to eventually assess the evolution of degradation and plan maintenance or replacement actions. This paper presents an innovative continuous Bayesian model updating method specific for base isolated buildings. In the proposed method, the Bayesian functions of the isolation system are periodically updated to account for daily measured data. The results of a comprehensive study on a base isolated three-stories RC school building, located in Bojano (Italy), are presented. The investigation comprises Ambient Vibration Testing (AVT), modal identification, installation of a continuous monitoring system and calibration of a Finite Element Model (FEM) of the isolated structure. In order to reduce the computational cost of the model updating procedure, a surrogate model is constructed and calibrated using the complete FEM of the structure through the Response Surface Methodology (RSM). The advantage of having long-term monitoring data on the results of the Bayesian model updating for base isolated structures is highlighted.