Model Updating Using Hierarchical Bayesian Strategy Employing B-WIM Calibration Data

Abstract

Bridge weigh-in-motion (B-WIM) systems are employed for monitoring traffic weights, providing useful information for management decisions. Many applications were proposed based on the information collected, such as calculation of influence lines and damage detection. In this work, an additional application is addressed, to perform model updating of structural parameters from information collected during the calibration of B-WIM systems. The goal of model updating techniques is to adjust the model parameters in order to achieve better agreement between predicted and experimental responses. Therefore, the resulting updated model is able to provide valuable information for decision makers. For many civil engineering applications, the updated parameters may have an inherent variability during the execution of the experimental procedure, since some external effects, such as environmental conditions, may change considerably along the process. To account for this inherent variability properly, a hierarchical Bayesian strategy is adopted. Results for both numerically simulated signals and a real engineering calibration procedure indicate that the proposed hierarchical Bayesian model updating approach is able to perform suitable estimates.