Probabilistic Identification of Simulated Damage on the Dowling Hall Footbridge through Bayesian FE Model Updating

This paper presents a probabilistic damage identification study on a full-scale structure, the Dowling Hall Footbridge, through a Bayesian finite element (FE) model updating. The footbridge is located at Tufts University and is equipped with a continuous monitoring system that measures its ambient acceleration response. A set of data is recorded once every hour or when triggered by large vibrations. The modal parameters of the footbridge are extracted from each set of data and are used for FE model updating. In this study, effects of physical damage are simulated by loading a small segment of the footbridge deck with concrete blocks. The footbridge deck is divided into five segments in a FE model of the test structure and the added mass on each segment is considered as an updating parameter. Overall, 72 sets of data are collected during the loading period and different subsets of these data are used to find the location and extent of the damage (added mass). The Adaptive Metropolis-Hastings algorithm with adaption on the proposal probability density function is successfully used to generate Markov Chains for sampling the posterior probability distributions of the five updating parameters. Effects of the number of data sets used in the identification process are investigated on the posterior probability distributions of the updating parameters. The probabilistic model updating framework accurately predicts the simulated damage and the level of confidence on the obtained results. The probabilistic damage identification results are found to be in good agreement with their corresponding deterministic counterparts.