Probabilistic Damage Identification of the Dowling Hall Footbridge Using Bayesian FE Model Updating

Abstract

AbstractThis paper presents a probabilistic damage identification study on a full-scale structure, the Dowling Hall Footbridge, through Bayesian finite element (FE) model updating. The footbridge is located at Tufts University campus and is equipped with a continuous monitoring system that measures the ambient acceleration response of the bridge. A set of data is recorded once an hour or when triggered by large vibrations. The modal parameters of the footbridge are extracted based on each set of measured ambient vibration data and are used for model updating. In this study, effects of physical damage are simulated by loading a small segment of footbridge’s deck with concrete blocks. The footbridge deck is divided into five segments and the added mass on each segment is considered as an updating parameter. Overall, 72 sets of data are collected during the loading period (i.e., damaged state of the bridge) and different subsets of these data are used to find the location and extent of the damage (added mass). Adaptive Metropolis Hasting 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. Effect of the number of data sets used in the identification process is investigated on the posterior probability distributions of the updating parameters.KeywordsBayesian FE model updatingAdaptive metropolises hasting algorithmDamage identificationUncertainty analysisDowling hall footbridge