Journal
STRUCTURAL CONTROL & HEALTH MONITORING
Volume 22, Issue 3, Pages 463-483Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/stc.1684
Keywords
Bayesian FE model updating; adaptive Metropolis-Hastings algorithm; damage identification; uncertainty analysis; Dowling Hall footbridge
Funding
- National Science Foundation [1125624]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1125624] Funding Source: National Science Foundation
Ask authors/readers for more resources
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 an 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 maximum a-posteriori estimates of damage in the probabilistic approach are found to be in good agreement with their corresponding deterministic counterparts. Copyright (c) 2014 John Wiley & Sons, Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available