Prediction Error Variances in Bayesian Model Updating Employing Data Sensitivity
Publication: Journal of Engineering Mechanics
Volume 142, Issue 12
Abstract
Efficiency of a Bayesian model updating algorithm is greatly affected by the choice of variance of prediction error models of different data points (evidence) used for model updating. In the context of structural model updating, a sensitivity-based novel approach is proposed in this work to find these variances without increasing the dimensionality of the model updating problem. Well-established relations of modal data sensitivity toward structural parameters are incorporated in the Bayesian framework to evaluate the prediction error variances. A high-rise shear building is considered for numerical illustration of the approach. Markov chain Monte Carlo (MCMC) simulation technique is employed using the Metropolis-Hastings algorithm to simulate the samples from the posterior distribution. Results are presented as a comparison of unknown parameters obtained using the proposed approach and an approach in which all prediction error variances are assumed to be equal. The study shows that the proposed approach is highly efficient in extracting appropriate information from the data, and therefore enhancing the efficiency of Bayesian algorithm. It also illustrates that the damage locations play an important role in the selection of variances of prediction error models. Furthermore, each data point of evidence can be very effective in estimating the model parameters, if the information contained in the data is exploited effectively.
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Information & Authors
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Published In
Journal of Engineering Mechanics
Volume 142 • Issue 12 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 25, 2015
Accepted: Jun 28, 2016
Published online: Aug 16, 2016
Published in print: Dec 1, 2016
Discussion open until: Jan 16, 2017
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