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Bayesian Model-Updating Implementation in a Five-Story Building

Authors :
Oscar D. Hurtado
Albert R. Ortiz
Daniel Gomez
Rodrigo Astroza
Source :
Buildings, Vol 13, Iss 6, p 1568 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experimental response and the modeled structure by updating its parameters based on the observed data. Structural numerical models are typically constructed using a deterministic approach, whereby a single best-estimated value of each structural parameter is obtained. However, structural models are often complex and involve many uncertain variables, where a unique solution that captures all the variability is not possible. Updating techniques using Bayesian Inference (BI) have been developed to quantify parametric uncertainty in analytical models. This paper presents the implementation of the BI in the parametric updating of a five-story building model and the quantification of its associated uncertainty. The Bayesian framework is implemented to update the model parameters and calculate the covariance matrix of the output parameters based on the experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is that the uncertainty in the experimental data is considered by defining the likelihood function as a multivariate normal distribution, leading to a better representation of the actual building behavior. The results showed that this Bayesian model-updating approach effectively allows a statistically rigorous update of the model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions, which is particularly useful in engineering applications where model accuracy is critical.

Details

Language :
English
ISSN :
20755309
Volume :
13
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Buildings
Publication Type :
Academic Journal
Accession number :
edsdoj.222d43dec61f451b8a45ea7afb0b7f97
Document Type :
article
Full Text :
https://doi.org/10.3390/buildings13061568