Bayesian updating of the displacement-strain transformation matrix

The updating of the displacement-strain transformation matrix has not garnered much attention over the years, as most studies focus on updating mass, damping, and stiffness matrices. In this study, updating of the parametrized displacement-strain transformation matrix is done in a Bayesian probabili...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2024-06, Vol.2647 (19), p.192021
Main Authors: Adarsh, S, Panda, Jagajyoti, Ray-Chaudhuri, S
Format: Article
Language:English
Subjects:
Bayesian analysis
Conditional probability
Damage localization
Damping
Data storage
Markov chains
Monte Carlo simulation
Parameter estimation
Parameter robustness
Parameter uncertainty
Probability density functions
Statistical analysis
Stiffness matrix
Strain
Structural health monitoring
Transformations (mathematics)
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The updating of the displacement-strain transformation matrix has not garnered much attention over the years, as most studies focus on updating mass, damping, and stiffness matrices. In this study, updating of the parametrized displacement-strain transformation matrix is done in a Bayesian probabilistic framework by combining the data from both acceleration and strain sensors. The Bayesian framework ensures that the uncertainties, especially modeling uncertainties, are explicitly considered and provides multiple possible estimates of the parameters, unlike a classical estimation framework where only one value is estimated. Samples from the posterior probability density function of the parameters are simulated using the transitional Markov Chain Monte Carlo method. The updated displacement-strain transformation matrix can be used to obtain strains with higher fidelity in numerical simulations and to obtain robust response predictions incorporating modeling uncertainties. In addition, once a transformation matrix has been updated, only acceleration data are required for damage localization, i.e., strain measurements are not required post-updating, thereby reducing the overall health monitoring costs and data storage burden. The Bayesian updating procedure is investigated using experimental data from a four-story shear building laboratory model.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2647/19/192021