Paul, B and Nisha, AS and Manohar, CS (2023) BAYESIAN UPDATING OF GLOBAL RESPONSE SENSITIVITY INDICES IN AN INSTRUMENTED STRUCTURE. In: 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2023, Athens.
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Abstract
The study discusses updating the global response sensitivity indices (GRSI) of an instrumented structure by employing factor mapping method in conjunction with an implicit Bayesian framework for combined state and parameter estimation. In conventional factor mapping-based approaches, an ensemble of structural parameters are classified into two disjoint sets, depending on whether a sample vector produces a response in a predefined range of interest or not. A probabilistic distance between the samples, thus classified, is measured for each parameter or group of parameters. In this study, we use Bhattacharyya�s distance as the probabilistic distance measure to determine the sensitivity of the associated individual parameters or groups of parameters. The estimation of this distance requires the probability density functions (pdfs) of the underlying random variables. This in turn allows one to employ the posterior pdfs of parameters in computing Bhattacharya�s distance-based GRSI. These posterior pdfs are obtained from the combined state and parameter estimation problem, which ensures the assimilation of the measurement data while computing the GRSI values. In this study, the developed method of updating GRSI has been demonstrated on a five-storied, bending-torsion coupled, instrumented building frame subject to a scaled recorded ground motion. An application of GRSI in the context of structural engineering problems, specifically for model reduction is also discussed. © 2023 UNCECOMP Proceedings. All rights reserved.
| Item Type: | Conference Proceedings |
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| Publication: | UNCECOMP Proceedings |
| Publisher: | National Technical University of Athens |
| Additional Information: | The copyright for this article belongs to Authors. |
| Keywords: | Mapping; Parameter estimation; Sensitivity analysis, Bayesian frameworks; Bhattacharyyum�s distance; Factor mapping; Global response; Global response sensitivity analyse; Implicit kalman filter; MCMC; Response sensitivity; Response sensitivity analysis; Sensitivity indices, Probability density function |
| Department/Centre: | Division of Mechanical Sciences > Civil Engineering |
| Date Deposited: | 04 Mar 2024 09:33 |
| Last Modified: | 04 Mar 2024 09:33 |
| URI: | https://eprints.iisc.ac.in/id/eprint/84360 |
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