contributor author | Luca Rosafalco | |
contributor author | Saeed Eftekhar Azam | |
contributor author | Stefano Mariani | |
contributor author | Alberto Corigliano | |
date accessioned | 2024-04-27T22:48:52Z | |
date available | 2024-04-27T22:48:52Z | |
date issued | 2024/03/01 | |
identifier other | 10.1061-AJRUA6.RUENG-1085.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4297566 | |
description abstract | Identifying the mechanical properties of civil structures is required for life-cycle assessment. Kalman filters are exploited for this goal, enabling the online update of a numerical model, acting as the digital twin of the structure, and quantifying the uncertainty of the estimated properties. As uncertainty about model formulation is usually disregarded in the identification, model class evidence has been recently formulated to compare different parametrizations of the properties of the monitored structure through a metric, allowing selection of the most plausible one. When dealing with parameter estimation, predominantly model evidence is deployed in batch Bayesian estimation. Here, the formulation of model class evidence is proposed for the unscented Kalman filter, which allows online calculation of model class evidence for a system without the need to compute the mapping gradient in time. This formulation was inspired by the model class evidence developed for the extended Kalman filter. Numerical results related to shear buildings are presented to validate the metric, showing the impact of under- and over-parametrizations on identification. | |
publisher | ASCE | |
title | System Identification via Unscented Kalman Filtering and Model Class Selection | |
type | Journal Article | |
journal volume | 10 | |
journal issue | 1 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering | |
identifier doi | 10.1061/AJRUA6.RUENG-1085 | |
journal fristpage | 04023063-1 | |
journal lastpage | 04023063-14 | |
page | 14 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 001 | |
contenttype | Fulltext | |