Efficient model-correction-based reliability analysis of uncertain dynamical systems

authored by: Benjamin Hirzinger, Udo Nackenhorst
Abstract: The scope of this paper is to apply a model-correction-based strategy for efficient reliability analysis of uncertain dynamical systems based on a low-fidelity (LF) model whose outcomes are corrected in a probabilistic sense to represent the more realistic outcomes of a high-fidelity (HF) model. In the model-correction approach utilized, the LF model is calibrated to the HF model close to the so-called most probable point in standard normal space, which allows a more realistic assessment of the considered complex dynamical system. Since only few expensive limit state function evaluations of the HF model are required, an efficient reliability analysis is enabled. In an application example, the LF model describes an existing single-span railway bridge modelled as simply supported Euler–Bernoulli beam subjected to moving single forces representing the axle loads of a moving train. The HF modelling approach accounts for the bridge–train interaction by modelling the passing train as mass-spring-damper system, however increasing the computational effort of the limit state function evaluations. Failure probabilities evaluated with the model-correction approach are contrasted and discussed with failure probabilities of the sophisticated bridge–train interaction model evaluated with the first-order reliability method (FORM). It is demonstrated that the efficiency of the method depends on the correlation between the LF and the HF model. A comparison of the results of FORM and the model-correction-based approach shows that the latter provides reliable failure probability prediction of the HF model while leading to a significant reduction in computational effort.
Organisation(s): Institute of Mechanics and Computational Mechanics
Type: Article
Journal: Acta mechanica
Volume: 235
Pages: 1419–1436
No. of pages: 18
ISSN: 0001-5970
Publication date: 03.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Computational Mechanics, Mechanical Engineering
Electronic version(s): https://doi.org/10.1007/s00707-023-03499-1 (Access: Open)

BibTeX

@article{7119f3bdd2fd40ddac49262cb214e652,
title = "Efficient model-correction-based reliability analysis of uncertain dynamical systems",
abstract = "The scope of this paper is to apply a model-correction-based strategy for efficient reliability analysis of uncertain dynamical systems based on a low-fidelity (LF) model whose outcomes are corrected in a probabilistic sense to represent the more realistic outcomes of a high-fidelity (HF) model. In the model-correction approach utilized, the LF model is calibrated to the HF model close to the so-called most probable point in standard normal space, which allows a more realistic assessment of the considered complex dynamical system. Since only few expensive limit state function evaluations of the HF model are required, an efficient reliability analysis is enabled. In an application example, the LF model describes an existing single-span railway bridge modelled as simply supported Euler–Bernoulli beam subjected to moving single forces representing the axle loads of a moving train. The HF modelling approach accounts for the bridge–train interaction by modelling the passing train as mass-spring-damper system, however increasing the computational effort of the limit state function evaluations. Failure probabilities evaluated with the model-correction approach are contrasted and discussed with failure probabilities of the sophisticated bridge–train interaction model evaluated with the first-order reliability method (FORM). It is demonstrated that the efficiency of the method depends on the correlation between the LF and the HF model. A comparison of the results of FORM and the model-correction-based approach shows that the latter provides reliable failure probability prediction of the HF model while leading to a significant reduction in computational effort.",
author = "Benjamin Hirzinger and Udo Nackenhorst",
note = "Funding Information: Funding of the International Research Training Group (IRTG) 2657 by DFG is gratefully acknowledged (Grant Reference Number 433082294) ",
year = "2024",
month = mar,
doi = "10.1007/s00707-023-03499-1",
language = "English",
volume = "235",
pages = "1419–1436",
journal = "Acta mechanica",
issn = "0001-5970",
publisher = "Springer-Verlag Wien",
number = "3",
}