A sub-convex similarity-based model updating method considering multivariate uncertainties
Abstract
This paper proposes an innovative model updating technique that thoroughly considers the interrelations among multivariate output features. The approach involves developing a novel uncertainty quantification metric, termed Sub-Convex Similarity. A specialised data preprocessing operator is proposed to reveal the inherent distributional attributes of multivariate datasets through a sequencing pre-processing. To manage the inherent randomness associated with sample location dispersion, we propose a binning algorithm based on the equal-bin-datapoints principle. This method allows for the quantification of multidimensional stochastic data without the need to calculate the joint probability distribution function. Utilising convex hull theory, sub-regional boundaries are established within each bin to reveal multivariate dataset characteristics. Sub-Convex Similarity serves as a metric for quantifying both interval-based and stochastic uncertainties, measuring discrepancies between simulated and experimental datasets in the context of both interval and stochastic model updating. The proposed model updating framework employs the sparrow search algorithm, a swarm intelligence-based optimization mechanism. The effectiveness and broad applicability of this approach are demonstrated through case studies involving a three-degree-of-freedom mass-spring system and a finite element model of a satellite, addressing multivariate uncertainties.
Details
- Organisationseinheit(en)
-
Institut für Risiko und Zuverlässigkeit
- Externe Organisation(en)
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University of Science and Technology Beijing
China Academy of Launch Vehicle Technology (CALT)
University of Strathclyde
The University of Liverpool
Tongji University
KU Leuven
- Typ
- Artikel
- Journal
- Engineering structures
- Band
- 318
- Anzahl der Seiten
- 18
- ISSN
- 0141-0296
- Publikationsdatum
- 01.11.2024
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Tief- und Ingenieurbau
- Elektronische Version(en)
-
https://doi.org/10.1016/j.engstruct.2024.118752 (Zugang:
Geschlossen
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