Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 110040 |
Fachzeitschrift | Mechanical Systems and Signal Processing |
Jahrgang | 188 |
Frühes Online-Datum | 27 Dez. 2022 |
Publikationsstatus | Veröffentlicht - 1 Apr. 2023 |
Abstract
Interval arithmetic offers a powerful tool for structural model updating when uncertain-but-bounded parameters are considered. However, the application of interval model updating for practical engineering structure is hindered due to model complexity and huge computational burden involved in the repeated evaluations of non-probabilistic constraints. In this light, an efficient inner-outer decoupling scheme is proposed for non-probabilistic model updating in this study. The mathematical operation of interval model updating is decomposed into two layers labelled as inner layer with the operation of uncertainty propagation and outer layer with the operation of interval optimization. In the inner uncertainty propagation, the High Dimensional Model Representation (HDMR) is utilized to enable the decomposition of the model outputs in terms of multivariate inputs into the sum of multiple single-variate functions, which is further approximated by Chebyshev polynomials so that the stationary points of each function can be derived. In the outer layer, a fast-running optimization strategy based on the stationary points of Chebyshev polynomial approximation is proposed to accelerate tracking the bounds of model parameters by avoiding time-consuming brute-force interval optimization. As a result, the original non-probabilistic updating process with two interacted layers can be completely decoupled into two independent operations of the inner uncertainty propagation and outer interval optimization so as to enhance the search efficiency and convergence rate significantly. Two numerical case studies illustrate capability of the proposed method in updating the structural parameters intervals efficiently with the model outputs intervals agreeing well with the testing outputs intervals. Two experimental cases of steel plates and the Canton Tower also demonstrate the efficiency and advantages of the method in interval model updating.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Signalverarbeitung
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Luft- und Raumfahrttechnik
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Mechanical Systems and Signal Processing, Jahrgang 188, 110040, 01.04.2023.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Efficient inner-outer decoupling scheme for non-probabilistic model updating with high dimensional model representation and Chebyshev approximation
AU - Mo, Jiang
AU - Yan, Wang Ji
AU - Yuen, Ka Veng
AU - Beer, Michael
N1 - Funding Information: Financial support to complete this study was provided by the Science and Technology Development Fund, Macau SAR (File no.: FDCT/017/2020/A1, FDCT/0101/2021/A2, FDCT/0010/2021/AGJ and SKL-IOTSC(UM)-2021-2023), the Research Committee of University of Macau (File no.: MYRG2020-00073-IOTSC, MYRG2022-00096-IOTSC and SRG2019-00194-IOTSC) and Guangdong-Hong Kong-Macau Joint Laboratory Program (Project No.: 2020B1212030009). Furthermore, the authors are grateful to professors at the Hong Kong Polytechnic University for sharing the ambient vibration data of Canton Tower on their website. The views and opinions expressed in this article are those of the authors and do not necessarily reflect those of the sponsors.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - Interval arithmetic offers a powerful tool for structural model updating when uncertain-but-bounded parameters are considered. However, the application of interval model updating for practical engineering structure is hindered due to model complexity and huge computational burden involved in the repeated evaluations of non-probabilistic constraints. In this light, an efficient inner-outer decoupling scheme is proposed for non-probabilistic model updating in this study. The mathematical operation of interval model updating is decomposed into two layers labelled as inner layer with the operation of uncertainty propagation and outer layer with the operation of interval optimization. In the inner uncertainty propagation, the High Dimensional Model Representation (HDMR) is utilized to enable the decomposition of the model outputs in terms of multivariate inputs into the sum of multiple single-variate functions, which is further approximated by Chebyshev polynomials so that the stationary points of each function can be derived. In the outer layer, a fast-running optimization strategy based on the stationary points of Chebyshev polynomial approximation is proposed to accelerate tracking the bounds of model parameters by avoiding time-consuming brute-force interval optimization. As a result, the original non-probabilistic updating process with two interacted layers can be completely decoupled into two independent operations of the inner uncertainty propagation and outer interval optimization so as to enhance the search efficiency and convergence rate significantly. Two numerical case studies illustrate capability of the proposed method in updating the structural parameters intervals efficiently with the model outputs intervals agreeing well with the testing outputs intervals. Two experimental cases of steel plates and the Canton Tower also demonstrate the efficiency and advantages of the method in interval model updating.
AB - Interval arithmetic offers a powerful tool for structural model updating when uncertain-but-bounded parameters are considered. However, the application of interval model updating for practical engineering structure is hindered due to model complexity and huge computational burden involved in the repeated evaluations of non-probabilistic constraints. In this light, an efficient inner-outer decoupling scheme is proposed for non-probabilistic model updating in this study. The mathematical operation of interval model updating is decomposed into two layers labelled as inner layer with the operation of uncertainty propagation and outer layer with the operation of interval optimization. In the inner uncertainty propagation, the High Dimensional Model Representation (HDMR) is utilized to enable the decomposition of the model outputs in terms of multivariate inputs into the sum of multiple single-variate functions, which is further approximated by Chebyshev polynomials so that the stationary points of each function can be derived. In the outer layer, a fast-running optimization strategy based on the stationary points of Chebyshev polynomial approximation is proposed to accelerate tracking the bounds of model parameters by avoiding time-consuming brute-force interval optimization. As a result, the original non-probabilistic updating process with two interacted layers can be completely decoupled into two independent operations of the inner uncertainty propagation and outer interval optimization so as to enhance the search efficiency and convergence rate significantly. Two numerical case studies illustrate capability of the proposed method in updating the structural parameters intervals efficiently with the model outputs intervals agreeing well with the testing outputs intervals. Two experimental cases of steel plates and the Canton Tower also demonstrate the efficiency and advantages of the method in interval model updating.
KW - Finite element model updating
KW - HDMR
KW - Interval arithmetic
KW - Non-probabilistic uncertainty
KW - Polynomial approximation
UR - http://www.scopus.com/inward/record.url?scp=85144619365&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2022.110040
DO - 10.1016/j.ymssp.2022.110040
M3 - Article
AN - SCOPUS:85144619365
VL - 188
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 110040
ER -