Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 117726 |
| Seitenumfang | 31 |
| Fachzeitschrift | Computer Methods in Applied Mechanics and Engineering |
| Jahrgang | 436 |
| Frühes Online-Datum | 8 Jan. 2025 |
| Publikationsstatus | Veröffentlicht - 1 März 2025 |
Abstract
Engineering structures are often subject to various types of uncertainties, including random, interval, and fuzzy uncertainties. When dealing with hybrid uncertainties, global sensitivity analysis (GSA) becomes particularly challenging due to the computational complexity associated with double-loop procedures in numerical simulations. In this paper, an efficient framework for GSA with hybrid uncertainties is proposed. Generally, surrogate models, such as the radial basis function neural network (RBFNN), are used to reduce computational efforts by replacing real response functions. Then global sensitivity indices can be obtained efficiently by combining with numerical simulation-based methods. However, this process can introduce additional sources of error due to the stochastic nature of the simulations. This paper presents a general framework for GSA with hybrid uncertainties, where variance-based indices for random, interval and fuzzy inputs are defined. Furthermore, to avoid the error propagation commonly associated with simulation-based techniques and to improve the computational efficiency, analytical solutions for these indices and the gradient of the output variance are derived based on the RBFNN. An additional validation strategy is designed to verify the importance ranking of uncertain inputs. Four applications are introduced to demonstrate the efficiency and accuracy of the proposed method for GSA with hybrid uncertainties.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Numerische Mechanik
- Ingenieurwesen (insg.)
- Werkstoffmechanik
- Ingenieurwesen (insg.)
- Maschinenbau
- Physik und Astronomie (insg.)
- Allgemeine Physik und Astronomie
- Informatik (insg.)
- Angewandte Informatik
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in: Computer Methods in Applied Mechanics and Engineering, Jahrgang 436, 117726, 01.03.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Efficient global sensitivity analysis framework and approach for structures with hybrid uncertainties
AU - Liu, Jinxing
AU - Shi, Yan
AU - Ding, Chen
AU - Beer, Michael
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/3/1
Y1 - 2025/3/1
N2 - Engineering structures are often subject to various types of uncertainties, including random, interval, and fuzzy uncertainties. When dealing with hybrid uncertainties, global sensitivity analysis (GSA) becomes particularly challenging due to the computational complexity associated with double-loop procedures in numerical simulations. In this paper, an efficient framework for GSA with hybrid uncertainties is proposed. Generally, surrogate models, such as the radial basis function neural network (RBFNN), are used to reduce computational efforts by replacing real response functions. Then global sensitivity indices can be obtained efficiently by combining with numerical simulation-based methods. However, this process can introduce additional sources of error due to the stochastic nature of the simulations. This paper presents a general framework for GSA with hybrid uncertainties, where variance-based indices for random, interval and fuzzy inputs are defined. Furthermore, to avoid the error propagation commonly associated with simulation-based techniques and to improve the computational efficiency, analytical solutions for these indices and the gradient of the output variance are derived based on the RBFNN. An additional validation strategy is designed to verify the importance ranking of uncertain inputs. Four applications are introduced to demonstrate the efficiency and accuracy of the proposed method for GSA with hybrid uncertainties.
AB - Engineering structures are often subject to various types of uncertainties, including random, interval, and fuzzy uncertainties. When dealing with hybrid uncertainties, global sensitivity analysis (GSA) becomes particularly challenging due to the computational complexity associated with double-loop procedures in numerical simulations. In this paper, an efficient framework for GSA with hybrid uncertainties is proposed. Generally, surrogate models, such as the radial basis function neural network (RBFNN), are used to reduce computational efforts by replacing real response functions. Then global sensitivity indices can be obtained efficiently by combining with numerical simulation-based methods. However, this process can introduce additional sources of error due to the stochastic nature of the simulations. This paper presents a general framework for GSA with hybrid uncertainties, where variance-based indices for random, interval and fuzzy inputs are defined. Furthermore, to avoid the error propagation commonly associated with simulation-based techniques and to improve the computational efficiency, analytical solutions for these indices and the gradient of the output variance are derived based on the RBFNN. An additional validation strategy is designed to verify the importance ranking of uncertain inputs. Four applications are introduced to demonstrate the efficiency and accuracy of the proposed method for GSA with hybrid uncertainties.
KW - Analytical solution
KW - Hybrid uncertainties
KW - Radial basis function network
KW - Sobol’ indices
KW - Variance-based global sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=85214295672&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2024.117726
DO - 10.1016/j.cma.2024.117726
M3 - Article
AN - SCOPUS:85214295672
VL - 436
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 117726
ER -