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A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis
HighlightsDGPR-based MCS is a dynamic reliability method based on GPR and MCS.Monte Carlo technique is utilized to estimate the MPP.The dynamic strategy makes DGPR-based MCS low dependence on the initial samples.The use of GPR and dynamic strategy greatly reduces the number of calls to FEA.The method shows high efficiency on cases study including engineering application.
AbstractThe performance function of a complex engineering structure is always highly nonlinear and implicit, and its reliability needs to be evaluated through a time-consuming computer codes, such as finite element analysis (FEA). Thus, computational efficiency and precision are hard to unify when using traditional reliability methods in large-scale complex engineering structures. In this paper, a Dynamic Gaussian Process Regression surrogate model based on Monte Carlo Simulation (DGPR-based MCS) was proposed for the reliability analysis of complex engineering structures. A small number of training samples are created by random approach with FEA codes for building the Gaussian process regression (GPR) surrogate model, and the highly nonlinear and implicit performance function is approximated by GPR with an explicit formulation under a small sample condition. Then, combined with the trained GPR surrogate model, the most probable point (MPP) is quickly predicted using Monte Carlo sample technique without any further FEA. An iterative algorithm is presented to refine the GPR using the information of the MPP to continually improve the reconstruction precision in the important region, which significantly contributes to the probability of failure, and the probability of failure is taken as a convergence condition. The proposed method has advantages of high efficiency and high precision compared to the traditional response surface method (RSM). It can directly take advantage of existing engineering structural software without modification.
A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis
HighlightsDGPR-based MCS is a dynamic reliability method based on GPR and MCS.Monte Carlo technique is utilized to estimate the MPP.The dynamic strategy makes DGPR-based MCS low dependence on the initial samples.The use of GPR and dynamic strategy greatly reduces the number of calls to FEA.The method shows high efficiency on cases study including engineering application.
AbstractThe performance function of a complex engineering structure is always highly nonlinear and implicit, and its reliability needs to be evaluated through a time-consuming computer codes, such as finite element analysis (FEA). Thus, computational efficiency and precision are hard to unify when using traditional reliability methods in large-scale complex engineering structures. In this paper, a Dynamic Gaussian Process Regression surrogate model based on Monte Carlo Simulation (DGPR-based MCS) was proposed for the reliability analysis of complex engineering structures. A small number of training samples are created by random approach with FEA codes for building the Gaussian process regression (GPR) surrogate model, and the highly nonlinear and implicit performance function is approximated by GPR with an explicit formulation under a small sample condition. Then, combined with the trained GPR surrogate model, the most probable point (MPP) is quickly predicted using Monte Carlo sample technique without any further FEA. An iterative algorithm is presented to refine the GPR using the information of the MPP to continually improve the reconstruction precision in the important region, which significantly contributes to the probability of failure, and the probability of failure is taken as a convergence condition. The proposed method has advantages of high efficiency and high precision compared to the traditional response surface method (RSM). It can directly take advantage of existing engineering structural software without modification.
A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis
Su, Guoshao (Autor:in) / Peng, Lifeng (Autor:in) / Hu, Lihua (Autor:in)
Structural Safety ; 68 ; 97-109
27.06.2017
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Machine learning classifier-based dynamic surrogate model for structural reliability analysis
| Taylor & Francis Verlag | 2025
| British Library Conference Proceedings | 2014