A dynamic reliability analysis method based on support vector machine and Monte Carlo simulation

Pan, X.; Zhang, Yangu

doi:10.3233/jcm-193473

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…performance to construct a response surface and achieved good results, such as artificial neural network (Han et al 2019, Marugan et al 2019) (ANN), radial basis function (Zhang et al 2021, Wang andFang 2018) (RBF), support vector regression (Pan et al 2020) (SVR).…”

Section: Introductionmentioning

confidence: 99%

Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOSA hybrid algorithm

et al. 2023

Preprint

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The traditional probabilistic reliability analysis methods have problems such as poor convergence, low calculation accuracy, and long time-consuming in the reliability calculation of concrete arch bridges. Due to the uncertainty of the parameters of the structure itself, the performance function is highly nonlinear, and other factors. A reliability calculation method for concrete arch bridges based on the Kriging model and particle swarm optimization algorithm (PSOSA) based on a simulated annealing algorithm is proposed. Take advantage of the Kriging model in small samples, nonlinear, high-dimensional data processing capabilities. With the help of the PSO algorithm, it has the advantages of strong global optimization ability and strong robustness. Combined with the SA algorithm self-adaptive, variable probability mutation operation. The ability of the PSO algorithm to get rid of the local minima is enhanced and supplemented, effectively avoiding falling into the local minima and making the result tend to the global optimum, which improves the slow convergence speed and precociousness of the traditional PSO algorithm. A numerical example verifies the method's effectiveness, and a reliability evaluation of an actual concrete arch bridge is carried out. The research results show that the method improves the calculation accuracy, dramatically improves the calculation efficiency, and realizes the rapid and accurate assessment of the reliability of complex bridge structures.

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Section: Introductionmentioning

confidence: 99%

Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOSA hybrid algorithm

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…When the performance function is strongly nonlinear, the widely used classical response surface method Zhu 2012, Gavin andYau 2008) (RSM) based on quadratic polynomial is difficult to accurately approximate the real performance function, resulting in a significant calculation error. In recent years, some scholars have proposed using a regression model with better regression performance to construct a response surface and achieved good results, such as artificial neural network (Han et al 2019, Marugan et al 2019) (ANN), radial basis function (Zhang et al 2021, Wang andFang 2018) (RBF), support vector regression (Pan et al 2020) (SVR).…”

Section: Introductionmentioning

confidence: 99%

Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOSA hybrid algorithm

Li,

Ye,

et al. 2023

Artif Intell Rev

View full text Add to dashboard Cite

The traditional probabilistic reliability analysis methods have problems such as poor convergence, low calculation accuracy, and long time-consuming in the reliability calculation of concrete arch bridges. Due to the uncertainty of the parameters of the structure itself, the performance function is highly nonlinear, and other factors. A reliability calculation method for concrete arch bridges based on the Kriging model and particle swarm optimization algorithm (PSOSA) based on a simulated annealing algorithm is proposed. Take advantage of the Kriging model in small samples, nonlinear, high-dimensional data processing capabilities.With the help of the PSO algorithm, it has the advantages of strong global optimization ability and strong robustness. Combined with the SA algorithm self-adaptive, variable probability mutation operation. The ability of the PSO algorithm to get rid of the local minima is enhanced and supplemented, effectively avoiding falling into the local minima and making the result tend to the global optimum, which improves the slow convergence speed and precociousness of the traditional PSO algorithm. A numerical example verifies the method's effectiveness, and a reliability evaluation of an actual concrete arch bridge is carried out. The research results show that the method improves the calculation accuracy, dramatically improves the calculation efficiency, and realizes the rapid and accurate assessment of the reliability of complex bridge structures.

show abstract

A dynamic reliability analysis method based on support vector machine and Monte Carlo simulation

Cited by 2 publications

References 10 publications

Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOSA hybrid algorithm

Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOSA hybrid algorithm

Reliability intelligence analysis of concrete arch bridge based on Kriging model and PSOSA hybrid algorithm

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