Overcoming the drawbacks of the FORM using a full characterization method

López, Rosalía Rodríguez; Torii, André Jacomel; Miguel, Leandro Fleck Fadel; Cursi, Eduardo Souza de

doi:10.1016/j.strusafe.2015.02.003

Cited by 31 publications

(19 citation statements)

References 33 publications

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“…Indeed, although the FORM has been widely applied to approximate the probability of failure of engineering systems, it has been observed that FORM presents some serious drawbacks in several situations, such as low accuracy and convergence issues [3,4]. Hence, we proposed two fronts:…”

Section: Projects Developed Within the Rbdo Contextmentioning

confidence: 99%

Optimization Under Uncertainty

Mun¹

2012

Modeling Risk

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Section: Projects Developed Within the Rbdo Contextmentioning

confidence: 99%

Optimization Under Uncertainty

Mun¹

2012

Modeling Risk

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“…Currently, the key issue in the modern structural design is to properly consider the uncertainties and geometrical properties on the loads and materials when analyzing and designing complex structural systems. 1,2 In the last decades, the first-order second-moment (FOSM) and the second-order second-moment (SOSM) 3 and Monte Carlo simulation (MCS) 4,5 have been widely employed. These methods are the most frequently used tools to approximate the failure probability of engineering systems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic sensitivity analysis for the frame structure of missiles

Yang

Yue

Zhao

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this study, the high-performance and high-reliability of missiles were achieved under the background of the probabilistic analysis for frame structure of missiles. On the basis of deep investigation of the artificial neural network method and mathematical model of multiorder moment method, artificial neural network based higher order moment approach was proposed for the probabilistic analysis of complex structure. As illustrated in frame structure probabilistic analysis with multiphysics fields based on artificial neural network based higher order moment, the reliability and failure probability of frame structure were studied. Furthermore, it was shown that the radial concentrated force P and the outer radius of the frame R2 are the most important factors, and the inside radius of the frame R1 and the ultimate tensile strength of materials σ b also have an important influence on the frame structure. Through comparison with Monte Carlo simulation, second-order fourth-moment approach and the artificial neural network based first-order second-moment approach, it was demonstrated that artificial neural network based higher order moment reshapes the possibility of complex aero-missile probabilistic analysis and improves computing efficiency while preserving the accuracy. Artificial neural network based higher order moment offers a useful insight for missile reliability design and optimization with multi-discipline. The efforts of this study also enrich the theory and method of mechanical reliability design.

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“…• the nature of the problems is different: in RBDO, the probability of failure is part of the constraints, while in RO, it is part of the objective function. • the great majority of these RBDO methods are based on the first-order reliability method (FORM), that may be inaccurate in the case of nonlinear limit state functions and non Gaussian random variables [26,30].…”

Section: Introductionmentioning

confidence: 99%

A gradient-based polynomial chaos approach for risk and reliability-based design optimization

Torii

López

Miguel

2017

J Braz. Soc. Mech. Sci. Eng.

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This paper presents an approach for the solution of risk and reliability-based design optimization problems using gradient-based algorithms together with polynomial chaos expansions (PCE) for probability of failure evaluation and sensitivity analysis. The approach is intended to alleviate the computational burden required by such problems with accurate evaluation of probabilities of failure. Benchmark problems from the literature are solved to assess the effectiveness of the proposed approach and the results show that it is able to obtain accurate solutions with tractable computational effort. The main limitation of the proposed approach, inherited from the PCE, is that the computational effort grows very fast for an increasing number of random variables.

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Overcoming the drawbacks of the FORM using a full characterization method

Cited by 31 publications

References 33 publications

Optimization Under Uncertainty

Optimization Under Uncertainty

Probabilistic sensitivity analysis for the frame structure of missiles

A gradient-based polynomial chaos approach for risk and reliability-based design optimization

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