Evaluation of accuracy and efficiency of some simulation and sampling methods in structural reliability analysis

Eamon, Christopher D.; Thompson, Michel D; Liu, Zhenyu

doi:10.1016/j.strusafe.2005.05.001

Cited by 20 publications

(6 citation statements)

References 31 publications

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“…Over the last few decades, several computational methods that can be linked to non- Direct MCS (Melchers 1999, Haldar and Mahadevan 2000, Nowak and Collins 2000 and more advanced simulation techniques such as the importance sampling (IS) method (Melchers 1999), the Latin Hypercube Sampling (LHS) (Nowak and Collins 2000) or the directional sampling technique (DS) (Melchers 1999) give good overall results and have been applied for nearly all types of structural reliability problems (Eamon et al 2005). However, they require significant numbers of runs especially for problems with high numbers of random variables and low probabilities of failure.…”

Section: Methodsmentioning

confidence: 99%

Simplified probabilistic non-linear assessment of existing railway bridges

Wiśniewski

Casas

Ghosn

2009

Structure and Infrastructure Engineering

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Section: Methodsmentioning

confidence: 99%

Simplified probabilistic non-linear assessment of existing railway bridges

Wiśniewski

Casas

Ghosn

2009

Structure and Infrastructure Engineering

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“…In addition, there is a load random variable, live load pressure (LL). Based on the results of reliability analysis of GRP structures in a previous study (Thompson et al 2005), material thickness variability is deemed insignificant and not included here.…”

Section: Design Constraints In Cas Rbdomentioning

confidence: 99%

Integrated reliability and sizing optimization of a large composite structure

Eamon

Rais‐Rohani

2009

Marine Structures

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Eamon, C. D., and Rais-Rohani, M. (2009). "Integrated reliability and sizing optimization of a large composite structure." Marine Structures, 22(2), 315-334, doi: 10.1016/j.marstruc.2008.03 AbstractIn this paper, we present the application of probabilistic design modeling and reliability-based design optimization (RBDO) methodology to the sizing optimization of a composite advanced submarine sail structure under parametric uncertainty. With the help of probabilistic sensitivity analysis, the influence of individual random variables on each structural failure mode is examined, and the critical modes are treated as probabilistic design constraints under consistent lower bounds on the corresponding reliability indices. Whereas the failure modes are applied to structural components in the solution of the RBDO problem, the overall system reliability is also evaluated as a post-optimization step. The results indicate that in comparison to a deterministic optimum design, the structural mass of the probabilistic optimum design is slightly higher when consistent probabilistic constraints are imposed, and the overall structural stiffness is found to be more critical than individual component laminate ply thicknesses in meeting the specified design constraints. Moreover, the post-optimality analysis shows that the overall system failure probability of the probabilistic optimum design is more than 50% lower than that of the deterministic optimal design with less than 5% penalty in structural mass. ------------------------------

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“…In FORM, the limit-state function is linearized at the MPP and the probability of failure is directly computed from the distance from the origin to the MPP (Mansour and Olsson 2018), denoted as the first-order reliability index. However, approximating the probability of failure using a linear function can result in high inaccuracies, which limits the applicability of FORM for engineering applications (Eamon et al 2005;Schuëller et al 2004). In SORM, the limit-state is approximated by a quadratic function using a second-order Taylor expansion at the MPP, in order to achieve higher accuracy in the probability estimates.…”

Section: Introductionmentioning

confidence: 99%

Second-order reliability methods: a review and comparative study

Mansour

Olsson

et al. 2021

Struct Multidisc Optim

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Second-order reliability methods are commonly used for the computation of reliability, defined as the probability of satisfying an intended function in the presence of uncertainties. These methods can achieve highly accurate reliability predictions owing to a second-order approximation of the limit-state function around the Most Probable Point of failure. Although numerous formulations have been developed, the lack of full-scale comparative studies has led to a dubiety regarding the selection of a suitable method for a specific reliability analysis problem. In this study, the performance of commonly used second-order reliability methods is assessed based on the problem scale, curvatures at the Most Probable Point of failure, first-order reliability index, and limit-state contour. The assessment is based on three performance metrics: capability, accuracy, and robustness. The capability is a measure of the ability of a method to compute feasible probabilities, i.e., probabilities between 0 and 1. The accuracy and robustness are quantified based on the mean and standard deviation of relative errors with respect to exact reliabilities, respectively. This study not only provides a review of classical and novel second-order reliability methods, but also gives an insight on the selection of an appropriate reliability method for a given engineering application.

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Evaluation of accuracy and efficiency of some simulation and sampling methods in structural reliability analysis

Cited by 20 publications

References 31 publications

Simplified probabilistic non-linear assessment of existing railway bridges

Simplified probabilistic non-linear assessment of existing railway bridges

Integrated reliability and sizing optimization of a large composite structure

Second-order reliability methods: a review and comparative study

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