Optimization algorithms for structural reliability

Li, Peiling; Kiureghian, Armen Der

doi:10.1016/0167-4730(91)90041-7

Cited by 537 publications

(230 citation statements)

References 7 publications

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“…The β-point can conveniently be computed by means of an optimisation algorithm (Liu and Der Kiureghian, 1991) based on the gradient of the limit-state function (16) and consequently of the peak load parameter λ p with respect to the basic variables w.…”

Section: Reliability Methodsmentioning

confidence: 99%

Size Sensitivity for the Reliability Index in Stochastic Finite Element Analysis of Damage

Gutiérrez

2006

Int J Fract

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Abstract. The direct differentiation method is applied to the estimation of statistical size effect behaviour in quasi-brittle solids. The scale factor is included in the finite element model and the autocorrelation function. Particular attention is paid to the proper differentiation of the Nataf transformation, which has been chosen to convert the basic random variables into a set of uncorrelated, standard normal variables. The predictive possibilities of the presented algorithm provide a valuable insight in the actual mechanisms responsible for failure. It can be evaluated to what extent the scale factor sensitivity of the failure probability is influenced by the phenomena related to the material disorder or the deterministic size effect.

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Section: Reliability Methodsmentioning

confidence: 99%

Size Sensitivity for the Reliability Index in Stochastic Finite Element Analysis of Damage

Gutiérrez

2006

Int J Fract

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“…For more details, the interested reader can the different works of Hasofer and Lind [5], Liu and Der Kiureghian [12] and Ditlevsen and Madsen [2].…”

Section: Reliability Analysismentioning

confidence: 99%

Recent methodologies for reliability-based design optimization

Kharmanda

Mohsine

Makloufi

et al. 2008

Int. J. Simul. Multidisci. Des. Optim.

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-In the field of Deterministic Design Optimization (DDO), the designer reduces the structural cost without taking into account uncertainties concerning materials, geometry and loading. This way the resulting optimum solution may represent a lower level of reliability and thus a higher risk of failure. But in the Reliability-Based Design Optimization (RBDO) model, the mean values of uncertain system variables are usually used as design variables, and the cost is optimized subject to prescribed probabilistic constraints as defined by a nonlinear mathematical programming problem. Therefore, a RBDO solution that reduces the structural weight in uncritical regions does not only provide an improved design but also a higher level of confidence in the design. In this paper, we present the advantage of the DDO and RBDO models and next some recent methodologies in order to show that the RBDO model is a practical tool for structural engineers.

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“…The problem (5) can, in theory, be solved by any nonlinear optimization algorithm (cf. [11] for comparison of various methods), however, two algorithms, namely the RackwitzFiessler algorithm [1] and NLPQL [17] are commonly assumed to be the most efficient ones.…”

Section: Since the Limit State Condition Is Also Transformed Intomentioning

confidence: 99%

Reliability analysis of a crashed thin-walled s-rail accounting for random spot weld failures

Stocki

Tauzowski

Knabel

2008

International Journal of Crashworthiness

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Possibly the most common application of spot welding is in the automobile manufacturing industry, where it is almost universally used to weld the sheet metal car components.However, due to manufacturing inaccuracies and fatigue failures an important number of spot welds may be missing in an operational vehicle. It seems that in order to properly analyze reliability of such structures, in particular crashworthiness reliability, the spot weld failures must be considered. Representing properties of each spot weld in a stochastic model by corresponding random variables is extremely inefficient. Therefore, in the current paper an approach is proposed for handling spot weld defects in the reliability analysis by accounting for their averaged influence on a failure criterion. The approach consists in appropriate treatment of a random noise component of the limit state function. The noise results from strategy of deleting a certain number of randomly selected spot weld elements from the finite element model each time the limit state function value is computed.Dealing with noisy limit state functions in structural reliability analysis is a challenging task. The only method that seems to be insensitive to this phenomenon is Monte Carlo sampling, which for most of the applications of practical interest is prohibitively expensive. Having this in mind, in the paper a method based on the algorithm proposed by Zou et al. in [23] is investigated. The method combines the best features of the first order reliability method, the response surface technique and the importance sampling method to achieve both accuracy and efficiency. A detailed study on the reliability of thin-walled s-rail subjected to crash is performed. Some suggestions concerning modification of the original algorithm are proposed.

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Optimization algorithms for structural reliability

Cited by 537 publications

References 7 publications

Size Sensitivity for the Reliability Index in Stochastic Finite Element Analysis of Damage

Size Sensitivity for the Reliability Index in Stochastic Finite Element Analysis of Damage

Recent methodologies for reliability-based design optimization

Reliability analysis of a crashed thin-walled s-rail accounting for random spot weld failures

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