Reliability-based structural optimization using neural networks and Monte Carlo simulation

Papadrakakis, Manolis; Lagaros, Nikos D.

doi:10.1016/s0045-7825(02)00287-6

Cited by 437 publications

(169 citation statements)

References 19 publications

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“…Despite the significant reduction of the computational time, the method is still time consuming and, for this reason, several variance reduction techniques, such as importance sampling or directional simulation, have been widely applied. An extensive description, as well as, numerical examples of the use of ANN-based MCS in structural reliability can be found in ( [31], [32], [27], [33], [34]). …”

Section: Ann-based Methods Of Failure Probability Computationmentioning

confidence: 99%

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

et al. 2015

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Section: Ann-based Methods Of Failure Probability Computationmentioning

confidence: 99%

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

et al. 2015

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“…Furthermore, many of the model parameters are statistically and/or physically dependent and their mathematical representations are of a nonlinear nature. Consequently, the ( ) function and the failure domain cannot be easily expressed or approximated by an analytical model (Papadrakakis and Lagaros 2002;.…”

Section: Probability Of Failurementioning

confidence: 99%

“…As the number of random variables increases and the problem becomes more complex, Monte Carlo (MC) simulation-based methods are found to be more reliable (Papadrakakis and Lagaros 2002). In each MC realisation, ( ) is evaluated for each segment of the structure and failure is defined by the violation of ( ) for any segment, i.e., failure is when the minimum of ( ), for all segments, is ≤ 0.…”

Section: Probability Of Failurementioning

confidence: 99%

The sensitivity of bridge safety to spatial correlation of load and resistance

2016

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Publication informationStructures, 5 : 23-34 Publisher ElsevierItem record/more information http://hdl.handle.net/10197/6997 Publisher's statement þÿ T h i s i s t h e a u t h o r s v e r s i o n o f a w o r k t h a t w a s a c c e p t e d f o r p u b l i c a t i o n i n S t r u c t u r e s .Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this AbstractRandom Field theory has emerged in recent years to model the statistical correlation of resistance in concrete structures and to determine its influence on the probability of structural failure. A major shortcoming in the work carried out to date is the spatial variability and corresponding correlation associated with applied traffic loads. In this paper the influence of spatial correlation of both traffic load and resistance is considered in the context of bridge safety assessment. The current study, explores, the nature of the problem by three theoretical examples. As a general trend, examples show that while traffic loads are weakly correlated, load effects are strongly correlated as the same heavy vehicle often causes extremes of load effect in different parts of the bridge which is due to the transverse sharing of load (measured here using a load sharing factor).It is found that the strength of correlation of load effect depends greatly on the load sharing factor which is treated in a simple way in many studies. In a more sophisticated beam-and-slab bridge example, load sharing factors are derived from a finite element analysis to assess transverse load sharing, and are shown to vary by girder number, girder segment and by load location. Despite the fact that load effect at points along the length of a bridge are strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small

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“…The development, over the last two decades [1], on the stochastic analysis methods has stimulated the interest for probabilistic structural optimization problems. There are two distinguished design formulations to account for the probabilistic systems response: Robust Design Optimization (RDO) [2][3][4] and Reliability-Based Design Optimization (RBDO) [5][6][7][8]. RDO formulations primarily seek to minimize the influence of stochastic variations on the mean design.…”

Section: Introductionmentioning

confidence: 99%

Reliability based robust design optimization of steel structures

Lagaros

Plevris

Papadrakakis

2007

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

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-In this work the uncertainty of a structural system is taken into account in the framework of a structural Reliability based Robust Design Optimization (RRDO) formulation where probabilistic constraints are incorporated into the robust design optimization formulation. A robust design optimization problem is formulated as a multi-criteria optimization problem. The Pareto front representing the solution of the RRDO problem is composed by designs with a state of robustness, since their performance is the least sensitive to the variability of the uncertain variables. The cross section dimensions together with other structural parameters, such as the modulus of elasticity, the yield stress and the applied loading, are considered as random variables. For the solution of the RRDO problem, the non-dominant Cascade Evolutionary Algorithm is employed combined with a weighted Tchebycheff metric.

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Reliability-based structural optimization using neural networks and Monte Carlo simulation

Cited by 437 publications

References 19 publications

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

The sensitivity of bridge safety to spatial correlation of load and resistance

Reliability based robust design optimization of steel structures

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