On the Robustness of Structural Risk Optimization With Respect to Epistemic Uncertainties

Beck, André Teófilo; Gomes, Wellison José de Santana; Bazán, Felipe Alexander Vargas

doi:10.1615/int.j.uncertaintyquantification.v2.i1.20

Cited by 26 publications

(5 citation statements)

References 30 publications

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“…As observed above, in this manuscript we do not propose to compete with alternative ways of addressing epistemic uncertainties in structural reliability and reliability-based optimization problems [56][57][58][59][60][66][67][68][69][70]. We include this sub-section only because we foresee a generalization, beyond the scope of this manuscript, to handling of epistemic uncertainties in reliability-based optimization.…”

Section: Epistemic Uncertainties In Structural Reliability and Optimizationmentioning

confidence: 99%

“…Epistemic uncertainties have also been considered in the context of reliability-based design optimization [60][61][62][63][64][65][66][67] and risk optimization [68][69][70]. Approaches specifically addressing insufficient information [61][62][63][64] lead to compromise solutions between reducing the objective function and increasing confidence on constraint feasibility.…”

Section: Epistemic Uncertainties In Structural Reliability and Optimi...mentioning

confidence: 99%

See 1 more Smart Citation

Optimal design of redundant structural systems: fundamentals

Beck

2020

Engineering Structures

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Section: Epistemic Uncertainties In Structural Reliability and Optimizationmentioning

confidence: 99%

Section: Epistemic Uncertainties In Structural Reliability and Optimi...mentioning

confidence: 99%

Optimal design of redundant structural systems: fundamentals

Beck

2020

Engineering Structures

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“…This methodology has since been further developed and multiple definitions of robustness are found in literature. Two main classes of methods can be drawn from it: the first is aimed at minimizing the output variance, see, e.g., [3,4,5], while the second is aimed at optimizing of both the objective function and the variance associated with this optimum, see, e.g., [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Robust Design Optimization of Expensive Stochastic Simulators Under Lack-of-Knowledge

Mierlo

Persoons

Faes

et al. 2023

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Robust design optimisation of stochastic black-box functions is a challenging task in engineering practice. Crashworthiness optimisation qualifies as such problem especially with regards to the high computational costs. Moreover, in early design phases, there may be significant uncertainty about the numerical model parameters. Therefore, this paper proposes an adaptive surrogate-based strategy for robust design optimisation of noise-contaminated models under lack-of-knowledge uncertainty. This approach is a significant extension to the Robustness under Lack-of-Knowledge method (RULOK) previously introduced by the authors, which was limited to noise-free models. In this work it is proposed to use a Gaussian Process as a regression model based on a noisy kernel. The learning process is adapted to account for noise variance either imposed and known or empirically learned as part of the learning process. The method is demonstrated on three analytical benchmarks and one engineering crashworthiness optimisation problem. In the case studies, multiple ways of determining the noise kernel are investigated: (1) based on a coefficient of variation, (2) calibration in the Gaussian Process model, (3) based on engineering judgement, including a study of the sensitivity of the result with respect to these parameters. The results highlight that the proposed method is able to efficiently identify a robust design point even with extremely limited or biased prior knowledge about the noise.

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“…So, the optimal solution in practice can be very sensitive to the design uncertainties and may easily crosses the reliability constraint limits. To avoid such a situation, the optimal design should either be represented with a confidence range (margin) or an optimization process with the objective of maintaining the desired level of reliability [1]. Reliability-based design optimization (RBDO) methodologies have been introduced to incorporate uncertainties in the design components and to provide a scheme seeking an optimal solution with the desired level of reliability.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Quantile-Based Adaptive Sampling RBDO with Shifting Constraint Strategy

Rahmani¹,

Fadakar²

2023

Avantgarde Reliability Implications in Civil Engineering

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There is an increasing demand for the performance optimization under the reliability constraints in various engineering problems. These problems are commonly known as reliability-based design optimization (RBDO) problems. Among different RBDO frameworks, the decoupled methods are widely accepted for their high efficiency and stability. However, when facing problems with high nonlinearity and nonnormally distributed random variables, they lose their computational performance. In this study, a new efficient decoupled method with two level quantile-based sampling strategy is presented. The strategies introduced for two level sampling followed by information reuse of nearby designs are intended to enhance the sampling from failure region, thus reducing the number of samples to improve the efficiency of sampling-based methods. Compared with the existing methods which decouples RBDO in the design space and thus need to struggle with searching for most probable point (MPP), the proposed method decouples RBDO in the probability space to further make beneficial use of an efficient optimal shifting value search strategy to reach an optimal design in less iterations. By comparing the proposed method with crude MCS and other sampling-based methods through benchmark examples, our proposed method proved to be competitive in dramatically saving the computational cost.

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On the Robustness of Structural Risk Optimization With Respect to Epistemic Uncertainties

Abstract: In the context of structural design, risk optimization allows one to find a proper point of balance between the concurrent goals of economy and safety. Risk optimization involves the minimization of total expected costs

Cited by 26 publications

References 30 publications

Optimal design of redundant structural systems: fundamentals

Optimal design of redundant structural systems: fundamentals

Robust Design Optimization of Expensive Stochastic Simulators Under Lack-of-Knowledge

An Efficient Quantile-Based Adaptive Sampling RBDO with Shifting Constraint Strategy

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