Efficient strategies for reliability-based optimization involving non-linear, dynamical structures

Valdebenito, Marcos A.; Schuëller, Gerhart I.

doi:10.1016/j.compstruc.2010.10.014

Cited by 68 publications

(26 citation statements)

References 36 publications

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“…Therefore, it is more appropriate to explicitly integrate the uncertainty within the framework of reliability-based design optimisation (RBDO). RBDO has been successfully tested in many practical cases, for example for design optimisation in the automotive and aircraft industries among others [17,18,19,20,21,22,23]. However, the use of such approach for complex problems still remains limited by the computing time and is up to now far from being a practical tool for all engineering applications and in particular for 3D metal forming processes.…”

Section: Introductionmentioning

confidence: 98%

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

Abdessalem

Hami²

2015

International Journal of Mechanical Sciences

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Section: Introductionmentioning

confidence: 98%

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

Abdessalem

Hami²

2015

International Journal of Mechanical Sciences

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“…The model includes non linear hysteretic devices (NLD) that follow an elasto-plastic law. For details on the model, it is referred to [4]. The results obtained for the optimization procedure are shown in Fig.…”

Section: Example and Conclusionmentioning

confidence: 99%

“…, n DV . In turn, these sensitivities are estimated using an approach proposed in [4]; this approach consists in generating a (approximate) linear model of the normalized demand, i.e. :…”

Section: Solution Strategymentioning

confidence: 99%

Efficient Reliability Sensitivity Estimation for Optimization Under Uncertainty

Valdebenito

Schuëller

2009

Proc Appl Math and Mech

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“…Similar to the decoupling approaches with approximate reliability methods, the decoupling approaches for RBDO problems with sampling-based reliability methods attempt to approximate the probability of failure throughout the optimization process. For example, in [25,24,47], the probability of failure is approximated by an exponential function of design parameters, while in [2] and [12] the Bayesian theorem is applied to approximate the reliability problem based on samples from the failure domain. Applications of decoupling approaches to the RO formulation include the Design Space Root Finding (DSRF) method, which aims to approximate the failure probabilities over the design space by calculating the roots of the limit state function [21].…”

Section: Short Literature Reviewmentioning

confidence: 99%

Coupling the cross-entropy with the line sampling method for risk-based design optimization

Depina

Papaioannou²,

Straub³

et al. 2016

Struct Multidisc Optim

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An algorithm for risk-based optimization (RO) of engineering systems is proposed, which couples the Cross-entropy (CE) optimization method with the Line Sampling (LS) reliability method. The CE-LS algorithm relies on the CE method to optimize the total cost of a system that is composed of the design and operation cost (e.g., production cost) and the expected failure cost (i.e., failure risk). Guided by the random search of the CE method, the algorithm proceeds iteratively to update a set of random search distributions such that the optimal or near-optimal solution is likely to occur. The LS-based failure probability estimates are required to evaluate the failure risk. Throughout the optimization process, the coupling relies on a local weighted average approximation of the probability of failure to reduce the computational demands associated with RO. As the CE-LS algorithm proceeds to locate a region of design parameters with near-optimal solutions, the local weighted average approximation of the probability of failure is refined. The adaptive refinement procedure is repeatedly applied until convergence criteria with respect to both the optimization and the approximation of the failure probability are satisfied. The performance of the proposed optimization heuristic is examined empiri- cally on several RO problems, including the design of a monopile foundation for offshore wind turbines.

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Efficient strategies for reliability-based optimization involving non-linear, dynamical structures

Cited by 68 publications

References 36 publications

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

Efficient Reliability Sensitivity Estimation for Optimization Under Uncertainty

Coupling the cross-entropy with the line sampling method for risk-based design optimization

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