Reliability-based optimization of structural systems by adaptive target safety – Application to RC frames

Aoues, Younès; Chateauneuf, Alaa

doi:10.1016/j.strusafe.2006.10.002

Cited by 39 publications

(21 citation statements)

References 26 publications

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“…Incremental loading models were proposed and used to identify the main failure modes and obtain the system reliability . An updating procedure based on adaptive target reliabilities for SRBDO was proposed and aimed to find the best balance between satisfying the target system reliability and optimizing the structural performance . A new problem formulation and solution method for the general single‐objective redundancy allocation problem was proposed to deal with the multiple subsystems designed with either active or cold‐standby redundancy …”

Section: Introductionmentioning

confidence: 99%

“…20 An updating procedure based on adaptive target reliabilities for SRBDO was proposed and aimed to find the best balance between satisfying the target system reliability and optimizing the structural performance. 21 A new problem formulation and solution method for the general single-objective redundancy allocation problem was proposed to deal with the multiple subsystems designed with either active or cold-standby redundancy. 22 As mentioned previously in the literatures, most studies on structural optimization have focused on single-objective optimization of deterministic structures, while little study has been carried out to address multi-objective optimization of random structures.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Method for Structural System Reliability-Based Design Optimization and its Application to Trusses

Liu

Yin

2015

Qual. Reliab. Engng. Int.

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Most research studies on structural optimum design have focused on single‐objective optimization of deterministic structures, while little study has been carried out to address multi‐objective optimization of random structures. Statistical parameters and redundancy allocation problems should be considered in structural optimization. In order to address these problems, this paper presents a hybrid method for structural system reliability‐based design optimization (SRBDO) and applies it to trusses. The hybrid method integrates the concepts of the finite element method, radial basis function (RBF) neural networks, and genetic algorithms. The finite element method was used to compute structural responses under random loads. The RBF neural networks were employed to approximate structural responses for the purpose of replacing the structural limit state functions. The system reliabilities were calculated by Monte Carlo simulation method together with the trained RBF neural networks. The optimal parameters were obtained by genetic algorithms, where the system reliabilities were converted into penalty functions in order to address the constrained optimization. The hybrid method applied to trusses was demonstrated by two examples which were a typical 10‐bar truss and a steel truss girder structure. Detailed discussions and parameter analysis for the failure sequences such as web‐bucking failure and beam‐bending failure in the SRBDO were given. This hybrid method provides a new idea for SRBDO of trusses. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Structural System Reliability-Based Design Optimization and its Application to Trusses

Liu

Yin

2015

Qual. Reliab. Engng. Int.

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“…A probabilistic approach provides a rational and consistent framework for performing this task [4], employing probability models to characterize different possible model-parameters values. In this setting, the design objective is typically related to the expected value of a system performance measure, such as failure probability or expected life-cycle cost [2,[5][6][7][8]. For applications involving complex system models this expected value can rarely be calculated or accurately approximated analytically [9].…”

Section: Introductionmentioning

confidence: 99%

Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling

Jia

Taflanidis

2015

Advances in Engineering Software

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The implementation of NP-SSO (Non-Parametric Stochastic Subset Optimization) to general design under uncertainty problems and its enhancement through various soft computing techniques is discussed. NP-SSO relies on iterative simulation of samples of the design variables from an auxiliary probability density, and approximates the objective function through kernel density estimation (KDE) using these samples. To deal with boundary correction in complex domains, a multivariate boundary KDE based on local linear estimation is adopted in this work. Also, a non-parametric characterization of the search space at each iteration using a framework based on support vector machine is formulated. To further improve computational efficiency, an adaptive kernel sampling density formulation is integrated and an adaptive, iterative selection of the number of samples needed for the KDE implementation is established.

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“…The most common ones use approximate techniques for this purpose, such as the first-order reliability method (FORM) and secondorder reliability method (SORM) (Der Kiureghian 1996;Chiralaksanakul and Mahadevan 2005;Aoues and Chateauneuf 2008) or the Dimension Reduction Method (DRM) (Xu and Rahman 2004), frequently combined with decoupling approaches (Agarwal and Renaud 2006;Du and Chen 2004;Zou and Mahadevan 2006). For applications involving complex system or probability models that result in complex behavior within the failure domain, or problems with a large number of uncertain model parameters, the errors associated with those approximate methods for estimating system reliability may be contributing to potentially erroneous (i.e., suboptimal) results (Katafygiotis and Zuev 2008;Valdebenito and Schuëller 2010).…”

Section: Introductionmentioning

confidence: 99%

Non-parametric stochastic subset optimization for design problems with reliability constraints

Jia

Taflanidis

Beck

2015

Struct Multidisc Optim

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The Non-Parametric Stochastic Subset Optimization (NP-SSO) is a recently developed algorithm appropriate for optimization problems that use reliability criteria as objective function and involve computationally expensive numerical models for the engineering system under consideration. This paper discusses its extension to reliability-based design optimization (RBDO) applications involving reliability criteria as a design constraint. The foundation of NP-SSO is the formulation of an augmented problem where the design variables are artificially considered as uncertain. In this context, the reliability of the engineering system is proportional to an auxiliary probability density function related to the design variables. NP-SSO is based on simulation of samples from this density and approximation of this reliability through kernel density estimation (KDE) using these samples. The RBDO problem is then solved using this approximation for evaluating the reliability constraints over the entire design domain and identifying the feasible region satisfying them. To improve computational efficiency, an iterative approach is proposed; at the end of each iteration, a new reduced search space is identified with reliability satisfying relaxed constraints, until the algorithm converges to the feasible design domain satisfying the desired constraints. A second refinement stage after initial convergence is also proposed to further improve the accuracy of the identified feasible region. A non-parametric characterization of the search space using a framework based on multivariate boundary KDE and support vector machine is established. To further improve the efficiency of the stochastic sampling stage, an adaptive selection of the number of samples required for the KDE approximation is proposed.

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Reliability-based optimization of structural systems by adaptive target safety – Application to RC frames

Cited by 39 publications

References 26 publications

A Hybrid Method for Structural System Reliability-Based Design Optimization and its Application to Trusses

A Hybrid Method for Structural System Reliability-Based Design Optimization and its Application to Trusses

Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling

Non-parametric stochastic subset optimization for design problems with reliability constraints

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