Reliability‐Based Optimization of Series Systems of Parallel Systems

Enevoldsen, I.; Sørensen, John Dalsgaard

doi:10.1061/(asce)0733-9445(1993)119:4(1069)

Cited by 37 publications

(9 citation statements)

References 13 publications

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“…This computation is not only expensive but also inaccurate due to system reliability approximations. These difficulties lead to instability problems and therefore convergence cannot be guaranteed [18].…”

Section: Optimal System Safety Factors For Design Optimizationmentioning

confidence: 99%

Optimal Design Under Uncertainty of Reinforced Concrete Structures Using System Reliability Approach

Aoues

Chateauneuf

Lemosse

et al. 2013

Int. J. UncertaintyQuantification

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The design of reinforced concrete (RC) structures involves several kinds of uncertainties, which are usually considered through the partial safety factors prescribed in the codes of practice. The traditional design optimization of RC structures uses deterministic information of the problem. The partial safety factors are used to consider loading fluctuation and the variability of material properties. The use of these safety factors in deterministic optimization usually leads to over-designing structures, as these safety factors are calibrated for a large class of structures. In the deterministic optimization procedure the reliability cannot be controlled. For this reason, the Reliability-Based Design Optimization (RBDO) is devoted to design economical and reliable structures. However, the RBDO problem involves the evaluation of probabilistic constraints performed by the reliability analysis. The most common RBDO formulations are based on a nested optimization problem, with an outer loop for the design optimization and an inner loop for the reliability analysis. Therefore, an expensive computation effort is required to solve the RBDO problem. In this paper, a new RBDO method of RC structures is proposed. The RBDO problem is decomposed to several cycles of deterministic design optimization (DDO) based on new safety factors called Optimal System Safety Factors (OSSF). At each cycle of the DDO, the system reliability analysis is performed to verify the reliability of the optimal design, the OSSF are computed by a probabilistic method on the basis of the previous system reliability analysis, then the updated OSSF are provided for the next cycle of the DDO. The application to the design of an RC structure shows the interest and the efficiency of the proposed method.

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Section: Optimal System Safety Factors For Design Optimizationmentioning

confidence: 99%

Optimal Design Under Uncertainty of Reinforced Concrete Structures Using System Reliability Approach

Aoues

Chateauneuf

Lemosse

et al. 2013

Int. J. UncertaintyQuantification

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“…The component failure events contribute to a system failure event. This failure event can be a series, parallel or a combination of series and parallel of all the individual failure events 4 . There are also other system failure criteria, for example a case where a system is said to have failed if K out of N components fail.…”

Section: Reliability Based Optimizationmentioning

confidence: 99%

Monte Carlo Simulation in Reliability Based Optimization Applied to Multidisciplinary System Design

Padmanabhan¹,

Tappeta²,

Batill³

2003

44th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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The work presented in the paper deals with the incorporation of Monte Carlo Simulation (MCS) techniques in Reliability Based Optimization (RBO). The main criteria used to select the suitable MCS techniques are the ease of obtaining analytical sensitivities and the smoothness of the probability of failure estimates with changes in design. The MCS techniques that best suit these criteria are Conditional Expectation MCS techniques that consist of the Directional Simulation and Axis-Orthogonal Simulation techniques. Details of obtaining probability of failure estimates and its sensitivities using these simulation techniques for component reliability and series system reliability are presented. A strategy for performing RBO using the axis-orthogonal simulation is presented. This strategy was applied to two multidisciplinary test problems. The first test problem is a simple analytic problem that is used for demonstration purposes. The second test problem is a control-augmented structure problem that has been used in various Multidisciplinary Design Optimization (MDO) studies. The MCS based RBO converged for both test problems and substantial improvements from initial designs, obtained using First Order Reliability Method based RBO, were observed for both * Graduate Research Assistant, Student Member AIAA. † Mechanical Engineer, Member AIAA. ‡ Professor, Associate Fellow AIAA.

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“…A non‐redundant structure exhibits a higher reliability index than the redundant structure for the same acceptable level of damage tolerance . 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 .…”

Section: Introductionmentioning

confidence: 99%

“…19 Incremental loading models were proposed and used to identify the main failure modes and obtain the system reliability. 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.…”

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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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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Reliability‐Based Optimization of Series Systems of Parallel Systems

Cited by 37 publications

References 13 publications

Optimal Design Under Uncertainty of Reinforced Concrete Structures Using System Reliability Approach

Optimal Design Under Uncertainty of Reinforced Concrete Structures Using System Reliability Approach

Monte Carlo Simulation in Reliability Based Optimization Applied to Multidisciplinary System Design

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

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