Application of the Sequential Optimization and Reliability Assessment Method to Structural Design Problems

This paper proposed a reliability design model for composite materials under the mixture of random and interval variables. Together with the inverse reliability analysis technique, the sequential single-loop optimization method is applied to the reliability-based design of composites. In the sequential single-loop optimization, the optimization and the reliability analysis are decoupled to improve the computational efficiency. As shown in examples, the minimum weight problems under the constraint of structural reliability are solved for laminated composites. The Particle Swarm Optimization (PSO) algorithm is utilized to search for the optimal solutions. The design results indicate that, under the mixture of random and interval variables, the method that combines the sequential single-loop optimization and the PSO algorithm can deal effectively with the reliability-based design of composites.

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“…Recent studies on a new type of method -the single loop method can avoid the nested loops of optimization and reliability assessment [8,12] .…”

Section: Sequential Single-loop Optimization Methods For the Reliabilimentioning

confidence: 99%

Reliability-based design of composites under the mixed uncertainties and the optimization algorithm

Chen

Wei

2008

Acta Mech. Solida Sin.

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“…Processing time is a crucial requirement for achieving the computational efficiency of an optimization algorithm (Tyni and Ylinen, 2006). The low number of function evaluations is a requirement for the computational efficiency of an optimization process (Liu et al, 2003;Sahai et al, 2004). Computational efficiency increases with the reduction in the number of limit state function evaluations (Du and Chen, 2002).…”

Section: Introductionmentioning

confidence: 99%

An Efficient Optimization Procedure for Reliability-Based Robust Multi-objective Design Applied to Reinforced Concrete Structures

Cassimiro,

Nascimento,

Motta

et al. 2023

Preprint

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Concrete structure designs use semi-probabilistic methods that apply safety factors to compensate for uncertainties, increasing loads and reducing resistances. While this ensures safety and reliability, probabilistic methods offer a more comprehensive approach by quantifying the probability of failure for a structure. We applied Reliability-Based Design Optimization (RBDO) and Reliability-Based Robust Multi-objective Design Optimization (RBRMDO) to a reinforced concrete linear plane frame. The study compared efficiency among procedures, considering accuracy, processing time, iterations, function evaluations, and Pareto point distribution quality. The main contribution lies in investigating various algorithm combinations to identify the most efficient and robust procedure for multi-objective optimization under uncertainties in concrete structure designs. The numerical methods Reliability Index Approach (RIA), Performance Measure Approach (PMA), Single-Loop Approach (SLA), and Sequential Optimization and Reliability Assessment (SORA) were coupled with Weighted Sum (WS) and Normal-Boundary Intersection (NBI) methods to build the Pareto frontier. The optimization used Sequential Quadratic Programming (SQP) through the Scipy library. The structural analysis employed the Finite Element Method (FEM) through the CasPy package. The reliability analysis employed the First-Order Reliability Method (FORM) for RIA and PMA through the Pystra package. SLA and SORA used a FORM routine implemented by us. HL-RF algorithm assessed the Most Probable Point (MPP) and finite differences calculated numerical derivatives. We tested four examples before the main design. SLA is the most efficient algorithm, and SORA presents excellent results. RIA and PMA underperformed in processing time and limit state function evaluations. NBI outperformed in the Pareto point distribution quality compared to WS.

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“…Qu and Haftka (Qu and Haftka 2003) has proposed a probabilistic sufficiency factor when using MCS. As far as probabilistic design methods concern, though there are a large quantity of archived papers on this topic, most of them are based on the approximation of MPP Liu et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment Using Discriminative Sampling and Metamodeling

Wang¹,

Wang²,

Shan³

2005

SAE Technical Paper Series

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Reliability assessment is the foundation for reliability engineering and reliability-based design optimization. It has been a difficult task, however, to perform both accurate and efficient reliability assessment after decades of research. This work proposes an innovative method that deviates significantly from conventional methods. It applies a discriminative sampling strategy to directly generate more points close to or on the limit state. A sampling guidance function was developed for such a strategy. Due to the dense samples in the neighborhood of the limit state, a kriging model can be built which is especially accurate near the limit state. Based on the kriging model, reliability assessment can be performed. The proposed method is tested by using well-known problems in the literature. It is found that it can efficiently assess the reliability for problems of single failure region and has a good performance for problems of multiple failure regions. The features and limitations of the method are also discussed, along with the comparison with the importance sampling (IS) based assessment methods.

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Application of the Sequential Optimization and Reliability Assessment Method to Structural Design Problems

Cited by 15 publications

References 43 publications

Reliability-based design of composites under the mixed uncertainties and the optimization algorithm

Reliability-based design of composites under the mixed uncertainties and the optimization algorithm

An Efficient Optimization Procedure for Reliability-Based Robust Multi-objective Design Applied to Reinforced Concrete Structures

Reliability Assessment Using Discriminative Sampling and Metamodeling

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