A response surface method based on weighted regression for structural reliability analysis

Kaymaz, İrfan; McMahon, Chris

doi:10.1016/j.probengmech.2004.05.005

Cited by 255 publications

(120 citation statements)

References 13 publications

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“…However, as Rajashekhar and Ellingwood [27] have pointed out, further iterations might be needed, and they proposed an iterative RSM technique, for which more detailed information can be found in the related references. In order to use some properties of the structural reliability problems in the construction of the RSF, Kaymaz [28] proposed a new approach, called Adaptive Response Surface Method (ADAPRES), based on weighted regression to get the coefficients of the RSF as explained in the following section.…”

Section: Standard Response Surface Methodsmentioning

confidence: 99%

“…Therefore, in the following normal regression is replaced by a weighted regression method in which the RSF is formed by giving higher weight to the experimental points closer to the limit state surface. In addition, the experimental points are chosen based on sign evaluation [28] that reduces the whole design space in order to generate experimental points from the region where the β-point is most likely to exist.…”

Section: Adapres: a Response Surface Methods For Structural Reliabilitmentioning

confidence: 99%

“…where β t is the target reliability index which is usually taken as 3, and β is the computed reliability index which is obtained from the reliability evaluation of the performance function given in (28). Although the performance function can be explicitly defined in terms of the applied loads and geometrical parameters, a finite element model given in Fig.…”

Section: Example: Reliability-based Design Optimization Of a Cantilevmentioning

confidence: 99%

“…By giving higher weights to the experimental points close to the limit state function as explained in Section 3.2.1, the RSF will be closer to the limit state function. After forming the RSF, it is used in place of the performance function given in (28) in the reliability analysis for which FORM is utilized, thus the reliability index β is computed corresponding to the design variables. This process continues for each value of the design variables at optimization iterations until the new design satisfies the constraint given in (29), and the optimum results obtained using RBDO ADAPRES are presented in Table (3).…”

Section: Solution Using Rbdo Adapresmentioning

confidence: 99%

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Approximation methods for reliability‐based design optimization problems

Kaymaz

2007

GAMM-Mitteilungen

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Key words Reliability-index based approach, performance-measure approach, response surface method.Deterministic optimum designs are obtained without considering of uncertainties related to the problem parameters such as material parameters (yield stress, allowable stresses, moment capacities, etc.), external loadings, manufacturing errors, tolerances, cost functions, which could lead to unreliable designs, therefore several methods have been developed to treat uncertainties in engineering analysis and, more recently, to carry out design optimization with the additional requirement of reliability, which referred to as reliability-based design optimization. In this paper, two most common approaches for reliability-based design optimization are reviewed, one of which is reliability-index based approach and the other performancemeasure approach. Although both approaches can be used to evaluate the probabilistic constraint, their use can be prohibitive when the associated function evaluation required by the probabilistic constraint is expensive, especially for real engineering problems. Therefore, an adaptive response surface method is proposed by which the probabilistic constraint is replaced with a simple polynomial function, thus the computational time can be reduced significantly as presented in the example given in this paper.

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Section: Standard Response Surface Methodsmentioning

confidence: 99%

Section: Adapres: a Response Surface Methods For Structural Reliabilitmentioning

confidence: 99%

Section: Example: Reliability-based Design Optimization Of a Cantilevmentioning

confidence: 99%

Section: Solution Using Rbdo Adapresmentioning

confidence: 99%

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Approximation methods for reliability‐based design optimization problems

Kaymaz

2007

GAMM-Mitteilungen

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“…Because of this property RSM is widely used in real life problems. Response surface methods have been used in applications such as product quality optimization [1; 3; 7; 8; 12], quality control [19], process optimization [16; 17; 18], ergonomic designs [9], structural reliability [10], and multidisciplinary design optimization problems [4; 11; 13].…”

Section: Introductionmentioning

confidence: 99%

Multi Response Optimization Application on a Manufacturing Factory

Akçay

Anagün

2013

MCA

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Abstract-The purpose of real-life problems is often to be able to find less expensive and more effective ways of production without compromising product quality because companies must provide competitive advantage to maintain existence. In order to improve quality, design of experiment techniques is employed. RSM is a widely used technique thanks to its minimum number of experiment requirement. Hence it is used especially with continuous solution spaces and high-cost experimentations. Moreover, in most cases there is more than one response that firms must optimize simultaneously. For instance companies want to reduce the costs while improving product quality. Decision making is more difficult when conflicting objectives exist. For this reason multi response optimization is an important field to study. In this study, optimization of a manufacturing problem with two responses was carried out by the application of response surface methodology (RSM) and desirability function.

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Computational Methods for Risk and Uncertainty Propagation

2012

Modelling Under Risk and Uncertainty

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A response surface method based on weighted regression for structural reliability analysis

Cited by 255 publications

References 13 publications

Approximation methods for reliability‐based design optimization problems

Approximation methods for reliability‐based design optimization problems

Multi Response Optimization Application on a Manufacturing Factory

Computational Methods for Risk and Uncertainty Propagation

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