Reliability-Based Structural Optimization Using Hybrid High Dimensional Model Representation

A design method of reliability-based structural optimization has a powerful advantage because some random variables can be considered. However, the sensitivity analysis of reliability with respect to random variables is very complicated and its computational cost is very expensive. Thus, in this paper, based on hybrid high dimensional model representation (HDMR) and first order second moment (FOSM) method, a new method for the reliability-based structural optimization is proposed. A numerical example is presented to demonstrate the computational efficiency of the proposed method. It is shown that the proposed method can reduce the number of finite element calculation and has the high efficiency.

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A bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique for high-dimensional problems

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216629504 ◽

2016 ◽

Vol 231 (12) ◽

pp. 2173-2186 ◽

Cited By ~ 3

Author(s):

Yaping Ju ◽

Geoff Parks ◽

Chuhua Zhang

Keyword(s):

Support Vector Regression ◽

Computational Cost ◽

Model Representation ◽

High Dimensional ◽

Support Vector ◽

High Dimensional Model Representation ◽

Dimensional Model ◽

Comparison Results ◽

Modeling Accuracy ◽

Metamodeling Technique

A major challenge of metamodeling in simulation-based engineering design optimization is to handle the “curse of dimensionality,” i.e. the exponential growth of computational cost with increase of problem dimensionality. Encouragingly, it has been reported recently that a high-dimensional model representation assisted by a radial basis function is capable of deriving high-dimensional input–output relationships at dramatically reduced computational cost. In this article, support vector regression is employed as an alternative to be coupled with high-dimensional model representation for the metamodeling of high-dimensional problems. In particular, the bisection sampling method is proposed to be used in the metamodeling process to generate high-quality training samples. Testing and comparison results show that the developed bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique can achieve high modeling accuracy with a smaller number of training sample evaluations. For the problem examined in this study, the bisection-sampling-based support vector regression–high-dimensional model representation enables high modeling accuracy and linear computational complexity as the problem dimensionality increases. Analysis of this performance advantage shows that the use of bisection method enables the developed metamodeling technique to be more effective in dealing with high-dimensional problems.

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Fragility curves production by seismic improvement of the high-dimensional model representation method

Engineering Computations ◽

10.1108/ec-12-2018-0586 ◽

2019 ◽

Vol 37 (1) ◽

pp. 120-143

Author(s):

Payam Asadi ◽

Hosein Sourani

Keyword(s):

Fragility Curves ◽

Random Variables ◽

Performance Level ◽

Model Representation ◽

High Dimensional ◽

High Dimensional Model Representation ◽

Response Functions ◽

Dimensional Model ◽

Content Type ◽

Representation Method

Purpose In the absence of random variables, random variables are generated by the Monte Carlo (MC) simulation method. There are some methods for generating fragility curves with fewer nonlinear analyses. However, the accuracy of these methods is not suitable for all performance levels and peak ground acceleration (PGA) range. This paper aims to present a method through the seismic improvement of the high-dimensional model representation method for generating fragility curves while taking advantage of fewer analyses by choosing the right border points. Design/methodology/approach In this method, the values of uncertain variables are selected based on the results of the initial analyses, the damage limit of each performance level or according to acceptable limits in the design code. In particular, PGAs are selected based on the general shape of the fragility curve for each performance limit. Also, polynomial response functions are estimated for each accelerogram. To evaluate the accuracy, fragility curves are estimated by different methods for a single degree of freedom system and a reinforced concrete frame. Findings The results indicated that the proposed method can not only reduce the computational cost but also has a higher accuracy than the other methods, compared with the MC baseline method. Originality/value The proposed response functions are more consistent with the actual values and are also congruent with each performance level to increase the accuracy of the fragility curves.

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