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

2019 ◽  
Vol 37 (1) ◽  
pp. 120-143
Author(s):  
Payam Asadi ◽  
Hosein Sourani

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.

2015 ◽  
Vol 32 (3) ◽  
pp. 643-667 ◽  
Author(s):  
Zhiyuan Huang ◽  
Haobo Qiu ◽  
Ming Zhao ◽  
Xiwen Cai ◽  
Liang Gao

Purpose – Popular regression methodologies are inapplicable to obtain accurate metamodels for high dimensional practical problems since the computational time increases exponentially as the number of dimensions rises. The purpose of this paper is to use support vector regression with high dimensional model representation (SVR-HDMR) model to obtain accurate metamodels for high dimensional problems with a few sampling points. Design/methodology/approach – High-dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for improving the efficiency of deducing high dimensional input-output system behavior. Support vector regression (SVR) method can approximate the underlying functions with a small subset of sample points. Dividing Rectangles (DIRECT) algorithm is a deterministic sampling method. Findings – This paper proposes a new form of HDMR by integrating the SVR, termed as SVR-HDMR. And an intelligent sampling strategy, namely, DIRECT method, is adopted to improve the efficiency of SVR-HDMR. Originality/value – Compared to other metamodeling techniques, the accuracy and efficiency of SVR-HDMR were significantly improved. The SVR-HDMR helped engineers understand the essence of underlying problems visually.


2013 ◽  
Vol 353-356 ◽  
pp. 3155-3158
Author(s):  
Wei Tao Zhao ◽  
Cheng Kui Niu ◽  
Lei Jia

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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