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An Efficient Reliability-based Optimization Method for Uncertain Structures Based on Non-probability Interval Model

C. Jiang1, Y.C. Bai1, X. Han1,2, H.M. Ning1
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Me-chanical and Vehicle Engineering, Hunan University, Changsha City 410082, P.R. China
Corresponding author.Tel:+86 731 88823993;fax:+86 731 88821445; Email:xu_han688@hotmail.com (X. Han)

Computers, Materials & Continua 2010, 18(1), 21-42. https://doi.org/10.3970/cmc.2010.018.021

Abstract

In this paper, an efficient interval optimization method based on a reliability-based possibility degree of interval (RPDI) is suggested for the design of uncertain structures. A general nonlinear interval optimization problem is studied in which the objective function and constraints are both nonlinear and uncertain. Through an interval order relation and a reliability-based possibility degree of interval, the uncertain optimization problem is transformed into a deterministic one. A sequence of approximate optimization problems are constructed based on the linear approximation technique. Each approximate optimization problem can be changed to a traditional linear programming problem, which can be easily solved by the simplex method. An iterative framework is also created, in which the design space is updated adaptively and a fine optimum can be well reached. Two numerical examples are investigated to demonstrate the effectiveness of the present method. Finally, it is employed to perform the optimization design of a practical automobile frame.

Keywords

Structural optimization, Uncertainty, Interval, Reliability, Possibility of degree

Cite This Article

C. . Jiang, Y. . Bai, X. . Han and H. . Ning, "An efficient reliability-based optimization method for uncertain structures based on non-probability interval model," Computers, Materials & Continua, vol. 18, no.1, pp. 21–42, 2010.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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