Open Access

ARTICLE

Structural Interval Reliability Algorithm Based on Bernstein Polynomials and Evidence Theory

by Xu Zhang¹, Jianchao Ni², Juxi Hu^3,*, Weisi Chen⁴

1 College of Software Engineering, Xiamen University of Technology, Xiamen, Fujian, 361005, China
2 School of Aerospace Engineering, Xiamen University, Xiamen, Fujian, 361005, China
3 School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
4 College of Software Engineering, Xiamen University of Technology, Xiamen, Fujian, 361005, China

* Corresponding Author: Juxi Hu. Email:

Computer Systems Science and Engineering 2023, 46(2), 1947-1960. https://doi.org/10.32604/csse.2023.035118

Received 08 August 2022; Accepted 08 December 2022; Issue published 09 February 2023

Abstract

Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors. Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid. The uncertain parameters mainly exist in the form of intervals. This method requires a lot of calculation and is often difficult to achieve efficiently. In order to solve this problem, this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty. Based on the non-probabilistic reliability index method, the extreme value of the limit state function is obtained using the properties of Bernstein polynomials, thus avoiding the need for a lot of sampling to solve the reliability analysis problem. The method is applied to numerical examples and engineering applications such as experiments, and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method, especially for some reliability problems with higher nonlinearity. Moreover, this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.

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