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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Lirong Sha,Tongyu Wang.Structural Reliability Analysis for Implicit Performance with Legendre Orthogonal Neural Network Method[J].Journal of Harbin Institute Of Technology(New Series),2016,23(1):60-66.DOI:10.11916/j.issn.1005-9113.2016.01.009.
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Structural Reliability Analysis for Implicit Performance with Legendre Orthogonal Neural Network Method
Author NameAffiliation
Lirong Sha School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022,China
School of Civil Engineering, Jilin Jianzhu University, Changchun 130118, China 
Tongyu Wang School of Civil Engineering, Jilin Jianzhu University, Changchun 130118, China 
Abstract:
In order to evaluate the failure probability of a complicated structure, the structural responses usually need to be estimated by some numerical analysis methods such as finite element method (FEM). The response surface method (RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However, the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network (LONN)-based RSM to estimate the structural reliability. In this method, the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method, i.e. first-order reliability methods (FORM) to calculate the failure probability of the structure. Numerical examples show that the proposed approach is applicable to structural reliability analysis, as well as the structure with implicit performance functions.
Key words:  reliability  orthogonal function  performance function  artificial neural network
DOI：10.11916/j.issn.1005-9113.2016.01.009
Clc Number:TB121;O346
Fund:

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