Reliability analysis of laminated composite structures using finite elements and neural networks |
| |
Authors: | PAM Lopes HM Gomes AM Awruch |
| |
Affiliation: | 1. Graduate Program in Civil Engineering, Federal University of Rio Grande do Sul, R. Osvaldo Aranha, 99, 3°. Andar, 90035-190, Porto Alegre, RS, Brazil;2. Graduate Program in Mechanical Engineering, Federal University of Rio Grande do Sul, R. Sarmento Leite, 425, sala 202, 2°. Andar, 90050-170, Porto Alegre, RS, Brazil |
| |
Abstract: | Saving of computer processing time on the reliability analysis of laminated composite structures using artificial neural networks is the main objective of this work. This subject is particularly important when the reliability index is a constraint in the optimization of structural performance, because the task of looking for an optimum structural design demands also a very high processing time. Reliability methods, such as Standard Monte Carlo (SMC), Monte Carlo with Importance Sampling (MC–IS), First Order Reliability Method (FORM) and FORM with Multiple Check Points (FORM–MCPs) are used to compare the solution and the processing time when the Finite Element Method (FEM) is employed and when the finite element analysis (FEA) is substituted by trained artificial neural networks (ANNs). Two ANN are used here: the Multilayer Perceptron Network (MPN) and the Radial Basis Network (RBN). Several examples are presented, including a shell with geometrically non-linear behavior, which shows the advantages using this methodology. |
| |
Keywords: | Structural reliability Laminated composite structures Artificial neural networks |
本文献已被 ScienceDirect 等数据库收录! |
|