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Global structural optimization considering expected consequences of failure and using ANN surrogates

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Author(s):
de Santana Gomes, Wellison Jose ; Beck, Andre Teofilo
Total Authors: 2
Document type: Journal article
Source: COMPUTERS & STRUCTURES; v. 126, p. 13-pg., 2013-09-15.
Abstract

The literature is filled with structural optimization articles which claim to minimize costs but which disregard the costs of failure. Due to uncertainties, minimum cost can only be achieved by considering expected consequences of failure. This article discusses challenges in solving real structural optimization problems, taking into account expected consequences of failure. The solution developed herein combines non-linear FE analysis (by positional FEM), structural reliability analysis, Artificial Neural Networks (used as surrogates for objective function) and a hybrid Particle Swarm Optimization algorithm, which efficiently solves for the global optimum. Optimization of a steel-frame transmission line tower is the application example. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 09/17365-6 - Risk optimization under random fatigue and corrosion processes
Grantee:Wellison José de Santana Gomes
Support Opportunities: Scholarships in Brazil - Doctorate