The use of genetic algorithms for structural optimization of hybrid sandwich panels

• Main Author: Escusa, Gonçalo Gomes
• Publication Date: 2017
• Other Authors: Sena-Cruz, José, Cruz, Fábio, Pereira, E. N. B., Valente, Isabel B., Barros, Joaquim A. O.
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/1822/46870
• topic: Genetic algorithms; Structural optimization; Sandwich panels; Multi-objective optimization; Engenharia e Tecnologia::Engenharia Civil

This paper describes the procedures followed to develop an optimization method for the design of a sandwich panel to be used in flooring applications. This sandwich panel is composed of polyurethane foam core, fibre reinforced polymer bottom layer and webs, and a fibre reinforced mortar top layer. The possibility of adopting additional internal ribs to increase the flexural and shear stiffness was also considered. The panel was described using a standard stacking sequence, coded as a string, using continuous variables to describe the geometric, economic and environmental parameters, and discrete variables to describe the laminate stack architecture. The optimization procedure was based on a global approach strategy, divided into two steps: (i) firstly, the features of each individual panel solution were assessed by analytical procedures and a fitness was assigned using a ranking function; (ii) secondly, the multi-objective optimization problem was solved by using a genetic algorithm, which performs a random search from generation to generation and keeps the “best individuals”. Penalty criteria were also considered when any panel solution was not satisfying the restrictions and design requirements. Different solutions were obtained by imposing different restrictions to the design of the sandwich panel, namely considering: (i) the length; (ii) the width; and, (iii) the use of one or two types of fibres (carbon and glass). This paper discusses the results obtained, both regarding the performance of the optimization procedure developed and the optimal solutions obtained for each case studied.