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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 108
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping
Paper 216

Multiobjective Optimization for Sustainable Design of Fibre Reinforced Polymer Composite Structures

C.A. Conceição António

LAETA/INEGI, Faculty of Engineering, University of Porto, Portugal

Full Bibliographic Reference for this paper
, "Multiobjective Optimization for Sustainable Design of Fibre Reinforced Polymer Composite Structures", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 216, 2015. doi:10.4203/ccp.108.216
Keywords: sustainability, multiobjective optimization, hybrid composites, fibre reinforced polymer composites, memetic algorithm, non-domination, learning procedures.

Summary
One approach for decreasing costs in lightweight structures using (FRP) composite materials adopting hybrid constructions is proposed. In this approach expensive and high-stiffness materials are used together with inexpensive and low-stiffness material. The optimal design procedure considers sizing, topology and material selection addressed in a multiobjective design optimization (MDO) framework, which considers minimum weight or cost and minimum strain energy. The trade-off between the objectives, depending on given stress, displacement and buckling constraints imposed on composite structures, is searched. Ply angles and ply thicknesses of shell laminates, the cross section dimensions of stiffeners and the variables related to material distribution are considered as design variables. A multiobjective memetic algorithm (MOMA) searching the Pareto-optimal front based on non-dominance concepts is proposed. MOMA is an evolutionary algorithm based on Darwin principles together with learning procedures using Dawkins concepts. MOMA applies multiple learning procedures exploring different cultural transmission rules. The approach is based on multiple populations, species conservation, migration, self-adaptive, local search, controlled mutation, age control and features-based allele's statistics. MOMA is promising in MDO with the aim of obtaining lightweight design solutions for composite structures and indicating alternative optimal designs what might be very important for the designers.

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