Optimization of multispan ribbed plywood plate macrostructure for multiple load cases

Abstract

This paper discusses an optimized structural plate of plywood composite that consists of top and bottom plywood flanges and a core of plywood ribs. The objective function is structure's weight. Typical constrains – maximal stress criteria and maximal deformation criteria – are used. The optimization is done by Genetic Algorithm (GA), and optimization results are used to train Feed-Forward Artificial Neural Network. The numerical simulation of plywood structure is done by using classical linear Kirchoff–Love theory of multilayer plate and Finite Element Method. As a result, an effective optimization methodology for plywood composite material is proposed. The most rational (according to strength-stiffness criteria) plywood composite macrostructure is obtained for some typical cases.

Keywords

• plywood composite
• structural optimization
• Genetic Algorithm
• Artificial Neural Network