Journal of Building Materials and Structures
Volume 5, Numéro 1, Pages 32-42

Comparative Study And Optimization Of The Mechanical Behavior Of Sandwich Beams Loaded In Three Point Bending

Authors : Fairouz Bourouis. Fayçal Mili.


The sandwich material, present a certain interest in term of rigidity and specific resistance for the aeronautic structure design. The study of this material meets always the choice problem of their constituents (coating and core) in order to find the optimal mechanical characteristic. The aim of our work is to do a comparative study of the mechanical behavior of the sandwich beams charged in three point bending. The structures considered consist of two skins of composite material with unidirectional fibers of six plies glass/ epoxy, carbon/epoxy or Kevlar /epoxy of stacking sequence [0°/90°]3s and [45°/-45°]3s and the core is in Alporas , Corecel, PVC, or Polyurethane foams. The different results obtained from the Matlab code showed that the correct choice of the material of the coatings and of the core that improves the mechanical behavior of the sandwich beam. In order to increase the performance of sandwich beams, in three points bending, an optimization program based on the principles of genetic algorithms has been developed to maximize the tensile strength of face yielding and face wrinkling failure modes. The equations used to evaluate the individuals in population results from the classical laminate theory with transverse shear stresses and discrete variables. Operators of genetic algorithms (selection, crossover and mutation) are applied to the children of a hundred generations. To achieve optimal solutions, the result appeared effective despite all the non deterministic nature of genetic algorithms. But, to achieve maximum effectiveness, it’s important to choose smartly the parameters of genetic algorithms depending on the nature of the problem studied and the mechanical characteristics of the function to be optimized.


Sandwich beam; Three point bending; Face wrinkling; Face yielding; Genetic algorithm

Evolutionist Approach And Mfcc Modeling For Arabic Automatic Recognition

Fadila Maouche.  Mohamed Benmohamed. 
pages 223-234.