TY - JOUR
ID - 514446
TI - Optimization of Functionally Graded Beams Resting on Elastic Foundations
JO - Journal of Solid Mechanics
JA - JSM
LA - en
SN - 2008-3505
AU - Yas, M.H
AU - Kamarian, S
AU - Jam, J.E
AU - Pourasghar, A
AD - Department of Mechanical Engineering, Razi University, Kermanshah
AD - Center for Composite Materials & Structures, MUT, Tehran
Y1 - 2011
PY - 2011
VL - 3
IS - 4
SP - 365
EP - 378
KW - Functionally graded beam
KW - Elastic foundations
KW - Optimization
KW - Genetic Algorithm
KW - Artificial Neural Network
DO -
N2 - In this study, two goals are followed. First, by means of the Generalized Differential Quadrature (GDQ) method, parametric analysis on the vibration characteristics of three-parameter Functionally Graded (FG) beams on variable elastic foundations is studied. These parameters include (a) three parameters of power-law distribution, (b) variable Winkler foundation modulus, (c) two-parameter elastic foundation modulus. Then, volume fraction optimization of FG beam with respect to the fundamental frequency is studied. Since the optimization process is so complicated and time consuming, Genetic Algorithm (GA), a computational algorithm based on Darwinian theories that allow to solve optimization problems without using gradient-based information on the objective functions and the constraints, is performed to obtain the best material profile through the thickness to maximize the first natural frequency. A proper Artificial Neural Network (ANN) is trained by training data sets obtained from GDQ method and then is applied as the objective function in genetic algorithm by reproducing the fundamental frequency for improving the speed of the optimization process. Finally, the optimized material profile for the maximum natural frequency of a FG beam resting on elastic foundations is presented.
UR - http://jsm.iau-arak.ac.ir/article_514446.html
L1 - http://jsm.iau-arak.ac.ir/article_514446_3e7767fa8826b9ce56b2c9d2327f77ca.pdf
ER -