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Structural Engineering and Mechanics
  Volume 75, Number 5, September10 2020 , pages 633-642

Optimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEM
Emrah Madenci and Şaban Gülcü

    Artificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gâteaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (Et/Eb), a shear correction factor (ks), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.
Key Words
    functionally graded material beam; artificial neural networks; mixed finite element method; displacement data
Emrah Madenci: Department of Civil Engineering, Necmettin Erbakan University, 42140 Konya, Turkey

Şaban Gülcü: Department of Computer Engineering, Necmettin Erbakan University, 42140 Konya, Turkey

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