Artificial intelligence method for predicting mechanical properties of sand/glass reinforced polymer: a new model

Document Type : Research Paper

Authors

1 Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, 67156-85420, Iran

2 Department of Computer Engineering, Kermanshah University of Technology, Kermanshah, 67156-85420, Iran

10.22075/macs.2020.20699.1268

Abstract

In this paper, the aim is to propose a new model to obtain the mechanical properties of sand/glass polymeric concrete including modulus of elasticity and the ultimate tensile stress. The active learning method (ALM) and neural network soft computation modeling are used to construct a simple and reliable model based on experimental datasets. The experimental data are obtained via the tensile and bending tests of sand/glass reinforced polymer with different weight percentages of sand and chopped glass fibers. The extracted results are then used for training and testing of the neural network models. Three different types of neural networks including feed-forward neural network (FFNN), radial basis neural network (RBNN) and generalized regression neural network (GRNN) are employed for connecting the properties of the sand/glass reinforced polymer to the properties of the resin and weight percentages of sand and glass fiber. Besides the neural network models, an ALM model is also applied to the problem which is a fuzzy regression algorithm. The models are compared with each other with respect to the statistical indices for both train and test datasets. Finally, to obtain the properties of the sand/glass reinforced polymer, the most accurate model is presented as an FFNN model.

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