A Neural Network Model for Ultrasonic Pulse Velocity and the Modulus of Elasticity of Polymer Concrete

Abstract

Abstract-In this study, polymer concrete properties such as ultrasonic pulse velocity and the modulus of elasticity were modeled by artificial neural network. Seven polymers were selected as high density polyethylene, low density polyethylene, polypropylene, thermoplastic elastomer, dimethyl terephthalate, polyethylene terephthalate, polyethylene naphthalate and twenty-seven experiments was used to train the network. Two type of network training such as cascade forward back prop and feed forward back prop containing one hidden layer which has ten neurons was compared to predict the ultrasonic pulse velocity and the modulus of elasticity. Moreover, five experiments were used for comparing of the network models. The result show that feed forward back prop type training is more effective to predict the ultrasonic pulse velocity and the modulus of elasticity of polymer concrete comparing the cascade forward back prop training type.Keywords-A Neural Network Model, Polymer Concrete, Ultrasonic Pulse Velocity and the Modulus of Elasticity.