RETRACTED: Prediction of Physical and Mechanical Properties of High Strength Concrete Containing CuO Nanoparticles by Artificial Neural Network and Genetic Programming

Abstract

In this article, two models based on artificial neural networks and genetic programming for predicting flexural strength and percentage of water absorption of concretes containing CuO nanoparticles have been developed at different ages of curing. For the purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed-forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC), and number of testings tried (NT). According to these input parameters, in the neural networks and genetic programming models, the flexural strength and percentage of water absorption values of concretes containing CuO nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the flexural strength and percentage of water absorption values of concretes containing CuO nanoparticles. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.