EVALUATION OF COMPRESSIVE STRENGTH, ULTIMATE LOAD AND DURABILITY CHARACTERISTICS OF HIGH PERFORMANCE CONCRETE BY ARTIFICIAL NEURAL NETWORKS

Neural networks have recently been widely used to model some of the human activities in many areas of Civil engineering applications. In the present project, the models in artificial neural networks (ANN) for predicting compressive strength of cubes, ultimate load of beams, columns and durability of concrete containing metakaolin with fly ash and silica fume with fly ash have been developed at the age of 3, 7, 28, 56 and 90 days. For purpose of building these models, training and testing using the available experimental results for required number of specimens produced with 7 different mixture proportions were used. The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK),fly ash (FA), water, sand, aggregate and super plasticizer and in another set of specimen which contain SF instead of MK. According to these input parameters, in the multilayer feed forward neural networks models are used to predict the compressive strength and ultimate load values of beams and columns concretes. The training and testing results in the neural network models have shown that neural networks have strong potential for predicting 3, 7, 28, 56 and 90 days compressive strength values and ultimate load values of beams and columns of concretes containing metakaolin ,silica fume and fly ash.