Prediction Containing the Micro Silica and Fly ash on Concrete Strength Using Artificial Neural Network (ANN)

Nowadays, intelligent methods inspired from nature are implemented to resolve complex problems, there are very popular too. The most common one is artificial neural network; they are capable to collect huge amount of complex information through experiments and tests. With increasing population and a rise in construction and also due to limited resources and consumable materials, demand for hot rolled earthquake-resistant materials in the construction industry has increased. The purpose of this research, by considering concrete mix design parameters as input, the Static neural network and Time-series modeling to predict the compressive strength of concrete will be used. Mixing fly ash and silica fume various designs with different percentages (1%, 5%, 7%, 10%, 12%, 15%, 18%) and mixed with silica fume, fly ashes identical percentages (% 1% 1% 3 and 3%, 5% and 5%, 7% and 7%, 9% and 9%, 10% and 10%) as a percentage of the weight of cement, to evaluate the performance of the models in question were applied. It turned out that neural network models for predicting time series with 5 neurons performance concrete compressive strength is accurate and reliable.