Yasin Ansari - Department of Construction Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
AmirHossein Hashemi - Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Use of neural network approaches in order to estimate mechanical and characteristics of concrete are common, in this regard, after making concrete samples in a laboratory the results of the laboratory are estimated by neural network. A drop impact test is used in order to evaluate impact strength of concrete samples; data obtained from the test usually has high dispersion. Various researches have been conducted to evaluate impact strength of concrete samples but no effort has made yet to predict impact strength of concrete by compressive, flexural strength. In the research, using neural network approach of ANN the impact strength of concrete is predicted from mixture design, compressive and flexural strength. In this regard, a numerical relation and range between compressive, flexural and impact strength have been predicted by collecting laboratory data from previous researches. Results for using neural network to estimate the compressive and flexural strength of concrete has shown that using this tool for estimating compressive and flexural strength of concrete is appropriate because the correlation coefficient between the estimated data and the laboratory data is near to 1.

Neural Network, impact strength, Compressive, Flexural Strength