Artificial neural network applications in fiber reinforced concrete

Abstract

The presence of fiber in the concrete increases the mechanical properties of the concrete considerably. This paper presents the application of Artificial neural network to predict the compressive and impact strength of the concrete with varying percentage of glass fiber along with different combination of chemical admixtures in concrete such as super plasticiser, air entraining agent, accelerators, retarder and water proofing agent. In experimental part of research, the specimens were tested to failure in order to measure the compressive strength and impact strength by drop weight method. The compressive strength and impact strength of the Glass Fiber Reinforced Concrete (GFRC) with different combination of admixtures get increased to 12% and 90% respectively as compared with control specimen. The predicted strength was compared with the experimentally obtained compressive and impact strength of glass fiber reinforced concrete. The strength predicted by ANN is very close to the experimental results with minimal error.