Behavioral Prediction of Reactive Powder Concrete Based on Artificial Neural Network

Tao JI; Yi Zhou Zhuang; Bao Chun Chen; Zhi Bin Huang

doi:10.4028/www.scientific.net/AMR.168-170.1030

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Behavioral Prediction of Reactive Powder Concrete Based on Artificial Neural Network

Abstract:

Based on orthogonal array testing strategy (OATS), the effects of sand-binder ratio (S/B), water-binder ratio (W/B), and the ratio of steel fiber volume to reactive powder concrete (RPC) volume (STF/R) on the compressive strength and chloride diffusion coefficient of RPC were investigated using an artificial neural network method. Research results reveal that the compressive strength of RPC approaches summit when STF/R is 2% or W/B is 0.18-0.2%, and decreases with the increasing of S/B. Furthermore, the chloride diffusion coefficient increases with W/B or STF/R and decreases with S/B.