Paper Title:
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.

  Info
Periodical
Advanced Materials Research (Volumes 168-170)
Edited by
Lijuan Li
Pages
1030-1033
DOI
10.4028/www.scientific.net/AMR.168-170.1030
Citation
T. Ji, Y. Z. Zhuang, B. C. Chen, Z. B. Huang, "Behavioral Prediction of Reactive Powder Concrete Based on Artificial Neural Network", Advanced Materials Research, Vols. 168-170, pp. 1030-1033, 2011
Online since
December 2010
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Price
$32.00
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