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An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica-Rice Husk Ash Ternary Blended Concrete

Najigivi, A ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1007/s40069-013-0038-z
  3. Publisher: Korea Concrete Institute , 2013
  4. Abstract:
  5. In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of SiO2 particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance
  6. Keywords:
  7. Artificial neural networks ; Permeability ; Ternary blended concrete ; Cements ; Concretes ; MATLAB ; Mechanical permeability ; Neural networks ; Volumetric analysis ; Levenberg-Marquardt ; Nano Silica ; Neural network model ; Permeability properties ; Rice husk ash ; Ternary blended concretes ; Volumetric properties ; Water absorption properties ; Concrete testing
  8. Source: International Journal of Concrete Structures and Materials ; Volume 7, Issue 3 , September , 2013 , Pages 225-238 ; 22341315 (ISSN)
  9. URL: http://link.springer.com/article/10.1007%2Fs40069-013-0038-z