Behavioral Prediction of Reactive Powder Concrete Based on Artificial Neural Network

2010 ◽  
Vol 168-170 ◽  
pp. 1030-1033
Author(s):  
Tao Ji ◽  
Yi Zhou Zhuang ◽  
Bao Chun Chen ◽  
Zhi Bin Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Diffusion Coefficient ◽  
Chloride Diffusion ◽  
Reactive Powder Concrete ◽  
Chloride Diffusion Coefficient ◽  
Binder Ratio ◽  
Artificial Neural ◽  
Reactive Powder

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.

scholarly journals Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Kraiwut Tuntisukrarom ◽  
Raungrut Cheerarot
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Bottom Ash ◽  
Strength Development ◽  
Ann Model ◽  
Explicit Equation ◽  
Strength Behavior ◽  
Artificial Neural ◽  
Hidden Layer

The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.

Influence of the Nanomaterials of Kaolin Content on the Chloride Permeability of Cement Mortar

2017 ◽  
Vol 730 ◽  
pp. 406-411 ◽  
Author(s):  
Xiao Yu Guo ◽  
Ying Fang Fan ◽  
Kun Yang
Keyword(s):  
Compressive Strength ◽  
Diffusion Coefficient ◽  
Cement Mortar ◽  
Chloride Diffusion ◽  
Chloride Diffusion Coefficient ◽  
Chloride Permeability

This study investigated the influence of nanokaolin content on the behavior of cement mortar at various curing ages. The fluidity, chloride permeability, bending and compressive strength of cement mortar with various nanokaolin additives were examined. The addition of 0%, 1%, 2%, 3%, 4%, 5% and 6% nanokaolin were taken into consideration. The results showed that the addition of nanokaolin decreases the fluidity of cement mortar, and the fluidity the cement mortar decreases with the increase of nanokaolin additives. It is obtained that the addition of nanokaolin increases both the bending and compressive strength of cement mortar, and with the increase of nanokaolin additives, the bending and compressive strength of cement mortar increase. The addition of 4% nanokaolin can result in a significant low chloride permeability of cement mortar among the seven dosages. The chloride diffusion coefficient of the mortar with the addition of 4% nanokaolin was decreased by 18.93%, 12.68% and 31.05% at 7, 14 and 28 curing days, respectively.

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