Predicting the impact of multiwalled carbon nanotubes on the cement hydration products and durability of cementitious matrix using artificial neural network modeling technique.
In this study the feasibility of using the artificial neural networks modeling in predicting the effect of MWCNT on amount of cement hydration products and improving the quality of cement hydration products microstructures of cement paste was investigated. To determine the amount of cement hydration products thermogravimetric analysis was used. Two critical parameters of TGA test are PHP(loss) and CH(loss). In order to model the TGA test results, the ANN modeling was performed on these parameters separately. In this study, 60% of data are used for model calibration and the remaining 40% are used for model verification. Based on the highest efficiency coefficient and the lowest root mean square error, the best ANN model was chosen. The results of TGA test implied that the cement hydration is enhanced in the presence of the optimum percentage (0.3 wt%) of MWCNT. Moreover, since the efficiency coefficient of the modeling results of CH and PHP loss in both the calibration and verification stages was more than 0.96, it was concluded that the ANN could be used as an accurate tool for modeling the TGA results. Another finding of this study was that the ANN prediction in higher ages was more precise.
Civil Engineering and Environment Faculty, Tarbiat Modares University, Tehran 111-14115, Iran.
Babak
B
Fakhim
Civil Engineering and Environment Faculty, Tarbiat Modares University, Tehran 111-14115, Iran.
Abolfazl
A
Hassani
Nanotechnology Research Center, Research Institute of Petroleum Industry, Tehran, Iran.
Alimorad
A
Rashidi
Civil Engineering Department, Iran University of Science and Technology, Tehran, Iran.
Parviz
P
Ghodousi
D037742:Nanotubes, Carbon / Q000737:chemistry*; D016571:Neural Networks, Computer
D037742:Nanotubes, Carbon