Predicting the Impact of Multiwalled Carbon Nanotubes on the Cement Hydration Products and Durability of Cementitious Matrix Using Artificial Neural Network Modeling Technique

Babak Fakhim,Abolfazl Hassani,Alimorad Rashidi,Parviz Ghodousi

doi:10.1155/2013/103713

Abstract

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 PHPloss and CHloss. 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.

Highlights

Cementitious materials are the most common construction materials used worldwide
Carbon nanotubes can be represented as a graphene sheet rolled into a cylinder with specific alignment of the hexagonal rings and hemifullerenes attached to the tips [8]
The results of thermogravimetric analysis (TGA) of medium density polyethylene (MDPE)/cloisite Na nanocomposites were predicted by the artificial neural network (ANN)

Summary

Introduction

Cementitious materials (especially concrete) are the most common construction materials used worldwide. MWNTs can be represented as a family of SWNTs of different diameters, which are combined within a single entity in the form of concentric type MWNTs. Carbon nanotubes can be considered as an exceptional reinforcing material due to their extremely high aspect ratios [9], ultra high strength [10], modulus [11], and elasticity [12]. Some researchers have attempted to model the TGA test results on a variety of composites using artificial neural networks. The results of TGA of MDPE/cloisite Na nanocomposites were predicted by the artificial neural network (ANN). A new approach based on artificial neural network (ANN) has been introduced to study the kinetics of thermal decomposition reactions of different polymeric materials, using dynamic thermogravimetry analysis (TGA) at several heating rates. The same kinetic model had been successfully used at different heating rates, with different polymeric materials such as polyethylene, cellulose and lignin [14]

Materials and Methodology

Results and Discussions

Conclusions

Conflict of Interests