Multiple Analytical Models to Evaluate the Impact of Carbon Nanotubes on the Electrical Resistivity and Compressive Strength of the Cement Paste

Abstract

Cement paste is the most common construction material being used in the construction industry. Nanomaterials are the hottest topic worldwide, which affect the mechanical properties of construction materials such as cement paste. Cement pastes containing carbon nanotubes (CNTs) are piezoresistive intelligent materials. The electrical resistivity of cementitious composites varies with the stress conditions under static and dynamic loads as carbon nanotubes are added to the cement paste. In cement paste, electrical resistivity is one of the most critical criteria for structural health control. Therefore, it is essential to develop a reliable mathematical model for predicting electrical resistivity. In this study, four different models—including the nonlinear regression model (NLR), linear regression model (LR), multilinear regression model (MLR), and artificial neural network model (ANN)—were proposed to predict the electrical resistivity of cement paste modified with carbon nanotube. Furthermore, the correlation between the compressive strength of cement paste and the electrical resistivity model has also been proposed in this study and compared with models in the literature. In this respect, 116 data points were gathered and examined to develop the models, and 56 data points were collected for the proposed correlation model. Most critical parameters influencing the electrical resistivity of cement paste were considered during the modeling process—i.e., water to cement ratio ranged from 0.2 to 0.485, carbon nanotube percentage varied from 0 to 1.5%, and curing time ranged from 1 to 180 days. The electrical resistivity of cement paste with a very large number ranging from 0.798–1252.23 Ω.m was reported in this study. Furthermore, various statistical assessments such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and OBJ were used to investigate the performance of different models. Based on statistical assessments—such as SI, OBJ, and R2—the output results concluded that the artificial neural network ANN model performed better at predicting electrical resistivity for cement paste than the LR, NLR, and MLR models. In addition, the proposed correlation model gives better performance based on R2, RMSE, MAE, and SI for predicting compressive strength as a function of electrical resistivity compared to the models proposed in the literature.

Rheological, Mechanical, Microstructural and Radiation Shielding Properties of Cement Pastes Containing Magnetite (Fe3O4) Nanoparticles

“…To date, Fe 3 O 4 (nanomagnetite) has been found to be the most versatile in concrete applications (Horszczaruk 2019;Mansouri et al 2019). This is because it can be used to facilitate the formation of concrete with various properties, including self-sensing (Piro et al 2021) and self-healing (Seifan et al 2018;Shaheen et al 2019). Moreover, due to its highly magnetic properties, when added to cement-based composites, nano-Fe 3 O 4 can be used as an electromagnetic wave absorber (He et al 2018;Lesbayev et al 2017), or as a rheology controlling agent under a magnetic field (Jiao et al, 2020(Jiao et al, , 2021.…”

A Review of the Combined Effect of Fibers and Nano Materials on the Technical Performance of Mortar and Concrete

“…Nano concrete, on the other hand, is concrete made with nanomaterials or containing nanomaterials with a particle size of fewer than 500 nanometers [14][15][16]. In general, using nanoparticles (most of them) in cementitious mixes improves the fresh [17,18] and mechanical properties [16,[19][20][21][22][23][24] due to densifying the mix, durability [11,20,[25][26][27] by a significantly increase in the hydration rate [17] and electrical resistivity [28].…”