Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions

Abstract

Concrete as a building material is classified as either normal or high strength based on its compressive strength. The compressive strength of conventional concrete ranges somewhere around 20–40 MPa. The incorporation of high-performance nanomaterials, such as carbon nanotubes (CNT), into the concrete mix, is gaining popularity to produce multifunctional composite materials with improved mechanical, physical, and electrical properties. However, the compressive strength of normal concrete (NC) increased with the addition of CNT to the mix design. Therefore, a reliable mathematical model is required to estimate the amount of CNT to gain the necessary compressive strength. In this research, five different models were proposed to forecast the compressive strength of conventional concrete modified with carbon nanotubes, including the artificial neural network model (ANN), M5P tree model, nonlinear regression model (NLR), multilinear regression model (MLR), and linear regression model (LR). For this purpose, 282 data were collected from the literature review to examine and develop the models. During the model development, the most powerful parameters influencing concrete's compressive strength were found, i.e., curing time ranged from 1 to 180 days, cement varied between 250 and 475 kg/m3, water to binder ratio ranged from 0.4–0.87, coarse aggregate 498–1466.8 kg/m3, fine aggregate 175.5–1285 kg/m3, and carbon nanotube varied between (0–10%). Based on statistical assessment parameters such as coefficient of determination R2, mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and objective (OBJ), the ANN model execute better performance in predicting the compressive strength of NC modified with CNT.

Keywords

• Conventional concrete
• Carbon nanotube
• Statistical analysis
• Modeling
• Sensitivity