Systemic multi-scale approaches to predict the flowability at various temperature and mechanical properties of cement paste modified with nano-calcium carbonate

Abstract

In this study, the effect of nano-calcium carbonate (nano-CaCO3) as an additive to the cement paste was evaluated and quantified. Microstructure tests, experimental tests, and modeling were conducted to predict the cement paste's rheological properties namely yield stress (yield point), shear stress limit, viscosity, and stress at the failure of cement at fresh and hardened stages. The cement paste modified with nano-CaCO3 was tested at a water-to-cement ratio of 0.35 with 0.45 and temperatures ranging from 25 to 75 ⁰C. The addition of nano-CaCO3 increased the shear stress limit and the yield stress (τo) by 10.28 to 51.35% and 14.36 to 91.4%, based on the nano-CaCO3 content, w/c, and temperature of the test. TGA tests showed that the 1% nano-CaCO3 additive reduces the weight loss of the cement at 800 ⁰C by 76% due to interacting with the nano-CaCO3 with the cement paste. The nonlinear regressions (NLR) model and Artificial Neural Network (ANN) technical approaches were used for the qualifications of the flow of slurry and stress at the failure of the cement paste modified with nano-CaCO3. Based on the statistical assessments of R and RMSE, the rheological properties such as yield stress, plastic viscosity, shear stress limit and compressive strength of cement paste modified with nano-CaCO3 can be well predicted in terms of w/c, nano-CaCO3 content, temperature, and curing time using two different simulation techniques. Based on the statistical assessments such as coefficient of correlation (R), root mean square error (RMSE), the NLR model is the most effective model to estimate rheological properties and compressive strength of the cement and it is performing better than the ANN model.