Viscosity, yield stress and compressive strength of cement-based grout modified with polymers
01 Dec 2019-Vol. 4, pp 100043
TL;DR: In this paper, two types of polycarboxylate (PCE) polymer (DBC-21 and VK-98) were used as additives in the cement-based grout.
Abstract: In this study, two types of polycarboxylate (PCE) polymer (DBC-21 and VK-98) were used as additives in the cement-based grout. The water-cement ratio (w/c) was fixed to 0.6 and 1.0 at temperature of 25° Celsius and 50° Celsius. Experimentalists were conducted to study the chemical composition of the cement-based grout, the mass loss, the rheology behavior, and the compressive strength. Numerical studies were performed to understand the shear strength, rheological properties and compressive strength by taking advantages of numerical models. The results show that the 0.16% PCE polymer additive leads to low cement weight loss at 800° Celsius, drastic increase of apparent and plastic viscosity, and significant improvement of compressive strength. Effects of polymer content, w/c, curing period and the temperature on the rheological properties and compressive strength (CS) of cement-based grout were investigated using a multiple nonlinear regression analysis.
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TL;DR: In this paper, the compressive strength of concrete mixtures with high volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design).
Abstract: Advances in technology and environmental issues allow the building industry to use ever more high-performance engineered materials. In this study, the hardness of concrete mixtures with high-volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design). High-performance building materials may have greater strength, ductility, external factor resistance, more environmentally sustainable construction, and lower cost than conventional building materials. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of concrete mixes containing a high volume of fly ash (HVFA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested HVFA concrete mixes) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear, Nonlinear Regressions, Multi-logistic Regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of concrete, i.e. fly ash (class C and F) incorporation ratio (0–80% of cement's mass), water-to-binder ratio (0.27–0.58), and gravel, sand, cement contents and curing ages (3–365 days). According to the correlation coefficient (R) and the root mean square error, the compressive strength of HVFA concrete can be well predicted in terms of w/b, fly ash, cement, sand, and gravel densities, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the ANN, M5P-tree, and Non-linear regression models seem to be the most reliable models. The results of this study suggest that the M5Ptree-based model is performing better than other applied models using training and testing datasets. The maximum and minimum percentage of error between the actual test results and the outcome of the prediction using MLR, LR, M5P-tree, and ANN were 0.03–43%, 0.03–54%, 0.04–33%, and 0.03–41% respectively. Based on the outcomes from the models and statistical assessments such as coefficient of determination (R2), mean absolute error (MAE) and the root mean square error (RMSE), the models M5P-tree, ANN, and MLR respectively were predicted the compressive strength of the HVFA concrete very well with a high value of R2 and low values of MAE and RMSE based on the comparison with experimental data. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of HVFA concrete with this data set.
69 citations
TL;DR: In this paper, the compressive strength of concrete bricks with fly ash incorporation ratio (C and F) and water-to-binder ratio (0.235-1.2), and curing ages (1-365 days) is predicted using a multiscale model.
Abstract: This study aims to establish systematic multiscale models to predict the compressive strength of cement mortar containing a high volume of fly ash (FA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested cement mortar modified with FA) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear and Nonlinear regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of cement mortar, i.e. fly ash (class C and F) incorporation ratio (0−70% of cement's mass), water-to-binder ratio (0.235–1.2), and curing ages (1–365 days). According to the correlation coefficient (R), mean absolute error and the root mean a square error, the compressive strength of cement mortar can be well predicted in terms of w/b, fly ash, and curing time using various simulation techniques. The results of this study suggest that the Non-linear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of cement mortar with this data set.
56 citations
TL;DR: In this paper, the effect of nano-silica (NS) as an additive to the Ordinary Portland Cement was evaluated and quantified using non-linear regression (NLR) based model.
46 citations
TL;DR: In this article, the effect of nano-silica (NS) as an additive to Ordinary Portland Cement was evaluated and quantified using a non-linear regression (NLR) based model.
Abstract: In this study, the effect of nano-silica (NS) as an additive to Ordinary Portland Cement was evaluated and quantified. Scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy analysis was used to identify the cement and NS contents. Experimental tests and modeling were conducted to quantify and predict the rheological properties of the cement in the liquid phase such as yield stress, maximum shear strength, plastic viscosity, and mechanical behavior such as compressive strength of cement after hardening. The cement modified with NS was tested at water-to-cement ratios (w/c) of 0.35 and 0.45 and temperatures ranging from 25 to 75 °C. X-ray diffraction (XRD) and TGA were used to analyze the cement, nano-silica, and cement modified with nano-silica. The behavior of cement paste in the liquid phase (slurry) and hardened phase modified with different percentages of nano-silica up to 1% (by dry weight of cement) was investigated. The compressive strength of cement paste modified with nano-silica was tested from a young age up to 28 days of curing. Non-linear regression (NLR) based model was used to assess the effect of nano-silica on the rheological properties and compressive strength of cement. Replacing the cement with nano-silica substantially reduced the volume of Ca(OH)2. TGA tests showed that the 1% nano-silica additive leads to low cement weight loss up to 800 °C due to the de-carbonation of CaCO3 in the hydrated compound and due to interacting the NS with the cement. The addition of NS increased the ultimate shear strength (τmax) and the yield stress (τo) by 15% to 53% and 23% to 186%, respectively based on the NS content, w/c, and temperature. An additional 1% of NS the compressive strength increased of the cement hardened by 15.1% to 72% based on the curing period, and w/c. Based on the model parameters and the experimental performance, the nano-silica is the most effective parameter in improving the properties of cement in both liquid and hardened phases.
36 citations
TL;DR: In this article, the effect of nano-calcium carbonate (nano-CaCO3) as an additive to the cement paste was evaluated and quantified, and 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 mixture modified with nano-caCO3.
34 citations
References
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708 citations
TL;DR: In this article, the reinforcing effect of highly dispersed multiwall carbon nanotubes (MWCNTs) in cement paste matrix has been investigated, and the MWCNTs were effectively dispersed in the mixing water by using a simple, one-step method utilizing ultrasonic energy and a commercially available surfactant.
Abstract: Due to their exceptional mechanical properties, carbon nanotubes (CNTs) are considered to be one of the most promising reinforcing materials for the next generation of high-performance nanocomposites. In this study, the reinforcing effect of highly dispersed multiwall carbon nanotubes (MWCNTs) in cement paste matrix has been investigated. The MWCNTs were effectively dispersed in the mixing water by using a simple, one step method utilizing ultrasonic energy and a commercially available surfactant. A detailed study on the effects of MWCNTs concentration and aspect ratio was conducted. The excellent reinforcing capabilities of the MWCNTs are demonstrated by the enhanced fracture resistance properties of the cementitious matrix. Additionally, nanoindentation results suggest that the use of MWCNTs can increase the amount of high stiffness C–S–H and decrease the porosity. Besides the benefits of the reinforcing effect, autogenous shrinkage test results indicate that MWCNTs can also have a beneficial effect on the early strain capacity of the cementitious matrix, improving this way the early age and long term durability of the cementitious nanocomposites.
550 citations
TL;DR: In this paper, the impact on hydration of several classes of chemicals is reviewed with an emphasis on the current understanding of interactions with cement chemistry, including setting retarders, accelerators, and water reducing dispersants.
500 citations
TL;DR: In this paper, the zeta potential of early hydration products of cement was found to be a key factor for superplasticizer adsorption, and it was shown that a hydrating cement grain is best represented by a mosaic structure, with super-plasticizers molecules mainly adsorbed on ettringite and some on monosulfate and C-S-H nucleated at surface.
468 citations
TL;DR: In this paper, the effect of limestone filler on the degree of hydration, the volume of hydrating products, and the optimal replacement of limestone filling in cement pastes at different w/cm ratios (0.25-0.50) were investigated by using a quadratic statistical model.
393 citations