Characterization and modeling the flow behavior and compression strength of the cement paste modified with silica nano-size at different temperature conditions
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.
About: This article is published in Construction and Building Materials.The article was published on 2020-10-10. It has received 46 citations till now. The article focuses on the topics: Portland cement & Compressive strength.
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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 article, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without nano-silica (NS).
Abstract: The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R2) were used to evaluate the performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.
48 citations
TL;DR: In this paper, the effects of nano-SiO2, nano-TiO2 and nano-CaCO3 on the early hydration and mechanical properties of Portland cement at small dosages, and optimizes the dosage of the three nano materials by response surface methodology.
Abstract: Nano materials have become an important means of modifying cement-based materials. The excessive content of nano materials often causes some problems. Therefore, optimizing the content of nano materials in Portland cement paste appear to be particularly important. This paper mainly studies the effects of nano-SiO2 (NS), nano-TiO2 (NT) and nano-CaCO3 (NC) on the early hydration and mechanical properties of Portland cement at small dosages, and optimizes the dosage of the three nano materials by response surface methodology (RSM). The results show that the addition of nano materials can greatly promote the hydration and improve the early compressive strength of Portland cement. When the addition amount is small, the three nano materials have the best effect when added together. Compared with the blank sample, the compressive strength of 1 day, 3 days and 7 days are increased by 52.54%, 53.76% and 61.09%, respectively. In addition, adding NT, NC and NS together to Portland cement can produce more hydration products, increase the rate of hydration, optimize pore structure and reduce the total porosity from 23.2% to 16.8%. Moreover, with three nano materials adding together, the response surface can predict that when the content of NS, NT and NC are 0.86 wt%, 2.75 wt% and 0.14 wt%, respectively, it will have the best hydration promotion effect of Portland cement and it's compressive strength can reach 83 MPa at 7 days.
42 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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TL;DR: In this paper, the influence of nano-SiO 2 (NS) addition on properties of hardened cement paste (hcp) as compared with silica fume (SF) has been studied through measurement of compressive and bond strengths of hcp, and by XRD and SEM analysis.
1,039 citations
TL;DR: In this article, the effect of incorporating nanomaterials in low dosages to the fabrication, workability, hydration, microstructure, and mechanical properties of cement-based composites are comprehensively reviewed.
512 citations
25 May 2006-Materials Science and Engineering A-structural Materials Properties Microstructure and Processing
TL;DR: In this paper, a liquid-form of nanosilica particle with a spherical diameter of about 20 nm was incorporated into the Portland cement paste at five different dosages and analyzed at four different ages to identify the nanosizing effects on the microstructures and material properties of composite cement paste.
Abstract: Both the filling effect and the pozzolanic reaction make siliceous materials as one of major ingredients of high-performance Portland cement-based composites. Hence, the introduction of nanosilica with finer particle size and larger silicon dioxide to the composite becomes a great deal of interest in recent years. In this study, a liquid-form of nanosilica particle with a spherical diameter of about 20 nm was incorporated into the Portland cement paste at five different dosages and analyzed at four different ages to identify the nanosizing effects on the microstructures and material properties of composite cement paste. Experimental results show that the Portland cement composite with 0.60% of added nanosilica by weight of cement has an optimum compressive strength, in which the increase of compressive strength is about 43.8%. Moreover, the corresponding nanosilica paste of one portion of water mixed with nanosilica of 1.08 wt.% of water has the maximum absolute value of zeta potential of 41.3 mV. Properties through the analyses of NMR, BET and MIP also indicate that the microstructure of Portland cement composite with nanosilica evidently has a more solid, dense and stable bonding framework.
275 citations
TL;DR: In this article, the characteristics of the pozzolanic reactivity of nanoSiO2 from studies of its reaction kinetics, morphology and structure of the hydrates and the influences of these features on the properties of cement-based materials were explored.
Abstract: The aim of this work is to understand the characteristics of the pozzolanic reactivity of nanoSiO2 from studies of its pozzolanic reaction kinetics, morphology and structure of the hydrates and the influences of these features on the properties of cement-based materials, so as to explore a more targeted way of using nanoSiO2 in cement or concrete. It revealed that the pozzolanic reaction of nanoSiO2 is of the first-order and the apparent reaction rate constant of nanoSiO2-4 nm is about one order of magnitude bigger than that of silica fume, but the specific reaction rate constant is about one half to that of silica fume. A compacter gel structure and poorer crystallinity of the hydrates of nanoSiO2 to those of silica fume are found, as well. The rate of hydration of cement at very early ages is enhanced by nanoSiO2, but the rate slows down with aging due to the compact gel structure. To make the use of the high pozzolanic reactivity and ultrafine particle size of nanoSiO2, as well as its resulting compact gel structure, colloidal nanoSiO2 was applied onto the hardened cement mortar by brushing technique and a less permeable surface was resulted, which shows the potential of using nanoSiO2 as a surface treatment material for cement-based materials.
156 citations
TL;DR: A hydration model for Portland cement pastes modified with nano-silica in partial substitution is formulated based on the nucleation growth process from microstructural investigations over time as discussed by the authors.
Abstract: A hydration model for Portland cement pastes modified with nano-silica in partial substitution is formulated based on the nucleation growth process from microstructural investigations over time. The model is calibrated against thermogravimetry, X-ray diffraction and calorimetry data for four different substitution rates from 0 to 12 wt% and is validated by backscattered electron microscopy. Finite element based compressive strength predictions using representative volume element analysis of the nano modified cement pastes agreed with the experimental values. The model predictions indicate that a rate of 8 wt% is the optimum replacement level of cement by nano-silica leading to a high density matrix promoting a maximum mechanical strength.
149 citations