Homogenization Model Examining the Effect of Nanosilica on Concrete Strength and Stiffness

Kim, Jung J.; Fan, Tai; Taha, Mahmoud Reda

doi:10.3141/2141-06

Cited by 20 publications

(7 citation statements)

References 22 publications

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“…Using either tip, loading and unloading is used to extract the stiffness of the cement paste nanocomposite (spherical indenter) or its individual microstructural phases (Berkovich indenter). Previous experiments on hydrated cement paste showed that an indentation load in the range of 0.5-1.0 mN will produce an indentation depth in the range of 200-300 nm for a typical cement matrix (Kim et al 2010). Analysis of the nanoindentation data to extract elastic modulus can be performed following the Oliver and Pharr method (Oliver and Pharr 1992).…”

Section: Nanoindentationmentioning

confidence: 98%

“…The Berkovich indenter was successfully used for extracting the mechanical characteristics of individual microstructural phases in the cement paste ). On the other hand, the spherical indenter can be used to extract the stress-strain constitutive model of the cement nanocomposite (Kim et al 2010). Using either tip, loading and unloading is used to extract the stiffness of the cement paste nanocomposite (spherical indenter) or its individual microstructural phases (Berkovich indenter).…”

Section: Nanoindentationmentioning

confidence: 99%

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Examining microstructural composition of hardened cement paste cured under high temperature and pressure using nanoindentation and 29Si MAS NMR

2012

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Microstructural composition of the hardened cement pastes are analyzed using nanoindentation and 29 Si MAS NMR after curing time periods of 7 and 28 days. Two curing conditions, room condition (20°C with 0.1 MPa pressure) and an elevated condition (80°C with 10 MPa pressure) are prepared to hydrate cement pastes [water to cement (w/c) ratio of 0.45]. The degree of hydration of the cement paste quantified using nanoindentation was compared with that from 29 Si MAS NMR. From nanoindentation of the hardened cement pastes, microstructural hydration products are characterized with respect to the corresponding modulus of elasticity. A hydration product, which has a relatively high modulus of elasticity over other known hydration products, was found in the hardened cement paste cured in elevated temperature and pressure. The effect of high pressure on the composition of the hydration product is discussed and it is hypothesized that the packing density of calcium-silicatehydrate (C-S-H) might increase when a cement paste is hydrated under high temperature and pressure.

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Section: Nanoindentationmentioning

confidence: 98%

Section: Nanoindentationmentioning

confidence: 99%

Examining microstructural composition of hardened cement paste cured under high temperature and pressure using nanoindentation and 29Si MAS NMR

2012

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“…However, LD-C-S-H keeps decreasing by replacing more cement with nanosilica. This observation indicates that there is a threshold replacing contents of cements with nanosilica to enhance durability of concrete [26,27]. The effects of nanosilica contents on the cement hydration and pozzolanic activity were examined using XRDA experiments.…”

Section: Resultsmentioning

confidence: 99%

Investigation of Cement Matrix Compositions of Nanosilica Blended Concrete

Kim¹,

Moon²,

Youm³

et al. 2014

International Journal of Railway

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The use of pozzolanic materials in concrete mixtures can enhance the mechanical properties and durability of concrete. By reactions with pozzolanic materials and calcium hydroxide in cement matrix, calcium-silicate-hydrate (C-S-H) increases and calcium hydroxide decreases in cement matrix of concrete. Consequently, the volume of solid materials increases. The pozzolanic particles also fill spaces between clinker grains, thereby resulting in a denser cement matrix and interfacial transition zone between cement matrix and aggregates; this lowers the permeability and increases the compressive strength of concrete. Moreover, the total contents of alkali in concrete are reduced by replacing cements with pozzolanic materials; this prevents cracks due to alkali-aggregate reaction (AAR). In this study, nanosilica is incorporated in cement pastes. The differences of microstructural compositions between the hydrated cements with and without nanosilica are examined using nanoindentation, XRDA and 29 Si MAS NMR. The results can be used for a basic research to enhance durability of concrete slab tracks and concrete railway sleepers.

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“…Analytical and numerical models, commonly based on homogenization, are increasingly being used to predict the mechanical properties (compressive/tensile strength, elastic modulus) of concrete [12,13]. In such models, detailed information about the microstructure, spatial distribution, and mechanical properties of individual phases are required [14,15].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance

Wan

Šavija

2021

Materials

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Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine learning (ML) methods have shown promising results for predicting compressive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of different ML models for this application. In this work, four representative ML models, i.e., Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained and used to predict the compressive strength of concrete based on its mixture composition and curing age. For each ML model, three kinds of features are used as input: the eight original features, six Principal Component Analysis (PCA)-selected features, and six manually selected features. The performance as well as the training speed of those four ML models with three different kinds of features is assessed and compared. Based on the obtained results, it is possible to make a relatively accurate prediction of concrete compressive strength using SVR, XGBoost, and ANN with an R-square of over 0.9. When using different features, the highest R-square of the test set occurs in the XGBoost model with manually selected features as inputs (R-square = 0.9339). The prediction accuracy of the SVR model with manually selected features (R-square = 0.9080) or PCA-selected features (R-square = 0.9134) is better than the model with original features (R-square = 0.9003) without dramatic running time change, indicating that dimensionality reduction has a positive influence on SVR model. For XGBoost, the model with PCA-selected features shows poorer performance (R-square = 0.8787) than XGBoost model with original features or manually selected features. A possible reason for this is that the PCA-selected features are not as distinguishable as the manually selected features in this study. In addition, the running time of XGBoost model with PCA-selected features is longer than XGBoost model with original features or manually selected features. In other words, dimensionality reduction by PCA seems to have an adverse effect both on the performance and the running time of XGBoost model. Dimensionality reduction has an adverse effect on the performance of LR model and ANN model because the R-squares on test set of those two models with manually selected features or PCA-selected features are lower than models with original features. Although the running time of ANN is much longer than the other three ML models (less than 1s) in three scenarios, dimensionality reduction has an obviously positive influence on running time without losing much prediction accuracy for ANN model.

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Homogenization Model Examining the Effect of Nanosilica on Concrete Strength and Stiffness

Cited by 20 publications

References 22 publications

Examining microstructural composition of hardened cement paste cured under high temperature and pressure using nanoindentation and 29Si MAS NMR

Examining microstructural composition of hardened cement paste cured under high temperature and pressure using nanoindentation and 29Si MAS NMR

Investigation of Cement Matrix Compositions of Nanosilica Blended Concrete

On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance

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