Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies
2.1. Experimental Program
2.1.2. Mix Proportions
2.1.3. Production of Specimens
2.1.4. Test Procedures
2.2. Prediction Method
2.2.1. Application of ANFIS to Predict Concrete Properties
2.2.2. Pegasus (Primal Estimated Sub-Gradient Solver for SVM)
Using Mini-Batch Iterations to Implement Pegasos
3. Results and Discussion
3.1. Compressive Strength
- Filling property: NC can act as a filler to improve the density of mortar resulting in a significant reduction of porosity. Figure 8 shows the SEM micrographs of MK10 and MK10NC3 samples. As shown in Figure 8b, the microstructure of cement matrix containing NC was more compact and the porosity was significantly reduced. The SEM results confirmed that NC, having the filling ability property, can fill the porosity in cement paste and make a denser cement matrix.
- Acting as a nucleus: In the structure of the C-S-H gel, the nanoparticles can act like a nucleus forming an extremely strong bond with C-S-H gel particles . Thus, when nanomaterials are uniformly dispersed in cement, they can promote the cement hydration due to their high reactivity, results in improvement of mechanical properties and durability of mortars.
- Crystal-formation control: If the amount of nanoparticles and their spacing are appropriate, the formation process of Ca(OH)2 crystals in the transition area can be reduced . Therefore, with increase of NC up to 3%, the compressive strength raised except for the samples containing 30% MK. It can be stated that NC can lead to a denser structure with less porosity when an appropriate amount is added.
3.2. Flexural Strength
3.3. Water Absorption
4. Accuracy of Predicted Methods
- Using ANFIS can get estimated results that are closer to the experimental results than Pegasos model.
- Using Pegasos model as an algorithm can obtain upper and lower bounds for each predicted data, while using ANFIS can only lead to one mean value, so user does not have any tolerance to report the data.
- Using ANFIS model requires obtaining the function, number of epoch and hidden layers with trial and error processes, while trial and error is not used in Pegasos algorithm.
- The speed of prediction process decreases with increasing number of data and input layers in ANFIS method and sometimes more time is required to run model when the hidden layers increase, however it is not an issue in Pegasos algorithm.
- The compressive and flexural strengths decreased with increasing MK content at both 28 and 90 days.
- Using of 0.3% PP fibres improved the compressive strength slightly. The average compressive strength for all samples increased by 2% at 28 and 90 days which is negligible.
- Compared to the CO sample, the incorporation of 3% NC increased the strength of samples containing 10% MK up to 17% and 19% at 28 and 90 days, respectively.
- Significant improvement in flexural strength was seen when PP fibres were used. Compared to the samples without PP, the samples with PP indicated an average increase of flexural strength by 12.7% and 17.4%, at 28 and 90 days, respectively.
- Comparing with other samples tested, mortars containing 3% NC and 10% MK were considered as the most suitable mixtures for mechanical properties.
- It was observed that the water absorption of mortar samples decreased with the increase of MK content up to 10%. However, the addition of more MK (i.e., 20% and 30%) did not have remarkable impact on the water absorption.
- The addition of PP improved water absorption. The water absorption results showed that an addition of 0.3% PP fibres reduced the water absorption of mortar compared to the samples without PP.
- The water absorption results decreased with increasing the contents of nanoparticles and MK.
- SEM images illustrated that the morphology of cement matrix became more porous with increasing MK content, but the porosity reduced with the inclusion of NC. In addition, there were more cement hydration products adhered around the fibres, accompanied with a more compact microstructure due to the filling ability of nanoparticles. This could improve the fibre–matrix interface, and thus enhance the load transfer between the cement matrix and fibres, leading an improvement in flexural strength of mortar.
- Based on the statistical values of MAPE, RRMSE and R2, the ANFIS model showed the best prediction accuracy and can be used to predict the properties of fibre reinforced cement mortar accurately.
Conflicts of Interest
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|Chemical Analysis (%)||Cement||MK|
|Surface area (BET) (m2/g)||0.31||2.54|
|Nanoparticles||Average Diameter (nm)||Specific Surface Area (m2/g)||Purity (%)|
|nano-CuO||20 ± 3||200||>99|
|Unit weight (g/cm3)||0.9–0.91|
|Reaction with water||Hydrophobic|
|Tensile strength (MPa)||300–400|
|Elongation at break (%)||100–600|
|Melting point (°C)||175|
|Thermal conductivity (W/m/K)||0.12|
|Sample ID||Cement (kg/m3)||MK (kg/m3)||NC (kg/m3)||PP (kg/m3)||Water (kg/m3)||Sand (kg/m3)||SP (kg/m3)|
|ANFIS||R2 Values for: Training Set, Testing Set, and Validation Set|
|Data Set||Training Set||Testing Set||Validation Set|
|RRMSE||2.96 × 10−6||9.48 × 10−4||4.8540 × 10−4|
|RRMSE||6.98 × 10−3||0.016||0.0068|
|RRMSE||1.18 × 10−5||0.0023||0.0025|
|ANFIS||The Relationship Between Predicted Values (y) and Experimental Data (x)|
|Data Set||Training Set||Testing Set||Validation Set|
|Compressive strength||y = 0.99x + 0.01||y = 0.99x + 0.23||y = 0.96x + 1.3|
|Flexural strength||y = 0.99x + 0.002||y = 1.04x − 0.37||y = 0.99x + 0.01|
|Water absorption||y = 0.99x + 0.02||y = 1.009x + 0.07||y = 1.17x − 1.43|
|Pegasos||R2 Values for: Training Set, Testing Set, and Validation Set|
|Data Set||Training Set||Testing Set|
|RRMSE||2.91 × 10−4||0.00052|
|RRMSE||2.96 × 10−6||2.85 × 10−3|
|Pegasos||The Relationship between Predicted Values (y) and Experimental Data (x)|
|Data Set||Training Set||Testing Set|
|Compressive strength||y = 0.91x + 4.23||y = 0.78x + 10.41|
|Flexural strength||y = 0.97x + 0.12||y = 0.88x + 1.03|
|Water absorption||y = 0.8x + 1.58||y = 0.92x + 0.47|
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Ghanei, A.; Jafari, F.; Khotbehsara, M.M.; Mohseni, E.; Tang, W.; Cui, H. Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies. Materials 2017, 10, 1215. https://doi.org/10.3390/ma10101215
Ghanei A, Jafari F, Khotbehsara MM, Mohseni E, Tang W, Cui H. Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies. Materials. 2017; 10(10):1215. https://doi.org/10.3390/ma10101215Chicago/Turabian Style
Ghanei, Amir, Faezeh Jafari, Mojdeh Mehrinejad Khotbehsara, Ehsan Mohseni, Waiching Tang, and Hongzhi Cui. 2017. "Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies" Materials 10, no. 10: 1215. https://doi.org/10.3390/ma10101215