# Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies

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## Abstract

**:**

^{2}obtained for each data set (validate, test and train) was higher than 0.90 and the values of mean absolute percentage error (MAPE) and the relative root mean squared error (PRMSE) were near zero. The ANFIS and Pegasos models can be used to predict the mechanical properties and water absorptions of fibre-reinforced mortars with MK and NC.

## 1. Introduction

_{2}[9,10,11], nano-TiO

_{2}[12,13] and other metal-containing nanoparticles [6,14,15,16,17]. There are very limited studies on nano-copper oxides (nano-CuO) and their effects on physical and mechanical properties of mortar and concrete [18,19,20]. Nano-CuO (NC) has been reported as an active nanoparticle with good potential to improving the strength and thermal properties of cementitious materials. Nazari et al. [18] investigated the effect of CuO nanoparticles on compressive strength of self-compacting concrete. Their results indicated that the compressive strength of samples increased with the NC content up to 4%. Khotbehsara et al. [19] conducted an experimental study on durability of self-compacting mortar containing NC and FA. They showed that the best results for electrical resistivity and chloride permeability were observed in the mixture containing 4% NC and 25% fly ash.

_{max}), concrete compressive strength and fineness modulus were input as layers, and the quantities of water, cement, fine and course aggregate were considered as output layers. Comparison of the results between FES and ANFIS systems indicated that ANFIS model was performed better for both training and prediction than FES [25]. Nazari and Riahi [26] used ANFIS to predict the percentage of water absorption of geopolymers made from seeded fly ash and rice husk bark ash. Fly ash, rice husk ash, temperature of curing and age were selected as input layers, and the predicted value of percentage of water absorption for each cementitious sample was considered as output layer. The results showed that ANFIS has the ability to predict the strength values completely similar to the experimental values, and the R

^{2}for training and testing sets were 0.9941 and 0.9929, respectively. Ahmadi [27] used ANFIS and optimal nonlinear regression models to predict elastic modulus of normal and high strength concretes. ANFIS was reported a reliable method to predict concrete strength as there was a good alignment between the experimental data and elastic modulus estimated from the model. Yuan et al. [28] studied the impact of unstructured and structured factors on concrete quality using two hybrid models of genetic based algorithm and ANFIS. The results indicated that both models showed excellent performance in strength prediction. Vakhshouri and Nejadi used ANFIS to predict compressive strength of self-compacting lightweight concrete [29]. In their research, an artificial intelligence was applied as a basic approach to simulate the non-linear and complex behaviour of concrete. The results indicated good predictions using the ANFIS analysis.

## 2. Methodology

#### 2.1. Experimental Program

#### 2.1.1. Materials

^{3}was utilized. The content of superplasticizer was adjusted to keep the same fluidity of samples.

#### 2.1.2. Mix Proportions

#### 2.1.3. Production of Specimens

^{3}cubes for water absorption, compressive and SEM tests, and 50 × 50 × 200 mm

^{3}steel moulds for flexural test. The mortar samples were compacted using a temping rod to exclude the air bubbles from the mortar. The mixtures were demoulded 24 h after casting and then they were cured in water at 23 ± 3 °C until they were tested.

#### 2.1.4. Test Procedures

#### 2.2. Prediction Method

#### 2.2.1. Application of ANFIS to Predict Concrete Properties

^{2}) [36].

^{2}) method is a popular method and is well-known for its capabilities in predicting and modelling materials. The R

^{2}method is currently used to assess concrete properties, such as compressive strength, elastic modulus and water absorption. In this research, a total of eight factors including cement, metakaolin, nano-CuO, water, PP, sand, SP and age were considered as inputs to predict the compressive strength, flexural strength and water absorption of mortar as output layers using ANFIS method with MATLAB. The proposed model were developed and tested against the results derived from 52 strength tests (obtained from compressive and flexural strength tests at 28 and 90 days), and also 26 water absorption tests at 28 day.

^{2}are given in Table 5. For the training, testing and validation data, the best value of R

^{2}was approximately 1 in the ANFIS model. The minimum values of R

^{2}were 90% for checking set. All R

^{2}values showed that the proposed ANFIS models are suitable and can be used to predict output layers such as compressive strength, flexural strength and water absorption of mortar.

#### 2.2.2. Pegasus (Primal Estimated Sub-Gradient Solver for SVM)

_{i}in Equation (5), then, by determination of the value of w, the value of b (output layer) would be estimated.

#### Using Mini-Batch Iterations to Implement Pegasos

_{t}as subset data for each iteration (t). The subset was variable and chosen randomly after changing the iteration. The $\mathrm{f}\text{}\left(\mathrm{w}\right)$ is an objective function in which w was minimized and determined in Equation (4) and f(w;At) was obtained for each k (members) and At (subset) with Equation (5) and expressed as follows:

## 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 [36]. 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 [36]. 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.

^{2}, mean absolute percentage error (MAPE) and the relative root–mean–squared error (RRMSE) were reported for compressive strength at 28 days and 90 days are shown in Table 5. The results of ANFIS and Pegasos models showed that these two models are able to predict the strength values close to the experimental ones. Noteworthy, the prediction values for training set in ANFIS model were more accurate in comparison to the Pegasos model. On the contrary, the values obtained from Pegasos for testing data were closer to the laboratory test results compared to the ANFIS model.

#### 3.2. Flexural Strength

_{2}with the high reactivity of nanoparticles during the hydration process particularly at early ages. Similar results were reported by Mohseni et al. [8] indicating that the flexural strength increased with increasing NC content up to 3%. However, any further increase in NC content showed insignificant strength improvement.

#### 3.3. Water Absorption

## 4. Accuracy of Predicted Methods

^{2}):

_{t}is the number of samples. Smaller values of RRMSE and MAPE, and larger R

^{2}indicate higher prediction accuracy.

^{2}between the test and predicted results for compressive strength, flexural strength and water absorption.

^{2}, RRMSE and MAPE in the best performance were 1, 2.91 × 10

^{−6}and 0.046, respectively, for compressive strength; 1, 0.0068 and 0.013, respectively, for flexural strengths; and 1, 1.18 × 10

^{−5}and 0.004, respectively, for water absorption.

^{2}values are more than 0.93 for both models, thus these two methods are practical and capable of accurately predicting the experimental results. Overlapping predicting values in ANFIS with Pegsos model indicates that predicted values during the training process in ANFIS model is so close to Pegasos model results. These two models have a little error to predict validating and testing set, that is why the gradient of trend ling in two model are different. Furthermore, the gradients of the two trend lines are almost one. Overlapping data in ANFIS and Pegsos models in flexural strength results are more than that for compressive strength. R

^{2}value also increased in flexural strength results in comparison with the compressive strength. Therefore, the predicted results are more accurate.

- 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.

## 5. Conclusions

- 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 R
^{2}, the ANFIS model showed the best prediction accuracy and can be used to predict the properties of fibre reinforced cement mortar accurately.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 1.**Nano-CuO particles of uniform distribution observed using transmission electron micrographs (TEM) (of size 50 nm).

**Figure 7.**Percentage change in compressive strength of samples by the addition of fibres at 28 and 90 days with similar volumes of metakaolin and nano-CuO.

**Figure 10.**Percentage change in flexural strength of samples by the addition of fibres at 28 and 90 days with similar volumes of MK and NC.

**Figure 13.**Percentage change in water absorption of samples by the addition of fibres at 28 days with similar volumes of MK and NC.

Chemical Analysis (%) | Cement | MK |
---|---|---|

SiO_{2} | 21.75 | 52.1 |

Al_{2}O_{3} | 5.15 | 43.8 |

Fe_{2}O_{3} | 3.23 | 2.6 |

CaO | 63.75 | 0.2 |

MgO | 1.15 | 0.21 |

SO_{3} | 1.95 | 0 |

K_{2}O | 0.56 | 0.32 |

Na_{2}O | 0.33 | 0.11 |

L.O.I | 2.08 | 0.99 |

Surface area (BET) (m^{2}/g) | 0.31 | 2.54 |

Specific gravity | 3.15 | 2.6 |

Nanoparticles | Average Diameter (nm) | Specific Surface Area (m^{2}/g) | Purity (%) |
---|---|---|---|

nano-CuO | 20 ± 3 | 200 | >99 |

Unit weight (g/cm^{3}) | 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 |

Length (mm) | 6 |

Diameter (μm) | 20 |

Sample ID | Cement (kg/m^{3}) | MK (kg/m^{3}) | NC (kg/m^{3}) | PP (kg/m^{3}) | Water (kg/m^{3}) | Sand (kg/m^{3}) | SP (kg/m^{3}) |
---|---|---|---|---|---|---|---|

CO | 450 | 0 | 0 | 0 | 220 | 1430 | 0.9 |

MK10 | 405 | 45 | 0 | 0 | 220 | 1415 | 1.9 |

MK10NC1 | 400.5 | 45 | 4.5 | 0 | 220 | 1410 | 1.9 |

MK10NC2 | 396 | 45 | 9 | 0 | 220 | 1400 | 1.9 |

MK10NC3 | 391.5 | 45 | 13.5 | 0 | 220 | 1395 | 1.9 |

MK20 | 360 | 90 | 0 | 0 | 220 | 1400 | 2.5 |

MK20NC1 | 355.5 | 90 | 4.5 | 0 | 220 | 1390 | 2.5 |

MK20NC2 | 351 | 90 | 9 | 0 | 220 | 1385 | 2.5 |

MK20NC3 | 346.5 | 90 | 13.5 | 0 | 220 | 1380 | 2.5 |

MK30 | 315 | 135 | 0 | 0 | 220 | 1380 | 3.5 |

MK30NC1 | 310.5 | 135 | 4.5 | 0 | 220 | 1375 | 3.5 |

MK30NC2 | 306 | 135 | 9 | 0 | 220 | 1370 | 3.5 |

MK30NC3 | 301.5 | 135 | 13.5 | 0 | 220 | 1360 | 3.5 |

PP | 450 | 0 | 0 | 1.35 | 220 | 1430 | 1.75 |

PP-MK10 | 405 | 45 | 0 | 1.35 | 220 | 1415 | 2.75 |

PP-MK10NC1 | 400.5 | 45 | 4.5 | 1.35 | 220 | 1410 | 2.75 |

PP-MK10NC2 | 396 | 45 | 9 | 1.35 | 220 | 1400 | 2.75 |

PP-MK10NC3 | 391.5 | 45 | 13.5 | 1.35 | 220 | 1395 | 2.75 |

PP-MK20 | 360 | 90 | 0 | 1.35 | 220 | 1400 | 3.75 |

PP-MK20NC1 | 355.5 | 90 | 4.5 | 1.35 | 220 | 1390 | 3.75 |

PP-MK20NC2 | 351 | 90 | 9 | 1.35 | 220 | 1385 | 3.75 |

PP-MK20NC3 | 346.5 | 90 | 13.5 | 1.35 | 220 | 1380 | 3.75 |

PP-MK30 | 315 | 135 | 0 | 1.35 | 220 | 1380 | 4.25 |

PP-MK30NC1 | 310.5 | 135 | 4.5 | 1.35 | 220 | 1375 | 4.25 |

PP-MK30NC2 | 306 | 135 | 9 | 1.35 | 220 | 1370 | 4.25 |

PP-MK30NC3 | 301.5 | 135 | 13.5 | 1.35 | 220 | 1360 | 4.25 |

**Table 5.**Adaptive neuro-fuzzy inference system (ANFIS) and Primal Estimated sub-GrAdient Solver for SVM (Pegasos) results.

ANFIS | R^{2} Values for: Training Set, Testing Set, and Validation Set | |||

Data Set | Training Set | Testing Set | Validation Set | |

Compressive strength | R^{2} | 1 | 0.90 | 0.94 |

RRMSE | 2.96 × 10^{−6} | 9.48 × 10^{−4} | 4.8540 × 10^{−4} | |

MAPE | 0.046 | 0.41 | 0.08 | |

Flexural strength | R^{2} | 1 | 0.96 | 0.94 |

RRMSE | 6.98 × 10^{−3} | 0.016 | 0.0068 | |

MAPE | 2.94 | 0.015 | 0.013 | |

Water absorption | R^{2} | 1 | 0.93 | 0.97 |

RRMSE | 1.18 × 10^{−5} | 0.0023 | 0.0025 | |

MAPE | 0.004 | 1.27 | 1.58 | |

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 | R^{2} Values for: Training Set, Testing Set, and Validation Set | |||

Data Set | Training Set | Testing Set | ||

Compressive strength | R^{2} | 0.96 | 0.9 | |

RRMSE | 2.91 × 10^{−4} | 0.00052 | ||

MAPE | 1.85 | 2.47 | ||

flexural strength | R^{2} | 0.99 | 0.91 | |

RRMSE | 0.0017 | 0.005 | ||

MAPE | 1.07 | 3.31 | ||

Water absorption | R^{2} | 0.96 | 0.9 | |

RRMSE | 2.96 × 10^{−6} | 2.85 × 10^{−3} | ||

MAPE | 0.00305 | 1.66 | ||

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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**MDPI and ACS Style**

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

**AMA Style**

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/ma10101215

**Chicago/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