Durability Behaviours of Engineered Cementitious Composites Blended with Carbon Nanotubes against Sulphate and Acid Attacks by Applying RSM Modelling and Optimization
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Research Mix Proportions Developed by RSM
2.3. Mixing and Sample Preparation
2.4. Testing Methods
2.4.1. Compressive Strength
2.4.2. Durability Properties Due to Acid Attack
2.4.3. Durability Properties Due to Sulphate Attack
2.4.4. Rapid Chloride Permeability Test (RCPT)
3. Results and Discussions
3.1. Compressive Strength of CNT-ECC
3.2. Weight Loss Due Acid Attack
3.3. Change in Length Due to Acid Attack
3.4. Compressive Strength Due to Acid Attack
3.5. pH Test
3.6. Rapid Chloride Penetration Test (RCPT)
3.7. Weight Gain Due to Sulphate Attack
3.8. Expansion Due to Sulphate Attack
3.9. Compressive Strength of Multiwalled CNT-ECC Due to Sulphate Attack
4. Predictive Model Developments Using Response Surface Methodology (RSM) and Optimization
4.1. Analysis of Variance (ANOVA)
4.2. Optimization
5. Conclusions
- The lowest strength of the ECC blended with several concentrations of PVA fibre was measured at 0.08% of multiwalled CNTs, while the maximum compressive strength was noticed at 0.05% of multiwalled CNTs. According to recommendations, the strongest ECC was produced by adding 0.05% multiwalled CNTs by weight of PC and, when more multiwalled CNTs were accumulated in the ECC, the strength began to decline over each curing time.
- The adverse effect response to a sulphate attack was due to the filling action of multiwalled CNTs that fill the empty spaces in pores and further impeded sulphate ion entry. Their diminishing efficiency might be attributed to nanomaterial agglomeration at high dosages.
- The ECC performed better because of the multiwalled CNTs in the combination when subjected to harsh circumstances. However, the composites that contained more multiwalled CNTs were considered to be more durable; mixtures that contained fewer multiwalled CNTs showed higher rates of weight loss and length change as a result of deterioration.
- The manufacturing of ECC uses more PVA fibres, which include varying amounts of multiwalled CNTs, raising pH levels, yet these pH levels were all shown to be less than those of the control combination. Additionally, when the amount of multiwalled CNTs as a nanomaterial grew in the combination, the pH value of the ECC mix mixed with different quantities of PVA fibre decreased.
- Utilizing carbon nanotubes resulted in an increased ability to resist penetration by chlorine ions. With the incorporation of multiwalled CNT ingredients, chloride permeability measurements decreased. Three mixtures, M4, M6, and M11, were categorized as having very low chloride penetration, while the remaining mixtures were categorized as having low chloride penetration.
- The compressive strength, sulphate resistance, and acid resistance were predicted using RSM models based on the quantities of PVA fibres and multiwalled CNTs used as nanoscale ingredients in the manufacturing of ECC. Every prototype had a level of certainty of 95%, while the difference between adjusted R2 and predicted R2 was not more than 0.2.
- The use of 0.05% multiwalled CNTs in an ECC combination with 1% to 1.50% of PVA fibre added can alleviate the challenges relating to large-scale implementation as well as the adverse effects of sulphate and acid attack, making it suitable for the construction sector.
- Multiwalled carbon nanotubes (MWCNTs) have shown remarkable potential in engineered cementitious composites (ECCs) and this has sparked a lot of attention in the area of materials research. The mechanical and functional qualities of the resultant composites may be improved by including MWCNTs in ECCs. Several desired properties can be obtained by spreading MWCNTs inside the cement matrix. MWCNTs are capable of strengthening the ECCs and enhancing their overall performance because of their remarkable mechanical qualities, which include great strength and stiffness. The MWCNTs’ tiny size and high aspect ratio also help them enhance the composites’ mechanical characteristics.
- To sum up, using MWCNTs in ECCs has the potential to significantly enhance the mechanical and environmental characteristics of these composites. The results thus far indicate that MWCNTs may help in the creation of high-performance and sustainable infrastructure materials, but further study is required.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Materials | Compound (%) | Specific Gravity | Blaine Fineness (m2/kg) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
SiO2 | Al2O3 | Fe2O3 | MnO | CaO | MgO | Na2O | K2O | T2O | |||
PC | 20.76 | 5.54 | 3.35 | - | 61.4 | 2.48 | 0.19 | 0.78 | - | 3.15 | 290 |
FA | 57.01 | 20.96 | 4.15 | 0.033 | 9.79 | 1.75 | 2.23 | 1.53 | 0.68 | 2.38 | 325 |
Properties | Values |
---|---|
Particle Size | 5 to 15 nm |
Length | 0.1 to 10 µm |
Number of Walls | 3 to 15 |
Specific Surface Area | 250 to 300 m2/g |
Reported Purity | >95% |
Modulus of Elasticity | 1.28 TPa |
Tensile Strength | 100 GPa |
pH | 4 to 10 |
Materials | Compound (%) | Specific Gravity | Blaine Fineness (m2/kg) | Loss on Ignition | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
SiO2 | Al2O3 | Fe2O3 | MnO | CaO | MgO | Na2O | K2O | T2O | ||||
FA | 57.01 | 20.96 | 4.15 | 0.033 | 9.79 | 1.75 | 2.23 | 1.53 | 0.68 | 2.38 | 290 | 1.25 |
Cement | 20.76 | 5.54 | 3.35 | - | 61.4 | 2.48 | 0.19 | 0.78 | - | 3.15 | 325 | 2.20 |
Mix ID | Materials (%) | Quantity of Materials Used in ECC Mixture (kg/m3) | ||||
---|---|---|---|---|---|---|
CNTs | PVA | PC | Fly Ash | Sand | Water | |
M0 | 0.00 | 2 | 583 | 700 | 467 | 385 |
M1 | 0.05 | 1.5 | 583 | 700 | 467 | 385 |
M2 | 0.065 | 2 | 583 | 700 | 467 | 385 |
M3 | 0.065 | 1.5 | 583 | 700 | 467 | 385 |
M4 | 0.08 | 1.5 | 583 | 700 | 467 | 385 |
M5 | 0.065 | 1.5 | 583 | 700 | 467 | 385 |
M6 | 0.065 | 1 | 583 | 700 | 467 | 385 |
M7 | 0.05 | 2 | 583 | 700 | 467 | 385 |
M8 | 0.065 | 1.5 | 583 | 700 | 467 | 385 |
M9 | 0.05 | 1 | 583 | 700 | 467 | 385 |
M10 | 0.065 | 1.5 | 583 | 700 | 467 | 385 |
M11 | 0.08 | 1 | 583 | 700 | 467 | 385 |
M12 | 0.065 | 1.5 | 583 | 700 | 467 | 385 |
M13 | 0.08 | 2 | 583 | 700 | 467 | 385 |
Response | Source | Sum of Squares | Df | Mean Square | F-Value | p-Value > F | Significance |
---|---|---|---|---|---|---|---|
Model | 484.28 | 5 | 96.86 | 60.66 | <0.0001 | Yes | |
A-PVA | 72.80 | 1 | 72.80 | 45.59 | 0.0003 | Yes | |
B-CNTs | 315.37 | 1 | 315.37 | 197.52 | <0.0001 | Yes | |
AB | 12.01 | 1 | 12.01 | 7.52 | 0.0288 | Yes | |
A2 | 0.38 | 1 | 0.38 | 0.24 | 0.6417 | No | |
B2 | 67.66 | 1 | 67.66 | 42.38 | 0.0003 | Yes | |
Compressive Strength | Residual | 11.18 | 7 | 1.60 | |||
Lack of Fit | 10.93 | 3 | 3.64 | 58.76 | 0.0009 | Yes | |
Pure Error | 0.25 | 4 | 0.062 | ||||
Cor Total | 495.46 | 12 | |||||
Model | 24.99 | 3 | 8.33 | 53.75 | <0.0001 | Yes | |
A-PVA | 23.68 | 1 | 23.68 | 152.79 | <0.0001 | Yes | |
B-CNTs | 0.12 | 1 | 0.12 | 0.80 | 0.3957 | No | |
Weight Loss | AB | 1.19 | 1 | 1.19 | 7.67 | 0.0218 | Yes |
Residual | 1.39 | 9 | 0.15 | ||||
Lack of Fit | 0.65 | 5 | 0.13 | 0.70 | 0.6507 | No | |
Pure Error | 0.74 | 4 | 0.19 | ||||
Cor Total | 26.39 | 12 | |||||
Model | 0.33 | 5 | 0.066 | 65.75 | <0.0001 | Yes | |
A-PVA | 0.073 | 1 | 0.073 | 72.11 | <0.0001 | Yes | |
B-CNTs | 0.22 | 1 | 0.22 | 218.94 | <0.0001 | Yes | |
AB | 0.029 | 1 | 0.029 | 28.71 | 0.0011 | Yes | |
Change in Length | A2 | 6.529 × 10−3 | 1 | 6.529 × 10−3 | 6.49 | 0.0383 | Yes |
B2 | 5.941 × 10−3 | 1 | 5.941 × 10−3 | 5.90 | 0.0455 | Yes | |
Residual | 7.047 × 10−3 | 7 | 1.007 × 10−3 | ||||
Lack of Fit | 6.367 × 10−3 | 3 | 2.122 × 10−3 | 12.48 | 0.0169 | Yes | |
Pure Error | 6.800 × 10−4 | 4 | 1.700 × 10−4 | ||||
Cor Total | 0.34 | 12 | |||||
Model | 542.55 | 5 | 108.51 | 61.96 | <0.0001 | Yes | |
A-PVA | 103.00 | 1 | 103.00 | 58.81 | 0.0001 | Yes | |
B-CNTs | 354.36 | 1 | 354.36 | 202.33 | <0.0001 | Yes | |
AB | 10.43 | 1 | 10.43 | 5.96 | 0.0447 | Yes | |
Compressive Strength due to Acid Attack | A2 | 0.64 | 1 | 0.64 | 0.37 | 0.5646 | No |
B2 | 58.60 | 1 | 58.60 | 33.46 | 0.0007 | Yes | |
Residual | 12.26 | 7 | 1.75 | ||||
Lack of Fit | 12.08 | 3 | 4.03 | 89.34 | 0.0004 | Yes | |
Pure Error | 0.18 | 4 | 0.045 | ||||
Cor Total | 554.81 | 12 | |||||
Model | 1.40 | 5 | 0.28 | 314.72 | <0.0001 | Yes | |
A-PVA | 0.84 | 1 | 0.84 | 950.15 | <0.0001 | Yes | |
B-CNTs | 0.49 | 1 | 0.49 | 555.25 | <0.0001 | Yes | |
AB | 4.000 × 10−4 | 1 | 4.000 × 10−4 | 0.45 | 0.5236 | No | |
pH Value | A2 | 0.024 | 1 | 0.024 | 27.46 | 0.0012 | Yes |
B2 | 0.055 | 1 | 0.055 | 61.86 | 0.0001 | Yes | |
Residual | 6.216 × 10−3 | 7 | 8.880 × 10−4 | ||||
Lack of Fit | 5.496 × 10−3 | 3 | 1.832 × 10−3 | 10.18 | 0.0242 | Yes | |
Pure Error | 7.200 × 10−4 | 4 | 1.800 × 10−4 | ||||
Cor Total | 1.40 | 12 | |||||
Model | 4.860 × 105 | 5 | 97,208.86 | 211.31 | <0.0001 | Yes | |
A-PVA | 3.589 × 105 | 1 | 3.589 × 105 | 780.11 | <0.0001 | Yes | |
B-CNTs | 1.149 × 105 | 1 | 1.149 × 105 | 249.76 | <0.0001 | Yes | |
Rapid Chloride Penetration Test | AB | 5587.56 | 1 | 5587.56 | 12.15 | 0.0102 | Yes |
A2 | 5331.97 | 1 | 5331.97 | 11.59 | 0.0114 | Yes | |
B2 | 37.56 | 1 | 37.56 | 0.082 | 0.7833 | No | |
Residual | 3220.26 | 7 | 460.04 | ||||
Lack of Fit | 3187.46 | 3 | 1062.49 | 129.58 | 0.0002 | Yes | |
Pure Error | 32.80 | 4 | 8.20 | ||||
Cor Total | 4.893 × 105 | 12 | |||||
Model | 0.82 | 5 | 0.16 | 91.72 | <0.0001 | Yes | |
A-PVA | 0.70 | 1 | 0.70 | 392.40 | <0.0001 | Yes | |
B-CNTs | 0.017 | 1 | 0.017 | 9.56 | 0.0175 | Yes | |
AB | 0.070 | 1 | 0.070 | 39.34 | 0.0004 | Yes | |
Weight Gain | A2 | 0.029 | 1 | 0.029 | 16.07 | 0.0051 | Yes |
B2 | 4.732 × 10−4 | 1 | 4.732 × 10−4 | 0.27 | 0.6225 | No | |
Residual | 0.012 | 7 | 1.785 × 10−3 | ||||
Lack of Fit | 0.012 | 3 | 4.117 × 10−3 | 115.52 | 0.0002 | Yes | |
Pure Error | 1.426 × 10−4 | 4 | 3.564 × 10−5 | ||||
Cor Total | 0.83 | 12 | |||||
Model | 1.501 × 10−5 | 2 | 7.504 × 10−6 | 46.92 | <0.0001 | Yes | |
A-PVA | 3.527 × 10−6 | 1 | 3.527 × 10−6 | 22.05 | 0.0008 | Yes | |
B-CNTs | 1.148 × 10−5 | 1 | 1.148 × 10−5 | 71.79 | <0.0001 | Yes | |
Expansion | Residual | 1.599 × 10−6 | 10 | 1.599 × 10−7 | |||
Lack of Fit | 1.567 × 10−6 | 6 | 2.612 × 10−7 | 32.65 | 0.0023 | Yes | |
Pure Error | 3.200 × 10−8 | 4 | 8.000 × 10−9 | ||||
Cor Total | 1.661 × 10−5 | 12 | |||||
Model | 481.53 | 5 | 96.31 | 63.29 | <0.0001 | Yes | |
A-PVA | 76.59 | 1 | 76.59 | 50.33 | 0.0002 | Yes | |
B-CNTs | 318.00 | 1 | 318.00 | 208.98 | <0.0001 | Yes | |
AB | 12.08 | 1 | 12.08 | 7.94 | 0.0259 | Yes | |
Compressive Strength due to Sulphate Attack | A2 | 0.27 | 1 | 0.27 | 0.18 | 0.6855 | No |
B2 | 60.63 | 1 | 60.63 | 39.84 | 0.0004 | Yes | |
Residual | 10.65 | 7 | 1.52 | ||||
Lack of Fit | 10.40 | 3 | 3.47 | 54.93 | 0.0010 | Yes | |
Pure Error | 0.25 | 4 | 0.063 | ||||
Cor Total | 492.18 | 12 |
Model Validation Constraints | CS | Weight Loss | Change in Length | CS Due Acid Attack | pH | RCPT | Weight Gain | Expansion | CS Due to Acid Attack |
---|---|---|---|---|---|---|---|---|---|
Std. Dev. | 1.26 | 0.39 | 0.032 | 1.32 | 0.030 | 21.45 | 0.042 | 3.999 × 10−4 | 1.23 |
Mean | 57.55 | 7.58 | 0.52 | 54.06 | 9.94 | 1110.63 | 0.94 | 4.931 × 10−3 | 55.07 |
C.V. % | 2.20 | 5.20 | 6.10 | 2.45 | 0.30 | 1.93 | 4.49 | 8.11 | 2.24 |
PRESS | 110.99 | 2.80 | 0.065 | 122.53 | 0.055 | 23,316.21 | 0.12 | 3.428 × 10−6 | 105.31 |
−2 Log Likelihood | 34.93 | 7.87 | −60.87 | 36.13 | −62.50 | 108.55 | −53.42 | −169.95 | 34.30 |
R-Squared | 0.9774 | 0.9471 | 0.9792 | 0.9779 | 0.9956 | 0.9934 | 0.9850 | 0.9037 | 0.9784 |
Adj R-Squared | 0.9613 | 0.9295 | 0.9643 | 0.9621 | 0.9924 | 0.9887 | 0.9742 | 0.8844 | 0.9629 |
Pred R-Squared | 0.7760 | 0.8939 | 0.8086 | 0.7792 | 0.9610 | 0.9523 | 0.8506 | 0.7936 | 0.7860 |
Adeq Precision | 25.006 | 23.186 | 27.990 | 26.312 | 65.367 | 52.562 | 33.040 | 22.382 | 25.901 |
BIC | 50.32 | 18.13 | −45.48 | 51.52 | −47.11 | 123.94 | −38.03 | −162.25 | 49.69 |
AICc | 60.93 | 20.87 | −34.87 | 62.13 | −36.50 | 134.55 | −27.42 | −161.28 | 60.30 |
Factors | Input Factors | Responses (Output Factors) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
CNTs (%) | PVA (%) | CS (MPa) | RCPT (Coulombs) | Sulphate Attack | Acid Attack | |||||||
Wt. Gain (%) | Expansion (%) | CS (MPa) | Wt. Loss (%) | Change in Length (%) | CS | pH | ||||||
Value | Min. | 0.05 | 1 | 50.18 | 799.25 | 0.44 | 0.0027 | 47.46 | 5.10 | 0.30 | 45.30 | 9.25 |
Max. | 0.08 | 2 | 74.30 | 1569.75 | 1.40 | 0.0069 | 71.75 | 10.20 | 0.90 | 71.72 | 10.61 | |
Goal | Range | Range | Max. | Min. | Min. | Max. | Max | Min. | Min. | Max. | Min. | |
Optimization Results | 0.05 | 1 | 72.88 | 992.72 | 0.63 | 0.0055 | 70.35 | 5.99 | 0.52 | 70.24 | 9.81 | |
Desirability | 77.10% (0.771) |
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Bheel, N.; Mohammed, B.S.; Liew, M.S.; Zawawi, N.A.W.A. Durability Behaviours of Engineered Cementitious Composites Blended with Carbon Nanotubes against Sulphate and Acid Attacks by Applying RSM Modelling and Optimization. Buildings 2023, 13, 2032. https://doi.org/10.3390/buildings13082032
Bheel N, Mohammed BS, Liew MS, Zawawi NAWA. Durability Behaviours of Engineered Cementitious Composites Blended with Carbon Nanotubes against Sulphate and Acid Attacks by Applying RSM Modelling and Optimization. Buildings. 2023; 13(8):2032. https://doi.org/10.3390/buildings13082032
Chicago/Turabian StyleBheel, Naraindas, Bashar S. Mohammed, M. S. Liew, and Noor Amila Wan Abdullah Zawawi. 2023. "Durability Behaviours of Engineered Cementitious Composites Blended with Carbon Nanotubes against Sulphate and Acid Attacks by Applying RSM Modelling and Optimization" Buildings 13, no. 8: 2032. https://doi.org/10.3390/buildings13082032
APA StyleBheel, N., Mohammed, B. S., Liew, M. S., & Zawawi, N. A. W. A. (2023). Durability Behaviours of Engineered Cementitious Composites Blended with Carbon Nanotubes against Sulphate and Acid Attacks by Applying RSM Modelling and Optimization. Buildings, 13(8), 2032. https://doi.org/10.3390/buildings13082032