Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach
Abstract
:1. Introduction
2. Artificial Neural Network
- 1.
- Simple elements known as neurons are responsible for the processing of information,
- 2.
- Processed information is passed neuron over connection link,
- 3.
- An associated weight is considered for each connection link,
- 4.
- Inputs are transmitted from a predefined activation function in neurons and outputs are determined.
Dataset
3. Experimental Program
3.1. Material
3.2. Mix Proportions and Test Method
4. MLP Modeling
5. Results and Discussion
5.1. Experimental Compressive Strength
5.2. Prediction of the Compressive Strength
6. Developing a Software to Predict the Results
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
SCM | Supplementary Cementitious Material |
ML | Machine Learning |
C-S-H | Calcium Silicate Hydrate |
C-A-H | Calcium Aluminate Hydrate |
C-A-S-H | Calcium Alumino Silicate Hydrate |
OPC | Ordinary Portland Cement |
FA | Fly Ash |
SL | Furnace Slag |
MK | Metakaolin |
RA | Rice Husk Ash |
SF | Silica Fume |
ZE | Zeolite |
BA | Bagasse Ash |
EC | Equilibrium Catalyst |
ANN | Artificial Neural Network |
MLP | Multi-Layer Perceptron |
W | Water |
C | Cement |
F | Fine aggregate |
G | Coarse aggregate |
SS | Specific Surface |
f | compressive strength |
MSE | Mean Squared Error |
LM | Levenberg-Marquardt |
RMSE | Root Mean Square Error |
NSE | Nash-Sutcliffe Efficiency |
MAPE | Mean Absolute Percentage Error |
R | Correlation Coefficient |
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Attribute | Unit | min | max | Average | Standard Deviation |
---|---|---|---|---|---|
Water | kg/m | 155 | 216 | 172 | 16.8 |
Cement | kg/m | 175 | 476 | 291 | 76.5 |
Fine Aggregate | kg/m | 470 | 971 | 798 | 163.5 |
Coarse Aggregate | kg/m | 865 | 1268 | 963.5 | 80.3 |
First SCM | kg/m | 8.75 | 126 | 54.3 | 33.6 |
SiO | % | 36 | 96 | 54 | 11.7 |
CaO | % | 0.09 | 38.1 | 6.6 | 11.5 |
FeO | % | 0.46 | 13.8 | 3 | 4.5 |
AlO | % | 0.1 | 45 | 28.8 | 16 |
MgO | % | 0.01 | 6.6 | 1.7 | 2.5 |
Specific Surface | cm/kg | 3100 | 235,000 | 17,316 | 36,623 |
Second SCM | kg/m | 8.75 | 165 | 62 | 46.5 |
SiO | % | 31.5 | 94.9 | 62.5 | 21.8 |
CaO | % | 0.06 | 44.4 | 9.7 | 12.5 |
FeO | % | 0.45 | 13.4 | 4.1 | 3.3 |
AlO | % | 1 | 54.5 | 14.8 | 9.2 |
MgO | % | 0.01 | 6.8 | 3.1 | 2.2 |
Specific Surface | cm/kg | 3100 | 1,500,700 | 92,688.8 | 178,892.4 |
Age | days | 3 | 365 | 39.9 | 55.4 |
Compressive strength | MPa | 7.9 | 85.5 | 37.7 | 21.6 |
Chemical Specification | SiO | AlO | FeO | CaO | MgO | SO | KO | NaO | L.O.I | I.R | CS | CS | CA | CAF |
21.6 | 5.8 | 3.1 | 61.4 | 4 | 1 | 0.6 | 0.21 | 2 | 0.2 | 47.24 | 25.02 | 8.26 | 12.1 | |
Physical Specification | Specific surface [mkg] | Specific gravity [kg/m] | Initial Setting Time [min] | Final Setting Time [min] | 28-day compressive strength [MPa] | |||||||||
350 | 3120 | 100 | 195 | 49 |
Chemical Specification | SiO | AlO | FeO | CaO | MgO | TiO | KO | NaO | L.O.I |
52.3 | 45.1 | 0.7 | 0.08 | 0.03 | 0.69 | 0.03 | 0.02 | 1.05 | |
Physical Specification | Specific surface [mkg] | Specific gravity [kg/m] | PH | color | Humidity [%] | ||||
2500 | 2600 | 4–5 | white | 0.5–1 |
Chemical Specification | SiO | AlO | FeO | CaO | MgO | KO | NaO | L.O.I |
48.5 | 29.75 | 7.8 | 6.62 | 1.78 | 0.03 | 0.38 | 1.86 | |
Physical Specification | Specific surface [mkg] | Specific gravity [kg/m] | PH | color | Humidity [%] | |||
600 | 2300 | 4–5 | light gray | 5–8 |
Chemical Specification | SiO | AlO | FeO | CaO | MgO | TiO | KO | NaO | L.O.I |
67.79 | 13.66 | 1.44 | 1.68 | 1.2 | 0.21 | 3.12 | 0.02 | 10.88 | |
Physical Specification | Specific surface [mkg] | Specific gravity [kg/m] | PH | color | Humidity [%] | ||||
1800 | 2350 | 4–5 | yellow | 5–8 |
Mixture | Cement | Water | Fine Aggregate | Coarse Aggregate | MK | ZE | FA |
---|---|---|---|---|---|---|---|
MK2.5ZE2.5 | 332.5 | 160 | 971 | 912 | 8.75 | 8.75 | 0 |
MK5ZE5 | 315 | 160 | 971 | 912 | 17.5 | 17.5 | 0 |
MK7.5ZE7.5 | 297.5 | 160 | 971 | 912 | 26.3 | 26.3 | 0 |
MK10ZE10 | 280 | 160 | 971 | 912 | 35 | 35 | 0 |
MK15ZE15 | 245 | 160 | 971 | 912 | 52.5 | 52.5 | 0 |
MK20ZE20 | 210 | 160 | 971 | 912 | 70 | 70 | 0 |
MK25ZE25 | 175 | 160 | 971 | 912 | 87.5 | 87.5 | 0 |
MK2.5FA2.5 | 332.5 | 160 | 971 | 912 | 8.75 | 0 | 8.75 |
MK5FA5 | 315 | 160 | 971 | 912 | 17.5 | 0 | 17.5 |
MK7.5FA7.5 | 297.5 | 160 | 971 | 912 | 26.3 | 0 | 26.3 |
MK10FA10 | 280 | 160 | 971 | 912 | 35 | 0 | 35 |
MK15FA15 | 245 | 160 | 971 | 912 | 52.5 | 0 | 52.5 |
MK20FA20 | 210 | 160 | 971 | 912 | 70 | 0 | 70 |
MK25FA25 | 175 | 160 | 971 | 912 | 87.5 | 0 | 87.5 |
ZE2.5FA2.5 | 332.5 | 160 | 971 | 912 | 0 | 8.75 | 8.75 |
ZE5FA5 | 315 | 160 | 971 | 912 | 0 | 17.5 | 17.5 |
ZE7.5FA7.5 | 297.5 | 160 | 971 | 912 | 0 | 26.3 | 26.3 |
ZE10FA10 | 280 | 160 | 971 | 912 | 0 | 35 | 35 |
ZE15FA15 | 245 | 160 | 971 | 912 | 0 | 52.5 | 52.5 |
ZE20FA20 | 210 | 160 | 971 | 912 | 0 | 70 | 70 |
ZE25FA25 | 175 | 160 | 971 | 912 | 0 | 87.5 | 87.5 |
Output | MSE | RMSE | NSE | MAPE | R |
---|---|---|---|---|---|
Compressive strength | 0.0272 | 0.9903 | 7.24% | 0.9952 |
Mixture | 3-Day | Compare with Control Specimen | 7-Day | Compare with Control Specimen | 28-Day | Compare with Control Specimen | 90-Day | Compare with Control Specimen |
---|---|---|---|---|---|---|---|---|
Control | 12.8 | – | 17 | – | 23.1 | – | 24.2 | – |
MK2.5ZE2.5 | 13.7 | 1.07 | 16.7 | 0.98 | 26 | 1.13 | 30.6 | 1.26 |
MK5ZE5 | 14.2 | 1.11 | 17.6 | 1.04 | 27.3 | 1.18 | 32.6 | 1.35 |
MK7.5Z7E.5 | 13.2 | 1.03 | 17 | 1.00 | 26.9 | 1.16 | 32.2 | 1.33 |
MK10ZE10 | 12.4 | 0.97 | 16.4 | 0.96 | 26.4 | 1.14 | 31.9 | 1.32 |
MK15ZE15 | 10 | 0.78 | 14.2 | 0.84 | 22.9 | 0.99 | 28.4 | 1.17 |
MK20ZE20 | 8.5 | 0.66 | 12.3 | 0.72 | 20.8 | 0.90 | 25.7 | 1.06 |
MK25ZE25 | 7.9 | 0.62 | 11.1 | 0.65 | 18.9 | 0.82 | 23.5 | 0.97 |
MK2.5FA2.5 | 14.7 | 1.15 | 18.6 | 1.09 | 27.3 | 1.18 | 32 | 1.32 |
MK5FA5 | 15.6 | 1.22 | 19.4 | 1.14 | 28.9 | 1.25 | 33.4 | 1.38 |
MK7.5FA7.5 | 14.2 | 1.11 | 18.3 | 1.08 | 27.7 | 1.20 | 32.2 | 1.33 |
MK10FA10 | 12.6 | 0.98 | 15.8 | 0.93 | 24.7 | 1.07 | 30.5 | 1.26 |
MK15FA15 | 11 | 0.86 | 15.3 | 0.90 | 24.2 | 1.05 | 29.5 | 1.22 |
MK20FA20 | 9.5 | 0.74 | 12.5 | 0.74 | 21.1 | 0.91 | 26.1 | 1.08 |
MK25FA25 | 8.5 | 0.66 | 11.5 | 0.68 | 19.6 | 0.85 | 24.1 | 1.00 |
ZE2.5FA2.5 | 13.1 | 1.02 | 17.5 | 1.03 | 26.4 | 1.14 | 30.8 | 1.27 |
ZE5FA5 | 13.7 | 1.07 | 18.3 | 1.08 | 27.2 | 1.18 | 31.9 | 1.32 |
ZE7.5FA7.5 | 13 | 1.02 | 17.6 | 1.04 | 26.4 | 1.14 | 30.8 | 1.27 |
ZE10FA10 | 12 | 0.94 | 15.4 | 0.91 | 24.3 | 1.05 | 29.6 | 1.22 |
ZE15FA15 | 8.9 | 0.70 | 14.4 | 0.85 | 23 | 1.00 | 28.1 | 1.16 |
ZE20FA20 | 7.7 | 0.60 | 12.6 | 0.74 | 21.6 | 0.94 | 26.5 | 1.10 |
ZE25FA25 | 7 | 0.55 | 10.5 | 0.62 | 18.4 | 0.80 | 22.9 | 0.95 |
Mixture | Cement | Water | Coarse Aggregate | Fine Aggregate | First SCM | Second SCM |
---|---|---|---|---|---|---|
Assumed Mix Design | 350 | 160 | 912 | 971 | 8.75–105 | 8.75–105 |
SCM | SiO (%) | CaO (%) | FeO (%) | AlO (%) | MgO (%) | Specific Surface (cm/g) |
---|---|---|---|---|---|---|
FA | 57.64 | 12.01 | 4.45 | 19.23 | 2.43 | 3100 |
MK | 51.37 | 0.23 | 0.46 | 44.6 | 0.03 | 3950 |
RA | 76.3 | 5.5 | 1.5 | 1.6 | 0.01 | 11,000 |
SF | 94.9 | 0.5 | 0.7 | 1 | 0.61 | 153,000 |
SL | 31.55 | 44.38 | 0.53 | 13.79 | 5.2 | 4497 |
ZE | 67.79 | 1.68 | 1.44 | 13.66 | 1.2 | 18,000 |
SCM | Replacement Level (kg/m) | SiO (%) | CaO (%) | FeO (%) | AlO (%) | MgO (%) | Specific Surface (cm/g) |
---|---|---|---|---|---|---|---|
Variation of pozzolans | 35 | 42 to 76.5 | 1 to 31 | 4.45 | 17 to 38 | 2.43 | 3100 |
SF | 35 | 94.9 | 0.5 | 0.7 | 1 | 0.61 | 153,000 |
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Moradi, N.; Tavana, M.H.; Habibi, M.R.; Amiri, M.; Moradi, M.J.; Farhangi, V. Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach. Materials 2022, 15, 5336. https://doi.org/10.3390/ma15155336
Moradi N, Tavana MH, Habibi MR, Amiri M, Moradi MJ, Farhangi V. Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach. Materials. 2022; 15(15):5336. https://doi.org/10.3390/ma15155336
Chicago/Turabian StyleMoradi, Nozar, Mohammad Hadi Tavana, Mohammad Reza Habibi, Moslem Amiri, Mohammad Javad Moradi, and Visar Farhangi. 2022. "Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach" Materials 15, no. 15: 5336. https://doi.org/10.3390/ma15155336