Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning
Abstract
1. Introduction
2. Materials and Methods
2.1. Prediction of Mechanical Properties
2.2. Regression Types
2.3. Accuracy Finding
3. Results and Discussion
3.1. HPC Concrete
3.1.1. HPC Mixture Design
Studies | Specimens | Cement (kg/m3) | Water (kg/m3) | Mineral Admixture (kg/m3) | Filler (kg/m3) | Superplasticizer | Fiber (%) | Fine Aggregate (kg/m3) | Coarse Aggregate (kg/m3) |
---|---|---|---|---|---|---|---|---|---|
Ayub et al. [9] | P-0 | 450 | 180 | - | - | - | 0 (Basalt Fiber) | 670 | 1100 |
PB-1 | 450 | 180 | - | - | - | 1 (Basalt Fiber) | 670 | 1100 | |
PB-2 | 450 | 180 | - | - | - | 2 (Basalt Fiber) | 670 | 1100 | |
PB-3 | 450 | 180 | - | - | - | 3 (Basalt Fiber) | 670 | 1100 | |
S-0 | 450 | 180 | 45 (Silica Fume) | - | - | 0 (Basalt Fiber) | 670 | 1100 | |
SB-1 | 450 | 180 | 45 (Silica Fume) | - | - | 1 (Basalt Fiber) | 670 | 1100 | |
SB-2 | 450 | 180 | 45 (Silica Fume) | - | - | 2 (Basalt Fiber) | 670 | 1100 | |
SB-3 | 450 | 180 | 45 (Silica Fume) | - | - | 3 (Basalt Fiber) | 670 | 1100 | |
MB-1 | 450 | 180 | 45 (Metakaolin) | - | - | 1 (Basalt Fiber) | 670 | 1100 | |
MB-2 | 450 | 180 | 45 (Metakaolin) | - | - | 2 (Basalt Fiber) | 670 | 1100 | |
MB-3 | 450 | 180 | 45 (Metakaolin) | - | - | 3 (Basalt Fiber) | 670 | 1100 | |
Mohaghegh et al. [83] | S-I-0 | 495.6 | 184.4 | 63.2 (Silica Fume) | 161.1 | 12 | 0 (Basalt Fiber) | 1066.6 | 419.3 |
S-II-0 | 495.6 | 184.4 | 63.2 (Silica Fume) | 161.1 | 12 | 0 (Basalt Fiber) | 1066.6 | 419.3 | |
S-III-0.5 | 495.6 | 184.4 | 63.2 (Silica Fume) | 159.7 | 12 | 0.5 (Basalt Fiber) | 1057.7 | 415.8 | |
S-IV-1 | 495.6 | 184.4 | 63.2 (Silica Fume) | 158.4 | 12 | 1 (Basalt Fiber) | 1048.9 | 412.3 | |
S-V-1.33 | 501.0 | 184.2 | 63.9 (Silica Fume) | 175.5 | 14 | 1.33 (Basalt Fiber) | 1043 | 410 | |
S-VI-1.67 | 501.0 | 184.2 | 63.9 (Silica Fume) | 156.6 | 14 | 1.67 (Basalt Fiber) | 1037 | 407.7 | |
S-VII-2 | 501.0 | 184.2 | 63.9 (Silica Fume) | 155.7 | 14 | 2 (Basalt Fiber) | 1031.1 | 405.4 | |
Nguyen et al. [94] | A | 954.1 | 215.9 | 73.8 (Silica Fume) | - | - | 0 | 1078.2 | - |
B | 954.1 | 215.9 | 73.8 (Silica Fume) | - | - | 2 (Steel Fiber) | 1058.1 | - | |
C | 954.1 | 215.9 | 73.8 (Silica Fume) | - | - | 4 (Steel Fiber) | 1038.1 | - | |
Kharun et al. [32] | HPC0 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 0 | 585 | 1005 |
HPC06 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 0.6 (Chopped Basalt Fiber) | 585 | 1005 | |
HPC09 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 0.9 (Chopped Basalt Fiber) | 585 | 1005 | |
HPC12 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 1.2 (Chopped Basalt Fiber) | 585 | 1005 | |
HPC15 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 1.5 (Chopped Basalt Fiber) | 585 | 1005 | |
HPC18 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 1.8 (Chopped Basalt Fiber) | 585 | 1005 | |
Alaraza et al. [84] | HPC0 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 0 | 585 | 1005 |
HPC06 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 0.6 (Minibar Basalt Fiber) | 585 | 1005 | |
HPC09 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 0.9 (Minibar Basalt Fiber) | 585 | 1005 | |
HPC12 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 1.2 (Minibar Basalt Fiber) | 585 | 1005 | |
HPC15 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 1.5 (Minibar Basalt Fiber) | 585 | 1005 | |
HPC18 | 500 | 125 | 125 (Micro Silica) | 100 | 12.5 | 1.8 (Minibar Basalt Fiber) | 585 | 1005 |
3.1.2. HPC Mechanical Properties
3.2. UHPC Concrete
3.2.1. UHPC Mixture Design
Studies | Sample | Cement (kg/m3) | Water (kg/m3) | Coarse Aggregate (kg/m3) | Fine Aggregate (kg/m3) | Metacaoline (kg/m3) | Dolomite (kg/m3) | Super-Plasticizer (%) | Silica Fume (kg/m3) | Quartz Fluor (kg/m3) | Fly Ash (kg/m3) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Saji and Unnikrishnan [81] | MC | 0.26 | 523 | 141 | 1252 | 604 | - | - | 2.61 | - | - | - |
MK10 | 0.3 | 470.7 | 141 | 1252 | 604 | 52 | - | 2.61 | - | - | - | |
MK20 | 0.3 | 418 | 141 | 1252 | 604 | 104 | - | 2.61 | - | - | - | |
MK30 | 0.38 | 366 | 141 | 1252 | 604 | 156 | - | 2.61 | - | - | - | |
MK10D5 | 0.315 | 447 | 141 | 1252 | 604 | 52 | 23 | 2.61 | - | - | - | |
MK10D7.5 | 0.32 | 435 | 141 | 1252 | 604 | 52 | 35 | 2.61 | - | - | - | |
MK10D10 | 423 | 141 | 1252 | 604 | 52 | 47 | 2.61 | - | - | - | ||
MK10D12.5 | 414 | 141 | 1252 | 604 | 52 | 56 | 2.61 | - | - | - | ||
Patel et al. [106] | C0 | 0.99 | 450 | 141 | 1134 | 731 | - | - | 2.61 | - | - | - |
C1 | 450 | 141 | 1134 | 731 | 18.5 | - | 2.61 | - | - | - | ||
C2 | 450 | 141 | 1134 | 731 | 23 | - | 2.61 | - | - | - | ||
C3 | 450 | 141 | 1134 | 731 | 35 | - | 2.61 | - | - | - | ||
C4 | 450 | 141 | 1134 | 731 | - | - | 2.61 | 18.5 | - | - | ||
C5 | 450 | 141 | 1134 | 731 | - | - | 2.61 | 23 | - | - | ||
C7 | 450 | 141 | 1134 | 731 | - | - | 2.61 | 28 | - | - | ||
Ghazy et al. [109] | UHPC | 0.17 | 950 | 170 | - | 750 | - | - | 3 | 200 | 450 | - |
UHPC 1-C | 950 | 170 | - | 725 | - | - | 3 | 200 | 400 | - | ||
UHPC 1-HE | 950 | 170 | - | 725 | - | - | 3 | 200 | 400 | - | ||
UHPC 2-C | 950 | 170 | - | 700 | - | - | 3 | 200 | 350 | - | ||
UHPC 2-HE | 950 | 170 | - | 700 | - | - | 3 | 200 | 350 | - | ||
UHPC 3-C | 950 | 170 | - | 650 | - | - | 3 | 200 | 750 | - | ||
UHPC 3-HE | 950 | 170 | - | 650 | - | - | 3 | 200 | 300 | - | ||
Tahwia et al. [107] | Co | 0.27 | 450 | 125 | 1110 | 740 | - | - | 12.5 | 50 | - | - |
CO-SF | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
CO-MF | 450 | 125 | 1110 | 740 | - | - | 12.5 | 50 | - | - | ||
M1 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
M2 | 450 | 125 | 1110 | 740 | 1 | - | 12.5 | 50 | - | - | ||
M3 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
M4 | 450 | 125 | 1110 | 740 | 0.53 | - | 12.5 | 50 | - | - | ||
M5 | 450 | 125 | 1110 | 740 | 0.92 | - | 12.5 | 50 | - | - | ||
M6 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
M7 | 450 | 125 | 1110 | 740 | 0.57 | - | 12.5 | 50 | - | - | ||
M8 | 450 | 125 | 1110 | 740 | 0.97 | - | 12.5 | 50 | - | - | ||
M9 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
M10 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
M11 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
M12 | 450 | 125 | 1110 | 740 | 0.5 | - | 12.5 | 50 | - | - | ||
M13 | 450 | 125 | 1110 | 740 | 0.75 | - | 12.5 | 50 | - | - | ||
Han and Zhou [110] | A | 470 | 165 | 1000 | 500 | - | - | 4.8 | - | - | - | |
B | 0.402 | 410 | 165 | 1000 | 500 | - | - | 4.8 | - | - | 25 | |
C | 0.54 | 350 | 165 | 1000 | 500 | - | - | 4.8 | - | - | 50 | |
D | 260 | 165 | 1000 | 500 | - | - | 4.8 | - | - | 90 | ||
E | 180 | 165 | 1000 | 500 | - | - | 4.8 | - | - | 120 | ||
Zhou et al. [108] | PC-80 | 0.212 | 856 | 182 | - | 1177 | - | - | 4 | 214 | - | - |
PC-55 | 0.31 | 577 | 178 | - | 1154 | - | - | 4 | 210 | - | - | |
PC-35 | 364 | 177 | - | 1145 | - | - | 4 | 208 | - | - | ||
PC-35-NS | 359 | 177 | - | 1145 | - | - | 4 | 205 | - | - | ||
PC-35-NA | 359 | 177 | - | 1145 | - | - | 4 | 205 | - | - | ||
PC-35-AA | 369 | 177 | 155 | 1145 | - | - | 4 | 207 | - | 311 | ||
Liu et al. [111] | A 0.24 | 0.23 | 1054.6 | 242.6 | 210.9 | 1054.5 | - | - | 3 | 316.4 | - | - |
Fan et al. [112] | ST-0 | 700 | 180 | - | 1104 | - | - | 2 | 125 | - | 175 | |
ST-0.5 | 700 | 190 | - | 1104 | - | - | 2 | 125 | - | 175 | ||
ST-2 | 700 | 200 | - | 1104 | - | - | 2 | 125 | - | 175 |
3.2.2. UHPC Mechanical Properties
3.3. Differences between HPC and UHPC
3.4. Machine Learning Results
- HPC Cement element lacks correlation with all characters except UHPC. This positive correlation was due to the near closeness of cement values of both mixture types.
- UHPC and HPC water did not correlate with characters. It noted that the UHPC and HPC water had no positive or negative effect on other elements.
- UHPC aggregates had a little positive effect on UHPC compressive strength. UHPC and HPC compressive strengths had a strong positive effect together.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Studies | Specimens | Compressive Strength (MPa) | Tensile Strength (MPa) | Flexural Strength (MPa) |
---|---|---|---|---|
Ayub et al. [9] | P-0 | 88.73 | 5.16 | 5 |
PB-1 | 84.71 | 5.16 | 6.42 | |
PB-2 | 89.66 | 5.40 | 7.46 | |
PB-3 | 89.36 | 6 | 5.99 | |
S-0 | 102.37 | 6.65 | 5.66 | |
SB-1 | 103.43 | 6.71 | 6.54 | |
SB-2 | 101.3 | 6.72 | 7.16 | |
SB-3 | 100.97 | 7.99 | 6.84 | |
MB-1 | 103.43 | 5.49 | 7.09 | |
MB-2 | 101.3 | 5.89 | 7.16 | |
MB-3 | 100.97 | 7.18 | 6.86 | |
Mohaghegh et al. [83] | S-I-0 | 79.5 | - | - |
S-II-0 | 81.2 | - | - | |
S-III-0.5 | 81.6 | - | - | |
S-IV-1 | 78.6 | - | - | |
S-V-1.33 | 79.8 | - | - | |
S-VI-1.67 | 78.6 | - | - | |
S-VII-2 | 77 | - | - | |
Nguyen et al. [94] | A | 97.8 | 8.2 | - |
B | 98.5 | 8.6 | - | |
C | 99 | 12.1 | - | |
Kharun et al. [32] | HPC0 | 101.43 | 5.53 | 14 |
HPC06 | 92.78 | 5.3 | 15.6 | |
HPC09 | 92.68 | 5.29 | 17.4 | |
HPC12 | 102.3 | 5.56 | 18.9 | |
HPC15 | 97.6 | 5.41 | 18.1 | |
HPC18 | 95.68 | 5.37 | 18.3 | |
Alaraza et al. [84] | HPC0 | 101.43 | - | 14.1 |
HPC06 | 101.43 | - | 16.8 | |
HPC09 | 105.39 | - | 19.8 | |
HPC12 | 90.50 | - | 17.2 | |
HPC15 | 89.51 | - | 16.4 | |
HPC18 | 92.30 | - | 17.1 |
Studies | Sample | Compressive Strength (MPa) | Tensile Strength (MPa) | Flexural Strength (MPa) |
---|---|---|---|---|
Saji and Unnikrishnan [81] | MC | 71.11 | 5.94 | 7.6 |
MK10 | 72.88 | 6.22 | 8.4 | |
MK20 | 71.15 | 5.62 | 8 | |
MK30 | 69.77 | 5.37 | 7.2 | |
MK10D5 | 73.77 | 6.08 | 8.8 | |
MK10D7.5 | 74.66 | 6.29 | 8.4 | |
MK10D10 | 72.44 | 5.98 | 7.2 | |
MK10D12.5 | 70.22 | 5.85 | 6.4 | |
Patel et al. [106] | C0 | 62.73 | - | - |
C1 | 64.3 | - | - | |
C2 | 70.66 | - | - | |
C3 | 66.85 | - | - | |
C4 | 62.33 | - | - | |
C5 | 65.5 | - | - | |
C7 | 63.66 | - | - | |
Ghazy et al. [109] | UHPC | 120 | 9 | 18.66 |
UHPC 1-C | 130 | 10.5 | 22.65 | |
UHPC 1-HE | 127 | 9.85 | 21.66 | |
UHPC 2-C | 138 | 11.75 | 28.66 | |
UHPC 2-HE | 132 | 11 | 26.02 | |
UHPC 3-C | 150 | 12.55 | 30 | |
UHPC 3-HE | 139 | 11.55 | 28 | |
Tahwia et al. [107] | Co | 67.2 | - | 10.5 |
CO-SF | 67.6 | - | 10.8 | |
CO-MF | 68 | - | 11 | |
M1 | 72 | - | 13 | |
M2 | 73 | - | 13.6 | |
M3 | 72 | - | 13 | |
M4 | 74 | - | 13.9 | |
M5 | 76 | - | 14.5 | |
M6 | 76.8 | - | 14.9 | |
M7 | 70 | - | 12 | |
M8 | 71 | - | 12.4 | |
M9 | 72 | - | 13 | |
M10 | 69 | - | 12.2 | |
M11 | 72 | - | 13 | |
M12 | 72 | - | 12.5 | |
M13 | 72 | - | 13 | |
Han and Zhou [110] | A | 46 | - | 6.1 |
B | 46 | - | 5.8 | |
C | 43 | - | 5.2 | |
D | 41 | - | 4.8 | |
E | 39 | - | 4.6 | |
Zhou et al. [108] | PC-80 | - | 7.3 | - |
PC-55 | - | 4.7 | - | |
PC-35 | - | 4.2 | - | |
PC-35-NS | - | 4.7 | - | |
PC-35-NA | - | 7 | - | |
PC-35-AA | - | 6.7 | - | |
Liu et al. [111] | A | 84.9 | 6.9 | - |
Fan et al. [112] | ST-0 | 145 | - | - |
ST-0.5 | 157 | - | - | |
ST-2 | 132 | - | - |
UHPC Cement | UHPC Water | UHPC Aggregates | HPC Cement | HPC Water | HPC Aggregates | UHPC Compressive Strength | HPC Compressive Strength | |
---|---|---|---|---|---|---|---|---|
Mean | 548.4 | 213.08 | 1007.1 | 550.5 | 235.4 | 1460.4 | 116.74 | 116.72 |
Std. | 30.1 | 18.7 | 61.03 | 28.99 | 9.0 | 23.7 | 2.87 | 2.96 |
Min | 436 | 180 | 900 | 500 | 220 | 1420 | 110 | 109 |
25% | 523 | 200 | 956 | 526.25 | 228 | 1420 | 115 | 115 |
50% | 552 | 213 | 1009 | 552 | 236 | 1461 | 117 | 117 |
75% | 574.75 | 225 | 1056.75 | 573 | 243 | 1482.75 | 119 | 119 |
Max | 608 | 264 | 1110 | 600 | 250 | 1500 | 125 | 125 |
Regression Types | RMSE | MAE | R2 |
---|---|---|---|
Linear Regression | 0.89 | 0.68 | 0.92 |
Lasso Regression | 1.42 | 1.17 | 0.79 |
Ridge Regression | 0.85 | 0.68 | 0.92 |
Random forest Regression | 0.94 | 0.75 | 0.91 |
K Neighbors Regression | 1.46 | 0.75 | 0.77 |
Decision tree Regression | 1.16 | 1.16 | 0.86 |
PLS Regression | 0.81 | 0.66 | 0.93 |
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Hematibahar, M.; Kharun, M.; Beskopylny, A.N.; Stel’makh, S.A.; Shcherban’, E.M.; Razveeva, I. Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning. J. Compos. Sci. 2024, 8, 287. https://doi.org/10.3390/jcs8080287
Hematibahar M, Kharun M, Beskopylny AN, Stel’makh SA, Shcherban’ EM, Razveeva I. Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning. Journal of Composites Science. 2024; 8(8):287. https://doi.org/10.3390/jcs8080287
Chicago/Turabian StyleHematibahar, Mohammad, Makhmud Kharun, Alexey N. Beskopylny, Sergey A. Stel’makh, Evgenii M. Shcherban’, and Irina Razveeva. 2024. "Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning" Journal of Composites Science 8, no. 8: 287. https://doi.org/10.3390/jcs8080287
APA StyleHematibahar, M., Kharun, M., Beskopylny, A. N., Stel’makh, S. A., Shcherban’, E. M., & Razveeva, I. (2024). Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning. Journal of Composites Science, 8(8), 287. https://doi.org/10.3390/jcs8080287