Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques
Abstract
:1. Introduction
2. Methodology
2.1. CFRP Confined/Wrapped Concrete Data Collection
2.2. CFRP Confined/Wrapped Concrete Data Collection
- -
- d specimen diameter (mm);
- -
- L specimen length (mm);
- -
- Fco compression strength of unwrapped concrete cylinder (MPa);
- -
- t the thickness of the used CFRP sheets (mm);
- -
- Ftf tension capacity of the used CFRP sheets (MPa);
- -
- Ef modulus of elasticity of the used CFRP sheets (GPa);
- -
- Fcc compression strength of wrapped concrete cylinder (MPa).
d | L | Fco | t | Ftf | Ef | Fcc | |
---|---|---|---|---|---|---|---|
mm | mm | MPa | mm | MPa | GPa | MPa | |
Training set | |||||||
Min. | 100.0 | 200.0 | 18.0 | 0.1 | 365.0 | 19.0 | 36.5 |
Max. | 300.0 | 610.0 | 63.0 | 5.3 | 4198.0 | 629.6 | 161.3 |
Avg. | 149.5 | 304.3 | 38.3 | 0.7 | 2374.1 | 173.0 | 71.6 |
SD | 31.3 | 73.4 | 10.4 | 0.7 | 1353.9 | 109.4 | 22.2 |
VAR | 0.2 | 0.2 | 0.3 | 1.1 | 0.6 | 0.6 | 0.3 |
Validation set | |||||||
Min. | 100.0 | 200.0 | 18.0 | 0.1 | 167.0 | 13.0 | 41.3 |
Max. | 300.0 | 600.0 | 52.2 | 3.0 | 4198.0 | 420.0 | 129.0 |
Avg. | 153.3 | 307.0 | 35.1 | 0.7 | 2370.5 | 160.5 | 65.6 |
SD | 28.0 | 56.0 | 7.0 | 0.8 | 1386.3 | 97.6 | 17.0 |
VAR | 0.2 | 0.2 | 0.2 | 1.1 | 0.6 | 0.6 | 0.3 |
d | L | Fco | t | Ftf | Ef | Fcc | |
---|---|---|---|---|---|---|---|
d | 1.00 | ||||||
L | 0.88 | 1.00 | |||||
Fco | −0.09 | −0.14 | 1.00 | ||||
t | 0.10 | 0.15 | 0.05 | 1.00 | |||
Ftf | 0.04 | −0.04 | −0.20 | −0.60 | 1.00 | ||
Ef | −0.12 | −0.14 | −0.26 | −0.54 | 0.67 | 1.00 | |
Fcc | −0.08 | −0.10 | 0.26 | 0.30 | 0.13 | −0.01 | 1.00 |
2.3. Predictive Models
3. Results and Discussion
3.1. General Behavior of the Wrapped Concrete Column
3.2. Prediction of Fcc Values
3.2.1. ANN Approaches
Hidden Layer | ||||||||
---|---|---|---|---|---|---|---|---|
Input Layer | H (1:1) | H (1:2) | H (1:3) | H (1:4) | H (1:5) | H (1:6) | ||
(Bias) | 2.298 | 1.054 | 0.046 | −1.003 | 0.171 | 0.160 | ||
d | −0.755 | 0.023 | −0.576 | 0.460 | −0.086 | 0.269 | ||
L | −0.119 | −0.022 | 0.113 | 1.274 | 0.488 | 0.383 | ||
Fco | 0.326 | 0.001 | −0.969 | −0.399 | 0.096 | −1.868 | ||
t | −1.134 | 0.928 | −0.270 | 0.308 | 0.009 | −0.590 | ||
Ftf | −0.651 | −0.141 | −0.045 | 0.542 | 0.892 | 0.749 | ||
Ef | −0.814 | 0.067 | 0.695 | 0.304 | −0.577 | 0.139 | ||
Output | Hidden Layer | |||||||
H (1:1) | H (1:2) | H (1:3) | H (1:4) | H (1:5) | H (1:6) | (Bias) | ||
Fcc | −1.724 | 3.071 | 1.948 | −2.161 | 2.409 | −0.987 | −2.656 |
Hidden Layer | ||||||||
---|---|---|---|---|---|---|---|---|
Input Layer | H (1:1) | H (1:2) | H (1:3) | H (1:4) | H (1:5) | H (1:6) | ||
(Bias) | −2.118 | −0.155 | 1.590 | 0.088 | 3.604 | −1.593 | ||
d | −0.325 | 0.173 | −1.436 | 1.302 | 0.182 | −0.595 | ||
L | −0.480 | 1.432 | −1.026 | 1.526 | −0.724 | 0.103 | ||
Fco | −0.267 | 0.058 | −0.088 | 0.083 | 0.072 | −1.264 | ||
t | 1.068 | −2.178 | 3.266 | −0.191 | 3.324 | −0.916 | ||
Ftf | −1.997 | −0.249 | 0.269 | −0.724 | −0.156 | −2.875 | ||
Ef | −0.484 | −0.173 | 1.742 | −0.050 | −0.238 | −2.793 | ||
Output | Hidden Layer | |||||||
H (1:1) | H (1:2) | H (1:3) | H (1:4) | H (1:5) | H (1:6) | (Bias) | ||
Fcc | −1.690 | −3.748 | −1.952 | 0.658 | 3.709 | −0.932 | −2.788 |
Hidden Layer | ||||||||
---|---|---|---|---|---|---|---|---|
Input Layer | H (1:1) | H (1:2) | H (1:3) | H (1:4) | H (1:5) | H (1:6) | ||
(Bias) | 1.703 | −3.786 | −1.735 | 3.387 | −7.024 | −3.192 | ||
d | 4.490 | 2.758 | 0.693 | 2.336 | 3.055 | 3.803 | ||
L | 5.030 | 6.018 | 0.230 | 1.216 | 6.178 | 4.085 | ||
Fco | −4.781 | −2.707 | 0.062 | −5.521 | −0.220 | 2.318 | ||
t | −5.914 | −5.006 | −14.219 | −4.898 | 0.185 | 1.355 | ||
Ftf | 0.126 | 3.619 | 0.918 | −0.835 | 10.729 | 11.586 | ||
Ef | 0.101 | 2.688 | −1.512 | 0.328 | −6.343 | 1.150 | ||
Output | Hidden Layer | |||||||
H (1:1) | H (1:2) | H (1:3) | H (1:4) | H (1:5) | H (1:6) | (Bias) | ||
Fcc | −3.293 | −6.103 | −12.682 | 3.349 | 6.661 | 6.336 | −6.252 |
3.2.2. GP Approach
3.2.3. EPR Approach
GP | ANN-BP | ANN-GRG | ANN-GA | EPR | ||
---|---|---|---|---|---|---|
MAE | Training | 6.29 | 3.24 | 3.29 | 3.51 | 4.22 |
Validation | 5.37 | 3.29 | 2.71 | 3.26 | 3.85 | |
RMSE | Training | 7.82 | 4.15 | 4.27 | 4.67 | 5.22 |
Validation | 6.84 | 4.13 | 3.38 | 3.93 | 4.77 | |
RRMSE % | Training | 10.91 | 5.79 | 5.97 | 6.52 | 7.28 |
Validation | 10.43 | 6.30 | 5.16 | 5.99 | 7.28 | |
MAPE% | Training | 9.13 | 4.93 | 4.86 | 5.13 | 6.31 |
Validation | 8.27 | 5.04 | 4.27 | 4.94 | 5.97 | |
SSE | Training | 7882 | 2220 | 2356 | 2815 | 3511 |
Validation | 1635 | 596 | 401 | 540 | 798 | |
Error % | Training | 10.91 | 5.79 | 5.97 | 6.52 | 7.28 |
Validation | 10.43 | 6.30 | 5.16 | 5.99 | 7.28 | |
R2 | Training | 0.878 | 0.966 | 0.963 | 0.956 | 0.945 |
Validation | 0.843 | 0.944 | 0.962 | 0.950 | 0.924 |
4. Conclusions
- -
- The GP model is the simplest but also the least accurate. Its accuracy is about 90%. The EPR model came next as a more complicated and more accurate model, with an accuracy of about 92.5%. The three ANN models presented more complicated and more accurate models with almost the same level of accuracy (94%).
- -
- The outcomes showed that the training algorithm of the ANN model slightly affected its accuracy. Back propagation (BP) and gradually reduced gradient (GRG) showed almost the same level of accuracy, while the genetic algorithm (GA) showed a lower level of accuracy.
- -
- Although the ANN models are more accurate than the GP and EPR models, the high complexity of the ANN models make them suitable for computerized calculations. On the other hand, the closed-form equations of GP and EPR could be used manually.
- -
- The summation of the absolute weights of each neuron in the input layer of the developed ANN models indicates that FRP properties (t and Ftf) are the most important factors, while the factors of unwrapped compressive strength (Fco) and sample dimensions (d and L) have a much lower influence.
- -
- The modulus of elasticity of CFRP (Ef) was not used in the GP model nor in the EPR model, which indicates its insignificant impact on Fcc compared to other parameters.
- -
- Because the ratio (L/d) is almost constant and equals 2.0 in all the database records, these two parameters are dependent, and they both have the same importance; hence, the appearance of (L) only in the GP model is quite enough for both of them and captures the effect of size on the Fcc.
- -
- Using the GA approach to reduce the possible 28 terms of traditional polynomial regression to only 15 terms was successful without reducing the accuracy.
- -
- All the developed predictive models are valid within the used ranges of input parameters, and they should be verified beyond that.
- -
- Generally, the closed-form equations proposed in this research work became decisive models in designing structural members belonging to this group of concrete columns jacketed with CFRP, with less need for the laboratory.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
d | L | Fco | t | Ftf | Ef | Fcc |
---|---|---|---|---|---|---|
mm | mm | Mpa | mm | Mpa | Gpa | Mpa |
Training set | ||||||
152 | 305 | 55.2 | 0.38 | 1577 | 105 | 57.9 |
100 | 200 | 25.9 | 0.167 | 3591 | 242 | 66.4 |
150 | 300 | 29.2 | 0.33 | 3788 | 225.7 | 88.2 |
152 | 305 | 38.6 | 0.31 | 755 | 73.3 | 41.9 |
152 | 610 | 26.2 | 2 | 580 | 87.3 | 64 |
100 | 200 | 25.9 | 0.167 | 3591 | 242 | 64.8 |
152 | 305 | 43.8 | 0.76 | 1577 | 105 | 85 |
152 | 305 | 38.6 | 1.22 | 388 | 27.7 | 49.3 |
152 | 305 | 55.2 | 0.38 | 1577 | 105 | 62.9 |
152 | 305 | 43.8 | 1.14 | 1577 | 105 | 94 |
152 | 305 | 35.9 | 0.165 | 4198 | 250.5 | 53.2 |
150 | 300 | 34.3 | 1 | 753 | 91 | 59.4 |
100 | 200 | 51.9 | 0.22 | 3481 | 230.5 | 104.6 |
152 | 305 | 38.6 | 0.92 | 822 | 54 | 64.7 |
152 | 305 | 41.1 | 0.165 | 3800 | 250.5 | 55.4 |
152 | 305 | 44.2 | 0.22 | 3762 | 260 | 62.9 |
152 | 305 | 18 | 5.31 | 513 | 35.9 | 82.23 |
160 | 320 | 61.81 | 1 | 450 | 34 | 62.68 |
152 | 305 | 43.8 | 1.14 | 1577 | 105 | 92.6 |
100 | 200 | 26.3 | 0.22 | 3481 | 230.5 | 70.9 |
152 | 305 | 33.7 | 1.14 | 1577 | 105 | 86.2 |
160 | 320 | 49.46 | 3 | 450 | 34 | 82.91 |
100 | 200 | 25.9 | 0.167 | 3591 | 242 | 63 |
150 | 300 | 52.3 | 0.33 | 3788 | 225.7 | 100 |
150 | 300 | 25.6 | 0.165 | 3550 | 235 | 43.9 |
152 | 305 | 38.6 | 0.92 | 1105 | 77.5 | 76.4 |
150 | 300 | 34.3 | 1 | 423 | 37 | 44.2 |
100 | 200 | 30.2 | 0.17 | 2716 | 224.6 | 46.6 |
152 | 305 | 43.8 | 1.14 | 1577 | 105 | 96.5 |
160 | 320 | 49.46 | 1 | 450 | 34 | 52.75 |
152 | 305 | 55.2 | 0.76 | 1577 | 105 | 74.6 |
100 | 200 | 26.3 | 0.11 | 3481 | 230.5 | 50.7 |
152 | 305 | 38 | 1.02 | 3615 | 240.7 | 135.7 |
300 | 600 | 24.5 | 0.501 | 3591 | 242 | 63 |
150 | 300 | 31.2 | 0.11 | 3481 | 230.5 | 52.4 |
152 | 305 | 38 | 0.68 | 3615 | 240.7 | 110.1 |
152 | 305 | 38.6 | 0.61 | 660 | 39.9 | 47.1 |
152 | 305 | 33.7 | 0.38 | 1577 | 105 | 49.4 |
150 | 300 | 45.2 | 0.22 | 3481 | 230.5 | 79.4 |
100 | 200 | 30.2 | 0.5 | 2873 | 224.6 | 87.2 |
152 | 305 | 35.9 | 0.33 | 4198 | 250.5 | 68.7 |
152 | 305 | 34.3 | 0.495 | 4198 | 250.5 | 97.3 |
152 | 305 | 43.8 | 0.76 | 1577 | 105 | 84 |
152 | 305 | 38.6 | 1.22 | 388 | 27.7 | 52.7 |
152 | 305 | 18 | 1.55 | 1353 | 96 | 82.23 |
152 | 305 | 38.38 | 0.33 | 795 | 72.4 | 44.87 |
152 | 305 | 18 | 2.26 | 978 | 62.5 | 79.49 |
152 | 305 | 55.2 | 1.14 | 1577 | 105 | 108 |
152 | 305 | 38.6 | 1.22 | 1352 | 95.7 | 89.5 |
152 | 305 | 35.9 | 0.33 | 4198 | 250.5 | 71.6 |
152 | 305 | 37.7 | 0.11 | 3905 | 260 | 50.3 |
100 | 200 | 30.2 | 0.42 | 1285 | 576.6 | 63.3 |
152 | 305 | 41.1 | 0.165 | 3800 | 250.5 | 52.6 |
150 | 300 | 51.7 | 0.165 | 3788 | 225.7 | 69.2 |
152 | 610 | 26.2 | 1 | 580 | 97.1 | 50.6 |
152 | 305 | 38.38 | 1.32 | 1352 | 95.7 | 89.48 |
150 | 300 | 29.8 | 0.165 | 3550 | 235 | 57 |
152 | 305 | 38 | 1.36 | 3615 | 240.7 | 161.3 |
152 | 305 | 38 | 0.68 | 3615 | 240.7 | 107.4 |
160 | 320 | 63.01 | 3 | 450 | 34 | 94.81 |
100 | 200 | 33.7 | 0.33 | 3481 | 230.5 | 109.9 |
152 | 305 | 38.6 | 1.22 | 1352 | 95.7 | 89.9 |
100 | 200 | 30.2 | 0.67 | 2658 | 224.6 | 104.6 |
152 | 305 | 33.7 | 0.76 | 1577 | 105 | 64.6 |
160 | 320 | 25.93 | 1 | 450 | 34 | 39.63 |
152 | 305 | 33.7 | 0.38 | 1577 | 105 | 47.9 |
160 | 320 | 63.01 | 1 | 450 | 34 | 76.21 |
152 | 305 | 33.7 | 0.76 | 1577 | 105 | 71.8 |
100 | 200 | 26.3 | 0.33 | 3481 | 230.5 | 84.9 |
152 | 305 | 43.8 | 0.38 | 1577 | 105 | 52.1 |
152 | 305 | 34.3 | 0.495 | 4198 | 250.5 | 90.4 |
152 | 305 | 33.7 | 0.38 | 1577 | 105 | 49.7 |
152 | 305 | 38.6 | 1.22 | 1352 | 95.7 | 89 |
152 | 305 | 44.2 | 0.22 | 3762 | 260 | 65.7 |
152 | 305 | 47.6 | 0.33 | 3757 | 250.5 | 85.5 |
100 | 200 | 33.7 | 0.11 | 3481 | 230.5 | 69.6 |
152 | 305 | 38 | 1.36 | 3615 | 240.7 | 158.5 |
152 | 305 | 55.2 | 0.76 | 1577 | 105 | 77.6 |
152 | 305 | 43.8 | 0.76 | 1577 | 105 | 79.2 |
150 | 300 | 25.6 | 0.33 | 3550 | 235 | 59.6 |
150 | 300 | 52.2 | 0.33 | 3788 | 225.7 | 103 |
160 | 320 | 61.81 | 3 | 450 | 34 | 93.19 |
150 | 300 | 31.3 | 0.165 | 3788 | 225.7 | 52.3 |
152 | 305 | 47.6 | 0.33 | 3757 | 250.5 | 85.5 |
152 | 305 | 55.2 | 1.14 | 1577 | 105 | 103.3 |
152 | 305 | 35.9 | 0.33 | 4198 | 250.5 | 69.9 |
152 | 305 | 38.6 | 0.61 | 1047 | 70.6 | 56.5 |
100 | 200 | 42 | 0.6 | 1265 | 82.7 | 73.5 |
150 | 300 | 34.9 | 0.24 | 1100 | 420 | 40.7 |
152 | 305 | 44.2 | 0.11 | 3905 | 260 | 48.1 |
300 | 600 | 24.5 | 0.501 | 3591 | 242 | 60.6 |
152 | 305 | 35.9 | 0.165 | 4198 | 250.5 | 47.2 |
152 | 305 | 38.6 | 0.92 | 1105 | 77.5 | 80.9 |
152 | 305 | 47.6 | 0.33 | 3757 | 250.5 | 82.7 |
150 | 300 | 23.6 | 0.11 | 3481 | 230.5 | 36.5 |
200 | 400 | 22.7 | 0.334 | 3591 | 242 | 66.3 |
160 | 320 | 58.24 | 3 | 450 | 34 | 100.41 |
100 | 200 | 25.9 | 0.167 | 3591 | 242 | 64.3 |
152 | 305 | 38.38 | 0.99 | 1105 | 77.4 | 77.68 |
100 | 200 | 30.2 | 0.14 | 1579 | 628.6 | 41.7 |
152 | 305 | 55.2 | 1.14 | 1577 | 105 | 106.5 |
200 | 400 | 22.7 | 0.334 | 3591 | 242 | 64.3 |
152 | 305 | 38.6 | 0.31 | 755 | 73.3 | 47.2 |
152 | 305 | 34.3 | 0.495 | 4198 | 250.5 | 82.6 |
152 | 305 | 34.3 | 0.165 | 4198 | 250.5 | 50.3 |
100 | 200 | 51.9 | 0.11 | 3481 | 230.5 | 75.2 |
152 | 305 | 34.3 | 0.165 | 4198 | 250.5 | 56.7 |
200 | 400 | 22.7 | 0.334 | 3591 | 242 | 69.1 |
152 | 305 | 38.9 | 0.33 | 3754 | 247 | 65.8 |
150 | 300 | 52.3 | 0.165 | 3788 | 225.7 | 68 |
200 | 400 | 22.7 | 0.334 | 3591 | 242 | 60.1 |
152 | 305 | 38.6 | 1.22 | 388 | 27.7 | 52.6 |
300 | 600 | 24.5 | 0.501 | 3591 | 242 | 59.4 |
152 | 305 | 38.6 | 0.92 | 1105 | 77.5 | 75.8 |
150 | 300 | 29.8 | 0.33 | 3550 | 235 | 72.1 |
100 | 200 | 33.7 | 0.22 | 3481 | 230.5 | 88 |
100 | 200 | 30.2 | 0.28 | 1824 | 629.6 | 56 |
150 | 300 | 31.2 | 0.22 | 3481 | 230.5 | 67.4 |
152 | 305 | 55.2 | 0.76 | 1577 | 105 | 77 |
152 | 305 | 38.38 | 0.66 | 1047 | 138.1 | 59.68 |
152 | 305 | 43.8 | 0.38 | 1577 | 105 | 54.7 |
152 | 305 | 34.3 | 0.165 | 4198 | 250.5 | 50 |
152 | 305 | 38.6 | 0.31 | 755 | 73.3 | 45.5 |
150 | 300 | 34.9 | 0.12 | 2600 | 200 | 42.2 |
150 | 300 | 51.7 | 0.33 | 3788 | 225.7 | 94.9 |
152 | 305 | 55.2 | 0.38 | 1577 | 105 | 58.1 |
160 | 320 | 58.24 | 1 | 450 | 34 | 77.51 |
152 | 305 | 38.6 | 0.61 | 660 | 39.9 | 50 |
150 | 300 | 34.3 | 2.44 | 365 | 19 | 62.5 |
Validation set | ||||||
152 | 305 | 38.9 | 0.33 | 3754 | 247 | 76.8 |
300 | 600 | 24.5 | 0.501 | 3591 | 242 | 58.8 |
152 | 305 | 38 | 1.02 | 3615 | 240.7 | 129 |
160 | 320 | 25.93 | 3 | 450 | 34 | 66.14 |
152 | 305 | 37.7 | 0.11 | 3905 | 260 | 48.5 |
150 | 300 | 23.6 | 0.33 | 3481 | 230.5 | 64.3 |
150 | 300 | 45.2 | 0.11 | 3481 | 230.5 | 59.4 |
160 | 320 | 29.51 | 1 | 450 | 34 | 49.88 |
100 | 200 | 42 | 0.6 | 1265 | 82.7 | 67.6 |
150 | 300 | 31.2 | 0.33 | 3481 | 230.5 | 81.7 |
152 | 305 | 38.9 | 0.33 | 3754 | 247 | 79.1 |
150 | 300 | 34.9 | 0.24 | 1100 | 420 | 41.3 |
152 | 305 | 35.9 | 0.165 | 4198 | 250.5 | 50.4 |
152 | 305 | 41.1 | 0.165 | 3800 | 250.5 | 57 |
150 | 300 | 34.3 | 2.83 | 167 | 13 | 47.5 |
150 | 300 | 31.3 | 0.33 | 3788 | 225.7 | 80.6 |
152 | 305 | 38.6 | 0.92 | 822 | 54 | 68.3 |
150 | 300 | 23.6 | 0.22 | 3481 | 230.5 | 50.8 |
152 | 305 | 33.7 | 1.14 | 1577 | 105 | 82.9 |
150 | 300 | 52.2 | 0.165 | 3788 | 225.7 | 66.5 |
160 | 320 | 29.51 | 3 | 450 | 34 | 71.35 |
152 | 305 | 18 | 2.06 | 1127 | 150 | 70.58 |
150 | 300 | 34.9 | 0.12 | 2600 | 200 | 44.3 |
152 | 305 | 33.7 | 1.14 | 1577 | 105 | 95.4 |
152 | 305 | 43.8 | 0.38 | 1577 | 105 | 48.7 |
150 | 300 | 29.2 | 0.165 | 3788 | 34 | 53.8 |
152 | 305 | 38.6 | 0.61 | 660 | 39.9 | 47.7 |
152 | 305 | 38.6 | 0.92 | 822 | 54 | 67.3 |
150 | 300 | 32.2 | 0.165 | 3788 | 225.7 | 61.2 |
152 | 305 | 33.7 | 0.76 | 1577 | 105 | 75.2 |
152 | 305 | 38.6 | 0.61 | 1047 | 70.6 | 61.9 |
100 | 200 | 42 | 0.6 | 1265 | 82.7 | 73.5 |
152 | 305 | 44.2 | 0.11 | 3905 | 260 | 51.1 |
152 | 305 | 38.6 | 0.61 | 1047 | 70.6 | 60.6 |
150 | 300 | 32.2 | 0.33 | 3788 | 225.7 | 85.6 |
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Onyelowe, K.C.; Jayabalan, J.; Ebid, A.M.; Samui, P.; Singh, R.P.; Soleymani, A.; Jahangir, H. Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques. Designs 2022, 6, 112. https://doi.org/10.3390/designs6060112
Onyelowe KC, Jayabalan J, Ebid AM, Samui P, Singh RP, Soleymani A, Jahangir H. Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques. Designs. 2022; 6(6):112. https://doi.org/10.3390/designs6060112
Chicago/Turabian StyleOnyelowe, Kennedy C., Jagan Jayabalan, Ahmed M. Ebid, Pijush Samui, Rahul Pratap Singh, Atefeh Soleymani, and Hashem Jahangir. 2022. "Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques" Designs 6, no. 6: 112. https://doi.org/10.3390/designs6060112