Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models
Abstract
:1. Introduction
2. Methods
2.1. Artificial Neural Network
2.2. Particle Swarm Optimization
2.3. Imperialism Competitive Algorithm
2.4. Metaheuristic-Based ANN Models
3. Data Source and Input Parameters
3.1. Background
3.2. Data Source
4. CFST Prediction
5. Results and Discussion
6. Sensitivity Analysis
7. Limitations and Future Investigations
8. Conclusions
- Both metaheuristic-based ANN approaches performed an acceptable performance in the prediction phase, as they were able to offer values for bearing capacity close to those measured in the laboratory.
- This study’s results indicated that the PSO-ANN model performed much better than the ICA-ANN model in both the model construction and evaluation processes. R2 values of 0.936 and 0.873 for PSO-ANN and ICA-ANN modelsindicate that PSO is more powerful than the ICA method at determining the optimal weights and biases of ANN.
- The comparison between measured bearing capacities together with those predicted by metaheuristic-based ANN models as well as different standards showed that both POS-ANN and ICA-ANN models are more accurate compared to available standards. This confirmed that such intelligent techniques are needed to be used in order to obtain closer bearing capacities to the measured values.
- Results of feature importance indicated that fy and T, with significant correlations of 0.457 and 0.432 respectively, have the highest effects on the bearing capacity of SCFST columns. Therefore, a higher level of care regarding these parameters and their designs should be considered in the laboratory while tests are planned and conducted.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Min | Max | Average | Std. Dev |
---|---|---|---|---|
fc (MPa) | 10 | 164 | 55.66 | 35.25 |
B (mm) | 50.8 | 450 | 208.55 | 106.80 |
L (mm) | 210 | 2540 | 937.10 | 575.45 |
T (mm) | 1.94 | 18 | 5.90 | 3.70 |
fy (MPa) | 229 | 1030.60 | 394.60 | 184.25 |
Pexp (kN) | 329 | 8912 | 2139 | 1802.60 |
Parameter | Min | Max | Average | Std. Dev |
---|---|---|---|---|
fc (MPa) | 20 | 130.8 | 47.70 | 24 |
B (mm) | 80 | 450 | 198.50 | 94.90 |
L (mm) | 295 | 2340 | 927.75 | 530.10 |
T (mm) | 1.94 | 11.25 | 5.10 | 2.40 |
fy (MPa) | 231 | 1030.60 | 377.20 | 184.60 |
Pexp (kN) | 490 | 3922 | 1588 | 843.70 |
Metaheuristic-Based ANN Model | Training | ||
---|---|---|---|
VAF (%) | R2 | RMSE | |
ICA-ANN | 84.873 | 0.855 | 0.097 |
PSO-ANN | 90.549 | 0.908 | 0.077 |
Model | Testing | ||
ICA-ANN | 87.264 | 0.873 | 0.085 |
PSO-ANN | 93.497 | 0.936 | 0.059 |
Model | Training + Testing | ||
ICA-ANN | 85.296 | 0.857 | 0.094 |
PSO-ANN | 91.125 | 0.913 | 0.074 |
No. | PExp | PPSO-ANN | PICA-ANN | PEC4 | PACI |
---|---|---|---|---|---|
(kN) | (kN) | (kN) | (kN) | (kN) | |
1 | 2275 | 2230 | 2163 | 2785 | 2520 |
2 | 1760 | 1625 | 1577 | 2751 | 2418 |
3 | 2985 | 2823 | 2738 | 2666 | 2415 |
4 | 3900 | 3723 | 3612 | 3441 | 3073 |
5 | 768 | 845 | 680 | 660 | 656 |
6 | 1426 | 1403 | 1361 | 1176 | 1059 |
7 | 1302 | 1445 | 1464 | 1136 | 1025 |
8 | 990 | 1007 | 1018 | 923 | 858 |
9 | 965 | 854 | 829 | 826 | 775 |
10 | 890 | 895 | 868 | 783 | 738 |
11 | 1530 | 1552 | 1505 | 1240 | 1127 |
12 | 1367 | 1355 | 1314 | 1202 | 1094 |
13 | 1088 | 971 | 942 | 940 | 932 |
14 | 1176 | 1269 | 1290 | 994 | 930 |
15 | 1160 | 1042 | 1011 | 900 | 851 |
16 | 1090 | 923 | 896 | 858 | 815 |
17 | 1630 | 1841 | 1890 | 1299 | 1190 |
18 | 1484 | 1592 | 1602 | 1262 | 1159 |
19 | 934 | 849 | 824 | 601 | 560 |
20 | 1934 | 2145 | 2242 | 1502 | 1477 |
21 | 2828 | 2995 | 3109 | 2445 | 2383 |
22 | 2238 | 2279 | 2300 | 2517 | 2284 |
23 | 956 | 1029 | 1070 | 1205 | 1127 |
24 | 3302.4 | 3450 | 3556 | 3666 | 3502 |
25 | 3203.8 | 3256 | 3280 | 3666 | 3502 |
26 | 3611.6 | 3523 | 3418 | 4383 | 4112 |
27 | 3474 | 3240 | 3120 | 4988 | 4695 |
28 | 840 | 732 | 702 | 572 | 553 |
29 | 860 | 799 | 775 | 619 | 593 |
30 | 1575 | 1592 | 1545 | 1404 | 1313 |
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Sarir, P.; Armaghani, D.J.; Jiang, H.; Sabri, M.M.S.; He, B.; Ulrikh, D.V. Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models. Materials 2022, 15, 3309. https://doi.org/10.3390/ma15093309
Sarir P, Armaghani DJ, Jiang H, Sabri MMS, He B, Ulrikh DV. Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models. Materials. 2022; 15(9):3309. https://doi.org/10.3390/ma15093309
Chicago/Turabian StyleSarir, Payam, Danial Jahed Armaghani, Huanjun Jiang, Mohanad Muayad Sabri Sabri, Biao He, and Dmitrii Vladimirovich Ulrikh. 2022. "Prediction of Bearing Capacity of the Square Concrete-Filled Steel Tube Columns: An Application of Metaheuristic-Based Neural Network Models" Materials 15, no. 9: 3309. https://doi.org/10.3390/ma15093309