Machine Learning-Based Strength Prediction of Round-Ended Concrete-Filled Steel Tube
Abstract
1. Introduction
2. Database Establishment
2.1. Finite Element Model
2.1.1. Selection of Elements and Mesh Division
2.1.2. Material and Constitutive Properties
- (1)
- Concrete
- (2)
- Steel
2.1.3. Contact and Boundary Conditions
2.2. Verification of FE Model
2.3. Establishment of Machine Learning Database
3. Machine Learning
3.1. Sensitivity Analysis
3.2. Selected Algorithm
3.2.1. Gradient Boosting Regression (GBR)
3.2.2. Random Forest Regression (RFR)
3.2.3. Extreme Gradient Boosting (XGB)
3.3. Evaluation Metrics and Hyperparameter Optimization
3.3.1. Evaluation Metrics
- (1)
- Coefficient of determination (R2)
- (2)
- Mean absolute error (MAE)
- (3)
- Root mean square error (RMSE)
- (4)
- Mean (MEAN)
- (5)
- Coefficient of variation (COV)
3.3.2. Hyperparameter Optimization
3.4. Comparison of Results
4. Comparison with Existing Calculation Methods
- (1)
- Ding’s formula
- (2)
- Ren’s formula
- (3)
- M.F. Hassanein’s formula
- (4)
- Formula of code ACI 318-11
- (5)
- Formula of code AISC 360-16
- (6)
- Formula of code GB 50936-2014
5. Conclusions
- (1)
- Using the finite element method, a machine learning database comprising 2400 RECFSTs is established. This database encompasses commonly used material strengths and cross-sectional dimensions of RECFST, effectively addressing the issue of insufficient experimental sample size in the current machine learning practices.
- (2)
- Through sensitive analysis, in addition to the primary parameter, the second parameters such as Acfcu, Ac + As, Ac, As, and Asfy also show a large correlation with the axial load-bearing capacity Nu, with their Pearson coefficients being 0.968, 0.909, 0.904, 0.694, and 0.684, respectively. Identifying these fundamental parameters closely related to axial load-bearing capacity is an essential foundation for applying machine learning to predict load-bearing capacity.
- (3)
- The three predictive models for the axial load-bearing capacity of RECFST, utilizing advanced machine learning methods, demonstrate higher accuracy compared to the existing theoretical or code-based calculation formulas. Furthermore, they exhibit higher efficiency compared to finite element methods. The development of a graphical user interface (GUI) based on these machine learning prediction models enables the rapid and accurate prediction of the axial load-bearing capacity of RECFST. This development holds significant importance for the engineering applications of RECFST.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Ref. | Specimens | (kN) | (kN) | (kN) | (kN) | |||
---|---|---|---|---|---|---|---|---|
[10] | WST1-A | 3429 | 3436 | 1.00 | 3695 | 1.03 | 3032 | 0.88 |
WST1-B | 3338 | 3419 | 1.02 | 3685 | 1.03 | 3040 | 0.91 | |
WST2-A | 4162 | 4101 | 0.99 | 4469 | 1.03 | 3996 | 0.96 | |
WST2-B | 4168 | 4046 | 0.97 | 4403 | 1.03 | 3955 | 0.95 | |
WST3-A | 3929 | 3881 | 0.99 | 4157 | 1.02 | 3529 | 0.90 | |
WST3-B | 4158 | 3879 | 0.93 | 4140 | 1.02 | 3530 | 0.85 | |
WST4-A | 4492 | 4548 | 1.01 | 4891 | 1.02 | 4543 | 1.01 | |
WST4-B | 5530 | 5127 | 0.93 | 5542 | 1.05 | 5114 | 0.92 | |
WST5-A | 5620 | 5107 | 0.91 | 5377 | 1.05 | 4630 | 0.82 | |
WST5-B | 5500 | 5196 | 0.94 | 5479 | 1.05 | 4699 | 0.85 | |
WST6-A | 3240 | 3247 | 1.00 | 3376 | 0.93 | 3374 | 1.04 | |
WST6-B | 2993 | 3237 | 1.08 | 3334 | 0.93 | 3330 | 1.11 | |
WST7-A | 4826 | 4776 | 0.99 | 3426 | 0.69 | 4809 | 1.00 | |
WST7-B | 4944 | 4913 | 0.99 | 3721 | 0.73 | 4915 | 0.99 | |
WST8-A | 6521 | 6282 | 0.96 | 3216 | 0.50 | 6381 | 0.98 | |
WST8-B | 6493 | 6275 | 0.97 | 3292 | 0.51 | 6367 | 0.98 | |
WST9-A | 4203 | 4423 | 1.05 | 4468 | 0.99 | 4314 | 1.03 | |
WST9-B | 4180 | 4405 | 1.05 | 4452 | 0.99 | 4296 | 1.03 | |
WST10-A | 7201 | 6758 | 0.94 | 5402 | 0.83 | 6442 | 0.89 | |
WST10-B | 6905 | 6513 | 0.94 | 5076 | 0.81 | 6241 | 0.90 | |
WST11-A | 9065 | 8853 | 0.98 | 5263 | 0.64 | 8237 | 0.91 | |
WST11-B | 8799 | 8761 | 1.00 | 5489 | 0.65 | 8420 | 0.96 | |
[78] | CFRT1-A | 3429 | 3603 | 1.05 | 3709 | 1.03 | 3043 | 0.89 |
CFRT1-B | 3338 | 3586 | 1.07 | 3690 | 1.03 | 3046 | 0.91 | |
CFRT2-A | 4162 | 4351 | 1.05 | 4472 | 1.03 | 4000 | 0.96 | |
CFRT2-B | 4168 | 4293 | 1.03 | 4411 | 1.03 | 3964 | 0.95 | |
CFRT3-A | 3929 | 4069 | 1.04 | 4158 | 1.02 | 3530 | 0.90 | |
CFRT3-B | 4158 | 4066 | 0.98 | 4149 | 1.02 | 3537 | 0.85 | |
CFRT4-A | 4492 | 4825 | 1.07 | 4899 | 1.02 | 4548 | 1.01 | |
CFRT4-B | 5530 | 5272 | 0.95 | 5543 | 1.05 | 5115 | 0.92 | |
CFRT5-A | 5620 | 5137 | 0.91 | 5378 | 1.05 | 4631 | 0.82 | |
CFRT5-B | 5500 | 5225 | 0.95 | 5479 | 1.05 | 4699 | 0.85 | |
[79] | C1 | 1339 | 1445 | 1.08 | 1476 | 1.02 | 1367 | 1.02 |
C2 | 1444 | 1647 | 1.14 | 1638 | 0.99 | 1606 | 1.11 | |
C3 | 1755 | 1968 | 1.12 | 1845 | 0.94 | 1961 | 1.12 | |
C4 | 1825 | 2218 | 1.22 | 2253 | 1.02 | 2100 | 1.15 | |
C5 | 2125 | 2495 | 1.17 | 2463 | 0.99 | 2429 | 1.14 | |
C6 | 2319 | 3056 | 1.32 | 2833 | 0.93 | 3046 | 1.31 | |
C7 | 1623 | 1789 | 1.10 | 1820 | 1.02 | 1754 | 1.08 | |
C8 | 1954 | 2471 | 1.26 | 2501 | 1.01 | 2357 | 1.21 | |
[80] | RCFST-A-0 | 2094 | 2092 | 1.00 | 2037 | 0.97 | 2213 | 1.06 |
[7] | RCFST-1 | 925 | 1108 | 1.20 | 1116 | 1.01 | 1084 | 1.17 |
RCFST-2 | 1215 | 1391 | 1.14 | 1296 | 0.93 | 1410 | 1.16 | |
RCFST-3 | 1635 | 1943 | 1.19 | 1353 | 0.70 | 2034 | 1.24 | |
RCFST-4 | 1658 | 1768 | 1.07 | 1776 | 1.00 | 1604 | 0.97 | |
RCFST-5 | 2091 | 2243 | 1.07 | 2104 | 0.94 | 2116 | 1.01 | |
[21] | RR-1-180-4 | 1755 | 2012 | 1.15 | 1865 | 0.93 | 2010 | 1.15 |
RR-1-180-6 | 2319 | 2805 | 1.21 | 2538 | 0.90 | 2754 | 1.19 | |
RR-0.5-180-6 | 1954 | 2361 | 1.21 | 2354 | 1.00 | 2219 | 1.14 | |
RR-0.5-180-4 | 1623 | 1641 | 1.01 | 1647 | 1.00 | 1589 | 0.98 | |
[81] | RRCFST-A-180-1 | 2026 | 2345 | 1.16 | 2106 | 0.90 | 2277 | 1.12 |
RRCFST-A-180-2 | 1915 | 2389 | 1.25 | 2178 | 0.91 | 2308 | 1.21 | |
RRCFST-C-180-1 | 1574 | 1975 | 1.25 | 1965 | 1.00 | 1830 | 1.16 | |
[82] | PY1-180-e0-fy235 | 1780 | 1879 | 1.06 | 1730 | 0.92 | 1881 | 1.06 |
PY1-180-fy345 | 2100 | 2150 | 1.02 | 1969 | 0.92 | 2108 | 1.00 | |
PYRE1-180-fy345 | 2060 | 2166 | 1.05 | 1974 | 0.91 | 2126 | 1.03 | |
[83] | RCC1-4-180 | 1755 | 1937 | 1.10 | 1813 | 0.94 | 1925 | 1.10 |
RCC1-6-180 | 2319 | 2729 | 1.18 | 2493 | 0.91 | 2664 | 1.15 | |
RCC0.5-4-180 | 1623 | 1569 | 0.97 | 1591 | 1.01 | 1497 | 0.92 | |
RCC0.5-6-180 | 1954 | 2247 | 1.15 | 2267 | 1.01 | 2075 | 1.06 | |
Mean | 1.06 | 0.94 | 1.02 | |||||
Standard deviation | 0.10 | 0.13 | 0.12 |
Ref. | Specimens | (kN) | (kN) | (kN) | (kN) | |||
---|---|---|---|---|---|---|---|---|
[10] | WST1-A | 3429 | 2763 | 0.81 | 3719 | 1.08 | 2960 | 0.86 |
WST1-B | 3338 | 2771 | 0.83 | 3730 | 1.12 | 2968 | 0.89 | |
WST2-A | 4162 | 3245 | 0.78 | 4305 | 1.03 | 3444 | 0.83 | |
WST2-B | 4168 | 3216 | 0.77 | 4266 | 1.02 | 3413 | 0.82 | |
WST3-A | 3929 | 3251 | 0.83 | 4387 | 1.12 | 3491 | 0.89 | |
WST3-B | 4158 | 3249 | 0.78 | 4383 | 1.05 | 3488 | 0.84 | |
WST4-A | 4492 | 3780 | 0.84 | 5029 | 1.12 | 4019 | 0.89 | |
WST4-B | 5530 | 4253 | 0.77 | 5605 | 1.01 | 4548 | 0.82 | |
WST5-A | 5620 | 4323 | 0.77 | 5347 | 0.95 | 4323 | 0.77 | |
WST5-B | 5500 | 4393 | 0.80 | 5436 | 0.99 | 4393 | 0.80 | |
WST6-A | 3240 | 3093 | 0.95 | 3805 | 1.17 | 3093 | 0.95 | |
WST6-B | 2993 | 3051 | 1.02 | 3754 | 1.25 | 3051 | 1.02 | |
WST7-A | 4826 | 4454 | 0.92 | 5509 | 1.14 | 4454 | 0.92 | |
WST7-B | 4944 | 4563 | 0.92 | 5654 | 1.14 | 4563 | 0.92 | |
WST8-A | 6521 | 5957 | 0.91 | 7408 | 1.14 | 5957 | 0.91 | |
WST8-B | 6493 | 5945 | 0.92 | 7405 | 1.14 | 5945 | 0.92 | |
WST9-A | 4203 | 3975 | 0.95 | 4921 | 1.17 | 3975 | 0.95 | |
WST9-B | 4180 | 3959 | 0.95 | 4905 | 1.17 | 3959 | 0.95 | |
WST10-A | 7201 | 5997 | 0.83 | 7460 | 1.04 | 5997 | 0.83 | |
WST10-B | 6905 | 5793 | 0.84 | 7199 | 1.04 | 5793 | 0.84 | |
WST11-A | 9065 | 7688 | 0.85 | 9593 | 1.06 | 7688 | 0.85 | |
WST11-B | 8799 | 7873 | 0.89 | 9816 | 1.12 | 7873 | 0.89 | |
[78] | CFRT1-A | 3429 | 2774 | 0.81 | 3735 | 1.09 | 2972 | 0.87 |
CFRT1-B | 3338 | 2776 | 0.83 | 3737 | 1.12 | 2974 | 0.89 | |
CFRT2-A | 4162 | 3248 | 0.78 | 4309 | 1.04 | 3447 | 0.83 | |
CFRT2-B | 4168 | 3224 | 0.77 | 4278 | 1.03 | 3422 | 0.82 | |
CFRT3-A | 3929 | 3252 | 0.83 | 4388 | 1.12 | 3492 | 0.89 | |
CFRT3-B | 4158 | 3256 | 0.78 | 4393 | 1.06 | 3496 | 0.84 | |
CFRT4-A | 4492 | 3785 | 0.84 | 5035 | 1.12 | 4024 | 0.90 | |
CFRT4-B | 5530 | 4254 | 0.77 | 5606 | 1.01 | 4549 | 0.82 | |
CFRT5-A | 5620 | 4324 | 0.77 | 5348 | 0.95 | 4324 | 0.77 | |
CFRT5-B | 5500 | 4393 | 0.80 | 5436 | 0.99 | 4393 | 0.80 | |
[79] | C1 | 1339 | 1086 | 0.81 | 1422 | 1.06 | 1146 | 0.86 |
C2 | 1444 | 1310 | 0.91 | 1538 | 1.06 | 1310 | 0.91 | |
C3 | 1755 | 1641 | 0.94 | 1944 | 1.11 | 1641 | 0.94 | |
C4 | 1825 | 1654 | 0.91 | 2127 | 1.17 | 1733 | 0.95 | |
C5 | 2125 | 1960 | 0.92 | 2245 | 1.06 | 1960 | 0.92 | |
C6 | 2319 | 2526 | 1.09 | 2936 | 1.27 | 2526 | 1.09 | |
C7 | 1623 | 1402 | 0.86 | 1868 | 1.15 | 1493 | 0.92 | |
C8 | 1954 | 1873 | 0.96 | 2425 | 1.24 | 1966 | 1.01 | |
[80] | RCFST-A-0 | 2094 | 1816 | 0.87 | 2171 | 1.04 | 1816 | 0.87 |
[7] | RCFST-1 | 925 | 866 | 0.94 | 1022 | 1.10 | 866 | 0.94 |
RCFST-2 | 1215 | 1164 | 0.96 | 1392 | 1.15 | 1164 | 0.96 | |
RCFST-3 | 1635 | 1739 | 1.06 | 2100 | 1.28 | 1739 | 1.06 | |
RCFST-4 | 1658 | 1422 | 0.86 | 1729 | 1.04 | 1422 | 0.86 | |
RCFST-5 | 2091 | 1903 | 0.91 | 2335 | 1.12 | 1903 | 0.91 | |
[21] | RR-1-180-4 | 1755 | 1688 | 0.96 | 2001 | 1.14 | 1688 | 0.96 |
RR-1-180-6 | 2319 | 2315 | 1.00 | 2609 | 1.13 | 2315 | 1.00 | |
RR-0.5-180-6 | 1954 | 1804 | 0.92 | 2008 | 1.03 | 1804 | 0.92 | |
RR-0.5-180-4 | 1623 | 1292 | 0.80 | 1516 | 0.93 | 1292 | 0.80 | |
[81] | RRCFST-A-180-1 | 2026 | 1956 | 0.97 | 2281 | 1.13 | 1956 | 0.97 |
RRCFST-A-180-2 | 1915 | 1983 | 1.04 | 2316 | 1.21 | 1983 | 1.04 | |
RRCFST-C-180-1 | 1574 | 1532 | 0.97 | 1767 | 1.12 | 1532 | 0.97 | |
[82] | PY1-180-e0-fy235 | 1780 | 1586 | 0.89 | 1896 | 1.07 | 1586 | 0.89 |
PY1-180-fy345 | 2100 | 1796 | 0.86 | 2110 | 1.00 | 1796 | 0.86 | |
PYRE1-180-fy345 | 2060 | 1813 | 0.88 | 2131 | 1.03 | 1813 | 0.88 | |
[83] | RCC1-4-180 | 1755 | 1614 | 0.92 | 1904 | 1.08 | 1614 | 0.92 |
RCC1-6-180 | 2319 | 2237 | 0.96 | 2510 | 1.08 | 2237 | 0.96 | |
RCC0.5-4-180 | 1623 | 1208 | 0.74 | 1584 | 0.98 | 1276 | 0.79 | |
RCC0.5-6-180 | 1954 | 1674 | 0.86 | 2134 | 1.09 | 1744 | 0.89 | |
Mean | 0.88 | 1.09 | 0.90 | |||||
Standard deviation | 0.08 | 0.08 | 0.07 |
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Specimens | fcu | fy | B | D | t | Nu_test | Nu_FE | Nu_FE/Nu_test |
---|---|---|---|---|---|---|---|---|
WST1-A | 40.4 | 327.7 | 47 | 252 | 3.75 | 3429 | 3395.56 | 0.990 |
RR-1-180-6 | 31 | 290 | 150 | 160 | 6 | 2319 | 2388.66 | 1.030 |
RCFST-1 | 38.06 | 324.6 | 51.5 | 117 | 2.86 | 925 | 988.265 | 1.068 |
Calculation Model | As | Ac | Acfcu or Acfc or Acfc’ | Asfy | A = As + Ac | Asfy/Acfcu |
---|---|---|---|---|---|---|
Ding [10] | × | √ | √ | × | × | √ |
Ren [21] | × | √ | √ | × | × | √ |
M.F. Hassanein [22] | × | × | × | × | × | × |
ACI318-11 [72] | × | × | √ | √ | × | × |
GB 50936-2014 [73] | × | × | × | × | √ | √ |
AISC 360-16 [74] | × | × | √ | √ | × | × |
ML Model | Key Hyperparameters Ranges | Best Key Hyperparameters |
---|---|---|
GBR | learning_rate: 0.01, 0.10, 0.5, 1 | learning_rate: 0.1 |
n_estimators: 100, 200, 300, 10,000 | n_estimators: 300 | |
max_depth: 2,3, 5, 10, 20 | max_depth: 5 | |
RFR | n_estimators:10~1000 | n_estimators:43 |
max_depth:1~20 | max_depth: 20 | |
min_samples_split: 2~11 | min_samples_split: 2 | |
XGB | learning_rate: 0.01, 0.10, 0.5, 1 | learning_rate: 0.1 |
n_estimators: 100, 200, 300, 10,000 | n_estimators: 10,000 | |
max_depth: 2, 3, 5, 10, 20 | max_depth: 5 |
ML Model | Evaluation Index | ||||
---|---|---|---|---|---|
Coefficient of Determination (R2) | Mean Absolute Error (MAE) | Root Mean Square Error (RMSE) | Mean (MEAN) | Coefficient of Variation (COV) | |
Training Set /Testing Set | Training Set /Testing Set | Training Set /Testing Set | Training Set /Testing Set | Training Set /Testing Set | |
XGB | 0.9998/0.9991 | 129/230 | 200/411 | 1.0011/1.0013 | 0.0091/0.0180 |
GBR | 0.9995/0.9989 | 208/269 | 304/429 | 1.0005/1.0004 | 0.0188/0.0224 |
RFR | 0.9997/0.9985 | 114/292 | 195/506 | 1.0002/0.9999 | 0.0091/0.0226 |
ML Model | Percentage of Error (%) | ||||
---|---|---|---|---|---|
≤0.5% | 0.5%~1% | 1%~5% | ≥5% | ||
GBR | Training set | 28.2 | 21.9 | 47.6 | 2.4 |
Testing set | 20.8 | 18.3 | 58.1 | 2.7 | |
RFR | Training set | 55.3 | 24.7 | 19.9 | 0.1 |
Testing set | 24.0 | 18.5 | 54.0 | 3.5 | |
XGB | Training set | 100 | 0 | 0 | 0 |
Testing set | 34.2 | 21.7 | 42.3 | 1.9 |
Ref. | Specimens | (kN) | (kN) | (kN) | (kN) | |||
---|---|---|---|---|---|---|---|---|
[10] | WST1-A | 3429 | 3326 | 0.97 | 3182 | 0.93 | 3327 | 0.97 |
WST1-B | 3338 | 3326 | 1.00 | 3182 | 0.95 | 3327 | 1.00 | |
WST2-A | 4162 | 3790 | 0.91 | 3549 | 0.85 | 3570 | 0.86 | |
WST2-B | 4168 | 3760 | 0.90 | 3537 | 0.85 | 3570 | 0.86 | |
WST3-A | 3929 | 3712 | 0.94 | 3586 | 0.91 | 3701 | 0.94 | |
WST3-B | 4158 | 3686 | 0.89 | 3729 | 0.90 | 3960 | 0.95 | |
WST4-A | 4492 | 4381 | 0.98 | 4361 | 0.97 | 4405 | 0.98 | |
WST4-B | 5530 | 5013 | 0.91 | 5031 | 0.91 | 5048 | 0.91 | |
WST5-A | 5620 | 5431 | 0.97 | 5158 | 0.92 | 5506 | 0.98 | |
WST5-B | 5500 | 5484 | 1.00 | 5158 | 0.94 | 5629 | 1.02 | |
WST6-A | 3240 | 3655 | 1.13 | 3341 | 1.03 | 3618 | 1.12 | |
WST6-B | 2993 | 3655 | 1.22 | 3332 | 1.11 | 3681 | 1.23 | |
WST7-A | 4826 | 5437 | 1.13 | 5457 | 1.13 | 5305 | 1.10 | |
WST7-B | 4944 | 5437 | 1.10 | 5457 | 1.10 | 5305 | 1.07 | |
WST8-A | 6521 | 6873 | 1.05 | 6345 | 0.97 | 6478 | 0.99 | |
WST8-B | 6493 | 6838 | 1.05 | 6229 | 0.96 | 6478 | 1.00 | |
WST9-A | 4203 | 4709 | 1.12 | 4832 | 1.15 | 4829 | 1.15 | |
WST9-B | 4180 | 4705 | 1.13 | 4846 | 1.16 | 4892 | 1.17 | |
WST10-A | 7201 | 6847 | 0.95 | 6484 | 0.90 | 7165 | 1.00 | |
WST10-B | 6905 | 6465 | 0.94 | 6483 | 0.94 | 6120 | 0.89 | |
WST11-A | 9065 | 8730 | 0.96 | 8160 | 0.90 | 9338 | 1.03 | |
WST11-B | 8799 | 8851 | 1.01 | 9219 | 1.05 | 9338 | 1.06 | |
[78] | CFRT1-A | 3429 | 3326 | 0.97 | 3182 | 0.93 | 3327 | 0.97 |
CFRT1-B | 3338 | 3326 | 1.00 | 3182 | 0.95 | 3327 | 1.00 | |
CFRT2-A | 4162 | 3790 | 0.91 | 3549 | 0.85 | 3570 | 0.86 | |
CFRT2-B | 4168 | 3760 | 0.90 | 3549 | 0.85 | 3570 | 0.86 | |
CFRT3-A | 3929 | 3712 | 0.94 | 3556 | 0.91 | 3701 | 0.94 | |
CFRT3-B | 4158 | 3724 | 0.90 | 3556 | 0.86 | 3901 | 0.94 | |
CFRT4-A | 4492 | 4434 | 0.99 | 4361 | 0.97 | 4345 | 0.97 | |
CFRT4-B | 5530 | 5013 | 0.91 | 5031 | 0.91 | 5048 | 0.91 | |
CFRT5-A | 5620 | 5431 | 0.97 | 5158 | 0.92 | 5629 | 1.00 | |
CFRT5-B | 5500 | 5484 | 1.00 | 5158 | 0.94 | 5629 | 1.02 | |
[79] | C1 | 1339 | 1256 | 0.94 | 1251 | 0.93 | 1158 | 0.86 |
C2 | 1444 | 1465 | 1.01 | 1493 | 1.03 | 1372 | 0.95 | |
C3 | 1755 | 1814 | 1.03 | 1787 | 1.02 | 1756 | 1.00 | |
C4 | 1825 | 1822 | 1.00 | 1748 | 0.96 | 1840 | 1.01 | |
C5 | 2125 | 2187 | 1.03 | 2190 | 1.03 | 2285 | 1.08 | |
C6 | 2319 | 2904 | 1.25 | 2696 | 1.16 | 2976 | 1.28 | |
C7 | 1623 | 1655 | 1.02 | 1618 | 1.00 | 1610 | 0.99 | |
C8 | 1954 | 2145 | 1.10 | 2158 | 1.10 | 2043 | 1.05 | |
[80] | RCFST-A-0 | 2094 | 2118 | 1.01 | 2028 | 0.97 | 1948 | 0.93 |
[7] | RCFST-1 | 925 | 1047 | 1.13 | 947 | 1.02 | 1026 | 1.11 |
RCFST-2 | 1215 | 1153 | 0.95 | 1357 | 1.12 | 1127 | 0.93 | |
RCFST-3 | 1635 | 1902 | 1.16 | 1917 | 1.17 | 2047 | 1.25 | |
RCFST-4 | 1658 | 1696 | 1.02 | 1566 | 0.94 | 1778 | 1.07 | |
RCFST-5 | 2091 | 2342 | 1.12 | 2009 | 0.96 | 2231 | 1.07 | |
[21] | RR-1-180-4 | 1755 | 1826 | 1.04 | 1885 | 1.07 | 1756 | 1.00 |
RR-1-180-6 | 2319 | 2630 | 1.13 | 2608 | 1.12 | 2618 | 1.13 | |
RR-0.5-180-6 | 1954 | 1997 | 1.02 | 2017 | 1.03 | 2038 | 1.04 | |
RR-0.5-180-4 | 1623 | 1465 | 0.90 | 1493 | 0.92 | 1372 | 0.85 | |
[81] | RRCFST-A-180-1 | 2026 | 2214 | 1.09 | 2338 | 1.15 | 2250 | 1.11 |
RRCFST-A-180-2 | 1915 | 2214 | 1.16 | 2334 | 1.22 | 2316 | 1.21 | |
RRCFST-C-180-1 | 1574 | 1655 | 1.05 | 1761 | 1.12 | 1704 | 1.08 | |
[82] | PY1-180-e0-fy235 | 1780 | 1720 | 0.97 | 1735 | 0.97 | 1736 | 0.98 |
PY1-180-fy345 | 2100 | 1928 | 0.92 | 2076 | 0.99 | 2036 | 0.97 | |
PYRE1-180-fy345 | 2060 | 1928 | 0.94 | 2076 | 1.01 | 2036 | 0.99 | |
[83] | RCC1-4-180 | 1755 | 1814 | 1.03 | 1729 | 0.99 | 1756 | 1.00 |
RCC1-6-180 | 2319 | 2523 | 1.09 | 2529 | 1.09 | 2503 | 1.08 | |
RCC0.5-4-180 | 1623 | 1459 | 0.90 | 1251 | 0.77 | 1351 | 0.83 | |
RCC0.5-6-180 | 1954 | 1904 | 0.97 | 1823 | 0.93 | 1865 | 0.95 | |
Mean | 1.01 | 0.99 | 1.01 | |||||
Standard deviation | 0.09 | 0.10 | 0.10 |
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Chen, D.; Fan, Y.; Zha, X. Machine Learning-Based Strength Prediction of Round-Ended Concrete-Filled Steel Tube. Buildings 2024, 14, 3244. https://doi.org/10.3390/buildings14103244
Chen D, Fan Y, Zha X. Machine Learning-Based Strength Prediction of Round-Ended Concrete-Filled Steel Tube. Buildings. 2024; 14(10):3244. https://doi.org/10.3390/buildings14103244
Chicago/Turabian StyleChen, Dejing, Youhua Fan, and Xiaoxiong Zha. 2024. "Machine Learning-Based Strength Prediction of Round-Ended Concrete-Filled Steel Tube" Buildings 14, no. 10: 3244. https://doi.org/10.3390/buildings14103244
APA StyleChen, D., Fan, Y., & Zha, X. (2024). Machine Learning-Based Strength Prediction of Round-Ended Concrete-Filled Steel Tube. Buildings, 14(10), 3244. https://doi.org/10.3390/buildings14103244