Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings
Abstract
:1. Introduction
2. Literature Review
2.1. Analytical Models
2.1.1. AISC 2016
2.1.2. Corte et al., 2013
2.1.3. G. Almasabha 2022
2.2. ML Models
3. Methodology
3.1. Data collection and Feature Definition
3.2. Data Preprocessing
3.3. Algorithm
3.3.1. Artificial Neural Network
3.3.2. Extreme Gradient Boosting
3.3.3. Light Gradient Boosting Machine ()
3.4. Stratified K-Fold Cross-Validation
3.5. Prediction Accuracy Measurement
4. Result and Discussion
4.1. Descriptive Statistics
4.2. Correlation Matrix Analysis
4.3. Performance of ML Algorithms
4.4. Features Importance Analysis
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Feature | Definition | Data Type |
---|---|---|
) | Flange slenderness ratio | Numeric |
) | Web slenderness ratio | Numeric |
) | Flange to web area ratio | Numeric |
) | Flange force | Numeric |
) | Web force | Numeric |
Link length ratio | Numeric |
Reference | No. of Tests | bf/tf | d/tw | e/(M/V) | fyflange, MPa | fyweb, MPa | Vtest (kN) |
---|---|---|---|---|---|---|---|
Ji et al., 2015 [3] | 12 | 12.9 | 40 | 0.58–0.97 | 319 | 228; 273 | 869–1130 |
Ji et al., 2016 [11] | 2 | 10.6; 14.2 | 35 | 0.7–0.76 | 378; 396 | 228 | 838–926 |
McDaniel et al., 2003 [5] | 2 | 10.6–13.3 | 33.9 | 0.59; 0.82 | 366 | 354 | 9363–9919 |
Volynkin et al., 2018 [46] | 5 | 12–12.8 | 21.7–44.2 | 0.76–1.02 | 364; 455 | 364; 374 | 783–1034 |
Dusicka et al., 2010 [8] | 5 | 11.8; 13.6 | 22–33.9 | 0.8; 0.82 | 223–503 | 242–503 | 1845–4348 |
Liu et al., 2017 [4] | 11 | 10–13 | 21–35 | 1.12–1.6 | 366 | 354–362 | 373–668 |
Okazaki et al., 2005 [6] | 11 | 11.5–18.3 | 22.1–56.8 | 1.04–1.49 | 319–362 | 382–404 | 585–1280 |
Okazaki, T. 2004 [7] | 6 | 12.2 | 57.5 | 1.11 | 351.6 | 393 | 1007–1140 |
Bokurt and Topaya 2017 [12] | 8 | 18–20.7 | 22.4–22.8 | 1.04–1.59 | 268–281 | 275–299 | 275–591 |
Bokurt and et al., 2019 [13] | 6 | 18–20 | 22.2–29 | 1.26–1.59 | 272–357 | 276–343 | 288–573 |
Tong et al., 2018 [53] | 4 | 12 | 17.9 | 1.25 | 461.2 | 463.4 | 720–1013 |
Mahmoudi et al., 2018 [54] | 1 | 10 | 34 | 0.78 | 301 | 301 | 478 |
Hjelmstad et al., 1983 [45] | 8 | 11.5; 15.6 | 43.4; 57 | 1.27–1.57 | 241.3; 285.4 | 711–914 | 600–1067 |
Dubina et al., 2008 [44] | 24 | 12.25 | 38.7 | 0.65–1.3 | 221–315 | 221–315 | 270–420 |
Price, B. 2015 [43] | 5 | 11.5; 16.5 | 23.8; 56.8 | 1.11; 1.23 | 353.7; 398.5 | 360; 403 | 433–1298 |
Total | 110 |
Stander Statistics | Features | |||||
---|---|---|---|---|---|---|
Mean | 13.51 | 36.66 | 1.01 | 879.08 | 891.67 | 1.09 |
Standard Error | 0.24 | 1.16 | 0.04 | 115.7 | 107.91 | 0.03 |
Median | 12.24 | 38.71 | 0.86 | 608.74 | 664.32 | 1.1 |
Mode | 12.24 | 38.71 | 0.86 | 803.88 | 550.24 | 0.87 |
Standard Deviation | 2.53 | 12.18 | 0.43 | 1213.48 | 1131.79 | 0.28 |
Sample Variance | 6.42 | 148.37 | 0.18 | 1,472,537 | 1,280,955 | 0.08 |
Kurtosis | 0.56 | −0.76 | 0.33 | 36.84 | 37.1 | −0.65 |
Skewness | 1.33 | 0.31 | 1.08 | 5.65 | 5.74 | −0.15 |
Range | 10.71 | 46.15 | 1.86 | 9622.04 | 8304.59 | 1.36 |
Minimum | 10 | 11.33 | 0.41 | 259.96 | 219.73 | 0.33 |
Maximum | 20.71 | 57.48 | 2.27 | 9882 | 8524.32 | 1.69 |
Sum | 1486.02 | 4032.6 | 110.61 | 96698.71 | 98083.4 | 119.9 |
Count | 110 | 110 | 110 | 110 | 110 | 110 |
Performance Comparison | Prediction Models | |||
---|---|---|---|---|
92.0 | 196.5 | 378.0 | 397.9 | |
132.5 | 284.0 | 507.9 | 804.2 | |
11.7 | 24.1 | 35.8 | 39.2 | |
0.99 | 0.96 | 0.90 | 0.75 | |
7 s | 9 s | 14 s |
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Almasabha, G.; Alshboul, O.; Shehadeh, A.; Almuflih, A.S. Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings. Buildings 2022, 12, 775. https://doi.org/10.3390/buildings12060775
Almasabha G, Alshboul O, Shehadeh A, Almuflih AS. Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings. Buildings. 2022; 12(6):775. https://doi.org/10.3390/buildings12060775
Chicago/Turabian StyleAlmasabha, Ghassan, Odey Alshboul, Ali Shehadeh, and Ali Saeed Almuflih. 2022. "Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings" Buildings 12, no. 6: 775. https://doi.org/10.3390/buildings12060775