# Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{2}). The prediction result displays that the $XGBOOST$ and $LightGBM$ provided better, and more reliable results compared to $ANN$ and the AISC code. The $XGBOOST$ and $LightGBM$ models yielded higher values of R

^{2}, lower (RMSE), (MAE), and (MAPE) values and have shown to perform more accurate. Therefore, the overall outcomes showed that the $LightGBM$ outperformed the $XGBOOST$ model. Moreover, the overstrength ratio predicted by the $LightGBM$ showed an excellent performance compared to the Gene Expression and Finite Element-based models. The developed models are vital for practitioners to predict the shear strength accurately, which pave the road towards wider application for automation in the steel buildings.

## 1. Introduction

_{p}represents the plastic shear strength (N), F

_{y}represents the measured steel yield strength of the web (MPa), d is the link depth (mm), t

_{f}and t

_{w}are the flange and web thicknesses (mm), respectively. Several investigations revealed the major factors that control the shear link strength, such as flange contribution [3,5], cyclic hardening [3], web slenderness [4], and link length ratio [4,6,7].

## 2. Literature Review

#### 2.1. Analytical Models

#### 2.1.1. AISC 2016

#### 2.1.2. Corte et al., 2013

_{0.08}/V

_{y}) of wide flange shear links without axial restraint, where ${A}_{v}=\left(d-{t}_{f}\right){t}_{w}$ and ${V}_{y}=\left({F}_{y}/\surd 3\right)\left(d-{t}_{f}\right){t}_{w}$. It is worth mentioning that the authors derived Equation (2) for the hot rolled steel link. However, the experimental database of the current study includes both hot rolled and built-up steel links.

#### 2.1.3. G. Almasabha 2022

_{GEP}) [34]. Various parameters were considered in this equation, such as b

_{f}/t

_{f}, d/t

_{w}, A

_{f}/A

_{w}, A

_{f}f

_{yflange}, A

_{w}f

_{yweb}, and e/(M/V).

#### 2.2. ML Models

## 3. Methodology

#### 3.1. Data collection and Feature Definition

#### 3.2. Data Preprocessing

#### 3.3. $ML$ Algorithm

#### 3.3.1. Artificial Neural Network

#### 3.3.2. Extreme Gradient Boosting

#### 3.3.3. Light Gradient Boosting Machine ($LightGBM$)

_{v}represents the C’s subset for the features having value v. The process might be affected by several factors, resulting in it being an insignificant process, such as specific leaves with reasonably minimal information gain are discarded, gaining additional memory storage capacity.

#### 3.4. Stratified K-Fold Cross-Validation

#### 3.5. Prediction Accuracy Measurement

## 4. Result and Discussion

#### 4.1. Descriptive Statistics

_{f}/t

_{f}from 10 to 20.71 with an average of 13.51, d/t

_{w}from 11.33 to 57.5 with an average of 36.66, e/(M/V), from 0.33 to 1.69, A

_{f}/A

_{w}from 0.41 to 2.27 with an average of 1.86, A

_{f}f

_{yflange}from 260 to 9882 kN with an average of 879 kN, A

_{w}f

_{yweb}from 219.7 to 8524.3 kN with an average of 891.67 kN.

#### 4.2. Correlation Matrix Analysis

#### 4.3. Performance of ML Algorithms

#### 4.4. Features Importance Analysis

_{u}/V

_{LightGBM}) and the experimental-to-AISC projected shear strength (V

_{u}/V

_{P}) are illustrated in Figure 10 and Figure 11. Likewise, the AISC based overstrength ratio, the $LightGBM$ demonstrated an excellent performance in the prediction of the shear link strength, where it is cruel to b

_{f}/t

_{f}, d/t

_{w}, A

_{f}/A

_{w}, A

_{f}f

_{yflange}, A

_{w}f

_{yweb}, and e/(M/V). The $LightGBM$ predictions are flat and close to 1.0, which indicates that the $LightGBM$ model is a comprehensive and competent algorithm for predicting the short links’ shear strength.

_{u}/V

_{LightGBM}), AISC 2016 (V

_{u}/V

_{p}), Gene expression model (V

_{u}/V

_{GEP}), and FEM-based model (V

_{0.08}/V

_{y}) are presented in Figure 12. The average of the predicted overstrength ratio is 0.97, 1.11, 1.73, and 1.74 for the V

_{u}/V

_{LightGBM}, V

_{u}/V

_{GEP}, V

_{0.08}/V

_{y}, and V

_{u}/V

_{p}, respectively. The results revealed that the LightGBM is an excellent model in order to evaluate the short links’ shear strength, while the AISC code equation is deficient to accurately estimate the shear link strength due to the fact the AISC code equation only considers the strength of web. This study revealed the significant effect of other variables such as the properties of flange and the link length ratio. The machine learning algorithm ($LightGBM$) has successfully traced the contribution of elements other than the web on the short link shear strength.

## 5. Conclusions

^{2}under the 10-fold cross-validation process have been implemented to enhance the robustness and effectiveness of such models. These measures reveal that the performance of the ML models set side by side to $AISCcode$ was arranged as follows: $LightGBM>XGBOOST>ANN>AISC\mathrm{code}$. According to the importance of the features extracted from ML algorithms, Web force and Link length ratio were the most prominent variables in the prediction results of the overstrength ratio of short links. In addition, the predicted overstrength ratio using the $LightGBM$ was compared to the available models in the literature, where the proficiency of developed models was reasonable. The analysis disclosed that the $LightGBM$ has the least average predicted overstrength ratio compared to the GEP, FEM, or AISC-based models. For future research, a larger database can be adopted to demonstrate the adequacy of these models to predict the overstrength ratio of short links. The impact of other variables on the prediction accuracy needs to be adopted. Moreover, modern algorithms are required to improve results accuracy.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- AISC. Seismic Provisions for Structural Steel Building; ANSI/AISC 341-16; AISC: Chicago, IL, USA, 2016. [Google Scholar]
- Engelhardt, M.D.; Popov, E.P. Experimental Performance of Long Links in Eccentrically Braced Frames. J. Struct. Eng.
**1992**, 118, 3067–3088. [Google Scholar] [CrossRef] - Ji, X.; Wang, Y.; Ma, Q.; Okazaki, T. Cyclic Behavior of Very Short Steel Shear Links. J. Struct. Eng.
**2015**, 142, 04015114. [Google Scholar] [CrossRef] - Liu, X.-G.; Fan, J.-S.; Liu, Y.-F.; Yue, Q.-R.; Nie, J.-G. Experimental research of replaceable Q345GJ steel shear links considering cyclic buckling and plastic overstrength. J. Constr. Steel Res.
**2017**, 134, 160–179. [Google Scholar] [CrossRef] - McDaniel, C.C.; Uang, C.-M.; Seible, F. Cyclic Testing of Built-Up Steel Shear Links for the New Bay Bridge. J. Struct. Eng.
**2003**, 129, 801–809. [Google Scholar] [CrossRef] - Okazaki, T.; Arce, G.; Ryu, H.-C.; Engelhardt, M.D. Experimental Study of Local Buckling, Overstrength, and Fracture of Links in Eccentrically Braced Frames. J. Struct. Eng.
**2005**, 131, 1526–1535. [Google Scholar] [CrossRef] - Okazaki, T. Seismic Performance of Link-To Column Connections in Steel Eccentrically Braced Frames; The University of Texas at Austin: Austin, TX, USA, 2004. [Google Scholar]
- Dusicka, P.; Itani, A.M.; Buckle, I.G. Cyclic Behavior of Shear Links of Various Grades of Plate Steel. J. Struct. Eng.
**2010**, 136, 370–378. [Google Scholar] [CrossRef] - AISC. Seismic Provisions for Structural Steel Buildings; ANSI/AISC 341-02; AISC: Chicago, IL, USA, 2002. [Google Scholar]
- AISC. Specification for Structural Steel Buildings; ANSI/AISC 360-10; AISC: Chicago, IL, USA, 2010. [Google Scholar]
- Ji, X.; Wang, Y.; Ma, Q.; Okazaki, T. Cyclic Behavior of Replaceable Steel Coupling Beams. J. Struct. Eng.
**2016**, 143, 04016169. [Google Scholar] [CrossRef] - Bozkurt, M.B.; Topkaya, C. Replaceable links with direct brace attachments for eccentrically braced frames: Replaceable Links with Direct Brace Attachments for EBF. Earthq. Eng. Struct. Dyn.
**2017**, 46, 2121–2139. [Google Scholar] [CrossRef] - Bozkurt, M.B.; Kazemzadeh Azad, S.; Topkaya, C. Development of detachable replaceable links for eccentrically braced frames. Earthq. Eng. Struct. Dyn.
**2019**, 48, 1134–1155. [Google Scholar] [CrossRef] - Chao, S.-H.; Khandelwal, K.; El-Tawil, S. Ductile Web Fracture Initiation in Steel Shear Links. J. Struct. Eng.
**2006**, 132, 1192–1200. [Google Scholar] [CrossRef] - Della Corte, G.; D’Aniello, M.; Landolfo, R. Analytical and numerical study of plastic overstrength of shear links. J. Constr. Steel Res.
**2013**, 82, 19–32. [Google Scholar] [CrossRef] - Hong, J.-K.; Uang, C.-M.; Okazaki, T.; Engelhardt, M.D. Link-to-Column Connection with Supplemental Web Doublers in Eccentrically Braced Frames. J. Struct. Eng.
**2015**, 141, 04014200. [Google Scholar] [CrossRef] - Hu, S.; Xiong, J.; Zhou, Q.; Lin, Z. Analytical and Numerical Investigation of Overstrength Factors for Very Short Shear Links in EBFs. KSCE J. Civ. Eng.
**2018**, 22, 4473–4482. [Google Scholar] [CrossRef] - Liu, X.-G.; Fan, J.-S.; Liu, Y.-F.; Zheng, M.-Z.; Nie, J.-G. Theoretical research into cyclic web buckling and plastic overstrength of shear links. Thin Walled Struct.
**2020**, 152, 106644. [Google Scholar] [CrossRef] - Ohsaki, M.; Nakajima, T. Optimization of link member of eccentrically braced frames for maximum energy dissipation. J. Constr. Steel Res.
**2012**, 75, 38–44. [Google Scholar] [CrossRef][Green Version] - Yin, W.-H.; Sun, F.-F.; Jin, H.-J.; Hu, D.-Z. Experimental and analytical study on plastic overstrength of shear links covering the full range of length ratio. Eng. Struct.
**2020**, 220, 110961. [Google Scholar] [CrossRef] - Song, H.; Ahmad, A.; Farooq, F.; Ostrowski, K.A.; Maslak, M.; Czarnecki, S.; Aslam, F. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr. Build. Mater.
**2021**, 308, 125021. [Google Scholar] [CrossRef] - Tarawneh, A.; Almasabha, G.; Murad, Y. ColumnsNet: Neural Network Model for Constructing Interaction Diagrams and Slenderness Limit for FRP-RC Columns. J. Struct. Eng.
**2022**, 148, 04022089. [Google Scholar] [CrossRef] - Saleh, E.; Tarawneh, A.; Naser, M.; Abedi, M.; Almasabha, G. You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete. Constr. Build. Mater.
**2022**, 330, 127270. [Google Scholar] [CrossRef] - Almasabha, G.; Tarawneh, A.; Saleh, E.; Alajarmeh, O. Data-Driven Flexural Stiffness Model of FRP-Reinforced Concrete Slender Columns. J. Compos. Constr.
**2022**, 26, 04022024. [Google Scholar] [CrossRef] - Tarawneh, A.; Almasabha, G.; Alawadi, R.; Tarawneh, M. Innovative and Reliable Model for Shear Strength of Steel Fibers Reinforced Concrete Beams. Structures
**2021**, 32, 1015–1025. [Google Scholar] [CrossRef] - Alshboul, O.; Alzubaidi, M.A.; Mamlook, R.E.A.; Almasabha, G.; Almuflih, A.S.; Shehadeh, A. Forecasting Liquidated Damages via Machine Learning-Based Modified Regression Models for Highway Construction Projects. Sustainability
**2022**, 14, 5835. [Google Scholar] [CrossRef] - Alshboul, O.; Shehadeh, A.; Tatari, O.; Almasabha, G.; Saleh, E. Multiobjective and multivariable optimization for earthmoving equipment. J. Facil. Manag.
**2022**. [Google Scholar] [CrossRef] - Shehadeh, A.; Alshboul, O.; Tatari, O.; Alzubaidi, M.A.; Hamed El-Sayed Salama, A. Selection of heavy machinery for earthwork activities: A multi-objective optimization approach using a genetic algorithm. Alex. Eng. J.
**2022**, 61, 7555–7569. [Google Scholar] [CrossRef] - Alshboul, O.; Shehadeh, A.; Hamedat, O. Development of integrated asset management model for highway facilities based on risk evaluation. Int. J. Constr. Manag.
**2021**, 1–10. [Google Scholar] [CrossRef] - Shehadeh, A.; Alshboul, O.; Hamedat, O. A Gaussian mixture model evaluation of construction companies’ business acceptance capabilities in performing construction and maintenance activities during COVID-19 pandemic. Int. J. Manag. Sci. Eng. Manag.
**2022**, 17, 112–122. [Google Scholar] [CrossRef] - Alshboul, O.; Shehadeh, A.; Hamedat, O. Governmental Investment Impacts on the Construction Sector Considering the Liquidity Trap. J. Manag. Eng.
**2022**, 38, 04021099. [Google Scholar] [CrossRef] - Shehadeh, A.; Alshboul, O.; Hamedat, O. Risk Assessment Model for Optimal Gain-Pain Share Ratio in Target Cost Contract for Construction Projects. J. Constr. Eng. Manag.
**2022**, 148, 04021197. [Google Scholar] [CrossRef] - Alshboul, O.; Shehadeh, A.; Almasabha, G.; Almuflih, A.S. Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction. Sustainability
**2022**, 14, 6651. [Google Scholar] [CrossRef] - Almasabha, G. Gene expression model to estimate the overstrength ratio of short links. Structures
**2022**, 37, 528–535. [Google Scholar] [CrossRef] - Alshboul, O.; Shehadeh, A.; Al-Kasasbeh, M.; Al Mamlook, R.E.; Halalsheh, N.; Alkasasbeh, M. Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: A management decision support model. Eng. Constr. Archit. Manag.
**2021**. [Google Scholar] [CrossRef] - Shehadeh, A.; Alshboul, O.; Al Mamlook, R.E.; Hamedat, O. Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Autom. Constr.
**2021**, 129, 103827. [Google Scholar] [CrossRef] - Cevik, A. Genetic programming based formulation of rotation capacity of wide flange beams. J. Constr. Steel Res.
**2007**, 63, 884–893. [Google Scholar] [CrossRef] - Fonseca, E.T.; da Vellasco, P.C.G.S.; de Andrade, S.A.L.; Vellasco, M.M.B.R. Neural network evaluation of steel beam patch load capacity. Adv. Eng. Softw.
**2003**, 34, 763–772. [Google Scholar] [CrossRef] - Güneyisi, E.M.; D’Aniello, M.; Landolfo, R.; Mermerdaş, K. A novel formulation of the flexural overstrength factor for steel beams. J. Constr. Steel Res.
**2013**, 90, 60–71. [Google Scholar] [CrossRef] - Fan, J.; Wang, X.; Wu, L.; Zhou, H.; Zhang, F.; Yu, X.; Lu, X.; Xiang, Y. Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Convers. Manag.
**2018**, 164, 102–111. [Google Scholar] [CrossRef] - Zhang, M.; Xiang, F.; Liu, Z. Short-term traffic flow prediction based on combination model of XGBoost-LightGBM. In Proceedings of the 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xi’an, China, 28–31 October 2018; pp. 322–327. [Google Scholar]
- Pathy, A.S.; Meher, B.P. Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods. Algal Res.
**2020**, 50, 102006. [Google Scholar] [CrossRef] - Price, B. Investigation on Innovative Shear Link Configurations and Optimal Design for Earthquake Resistant Steel Eccentrically Braced Frames. Master’s Thesis, University of Texas at Arlington, Arlington, TX, USA, 2015. [Google Scholar]
- Dubina, D.; Stratan, A.; Dinu, F. Dual high-strength steel eccentrically braced frames with removable links. Earthq. Eng. Struct. Dyn.
**2008**, 37, 1703–1720. [Google Scholar] [CrossRef] - Hjelmstad, K.D.; Popov, E.P. Cyclic Behavior and Design of Link Beams. J. Struct. Eng.
**1983**, 109, 2387–2403. [Google Scholar] [CrossRef] - Volynkin, D.; Dusicka, P.; Clifton, G.C. Intermediate Web Stiffener Spacing Evaluation for Shear Links. J. Struct. Eng.
**2018**, 145, 04018257. [Google Scholar] [CrossRef] - Yang, X. Artificial neural networks. In Handbook of Research on Geoinformatics; IGI Global: Hershey, PA, USA, 2009; pp. 122–128. [Google Scholar]
- Kim, M.; Jung, S.; Kang, J. Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea. Sustainability
**2020**, 12, 109. [Google Scholar] [CrossRef][Green Version] - Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar] [CrossRef][Green Version]
- Friedman, J.H. Greedy function approximation: A gradient boosting machine. Ann. Statist.
**2001**, 29, 11891232. [Google Scholar] [CrossRef] - Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T.-Y. LightGBM: A highly efficient gradient boosting decision tree. Adv. Neur. Infor. Process. Sys.
**2017**, 30, 3146–3154. [Google Scholar] - Zeng, H.; Yang, C.; Zhang, H.; Wu, Z.; Zhang, J.; Dai, G.; Babiloni, F.; Kong, W. A lightGBM-based EEG analysis method for driver mental states classification. Comput. Intell. Neurosci.
**2019**, 2019, 3761203. [Google Scholar] [CrossRef] - Tong, L.; Zhang, Y.; Zhang, L.; Liu, H.; Zhang, Z.; Li, R. Ductility and energy dissipation behavior of G20Mn5QT cast steel shear link beams under cyclic loading. J. Constr. Steel Res.
**2018**, 149, 64–77. [Google Scholar] [CrossRef] - Mahmoudi, F.; Dolatshahi, K.M.; Mahsuli, M.; Nikoukalam, M.T.; Shahmohammadi, A. Experimental study of steel moment resisting frames with shear link. J. Constr. Steel Res.
**2018**, 154, 197–208. [Google Scholar] [CrossRef]

Feature | Definition | Data Type |
---|---|---|

$({b}_{f}/{t}_{f}$) | Flange slenderness ratio | Numeric |

$(d/{t}_{w}$) | Web slenderness ratio | Numeric |

$({A}_{f}/{A}_{w}$) | Flange to web area ratio | Numeric |

$({A}_{f}{f}_{yflange}$) | Flange force | Numeric |

$({A}_{w}{f}_{yweb}$) | Web force | Numeric |

$e/\left(M/V\right)$ | Link length ratio | Numeric |

Reference | No. of Tests | b_{f}/t_{f} | d/t_{w} | e/(M/V) | f_{yflange}, MPa | f_{yweb}, MPa | V_{test} (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 | |||||
---|---|---|---|---|---|---|

$({\mathit{b}}_{\mathit{f}}/{\mathit{t}}_{\mathit{f}})$ | $(\mathit{d}/{\mathit{t}}_{\mathit{w}})$ | $({\mathit{A}}_{\mathit{f}}/{\mathit{A}}_{\mathit{w}})$ | $\left({\mathit{A}}_{\mathit{f}}{\mathit{f}}_{\mathit{yflange}}\right)$ | $\left({\mathit{A}}_{\mathit{w}}{\mathit{f}}_{\mathit{yweb}}\right)$ | $\mathit{e}/\left(\mathit{M}/\mathit{V}\right)$ | |

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 | |||
---|---|---|---|---|

$\mathit{L}\mathit{i}\mathit{g}\mathit{h}\mathit{t}\mathit{G}\mathit{B}\mathit{M}$ | $\mathit{X}\mathit{G}\mathit{B}\mathit{O}\mathit{O}\mathit{S}\mathit{T}$ | $\mathit{A}\mathit{N}\mathit{N}$ | $\mathit{A}\mathit{I}\mathit{S}\mathit{C}\mathit{C}\mathit{o}\mathit{d}\mathit{e}$ | |

$MAE$ | 92.0 | 196.5 | 378.0 | 397.9 |

$RMSE$ | 132.5 | 284.0 | 507.9 | 804.2 |

$MAPE$ | 11.7 | 24.1 | 35.8 | 39.2 |

${R}^{2}$ | 0.99 | 0.96 | 0.90 | 0.75 |

$\mathrm{Training}\mathrm{Tim}$ | 7 s | 9 s | 14 s |

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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Almasabha, 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