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Almasabha, G.;  Alshboul, O.;  Shehadeh, A.;  Almuflih, A.S. Mathematical Model of Short Links in Steel Buildings. Encyclopedia. Available online: https://encyclopedia.pub/entry/24532 (accessed on 11 July 2025).
Almasabha G,  Alshboul O,  Shehadeh A,  Almuflih AS. Mathematical Model of Short Links in Steel Buildings. Encyclopedia. Available at: https://encyclopedia.pub/entry/24532. Accessed July 11, 2025.
Almasabha, Ghassan, Odey Alshboul, Ali Shehadeh, Ali Saeed Almuflih. "Mathematical Model of Short Links in Steel Buildings" Encyclopedia, https://encyclopedia.pub/entry/24532 (accessed July 11, 2025).
Almasabha, G.,  Alshboul, O.,  Shehadeh, A., & Almuflih, A.S. (2022, June 27). Mathematical Model of Short Links in Steel Buildings. In Encyclopedia. https://encyclopedia.pub/entry/24532
Almasabha, Ghassan, et al. "Mathematical Model of Short Links in Steel Buildings." Encyclopedia. Web. 27 June, 2022.
Mathematical Model of Short Links in Steel Buildings
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This entry is adapted from the peer-reviewed paper 10.3390/buildings12060775

The rapid growth of using the short links in steel buildings due to their high shear strength and rotational capacity attracts the attention of structural engineers to investigate the performance of short links. However, insignificant attention has been oriented to efficiently developing a comprehensive model to forecast the shear strength of short links, which is expected to enhance the steel structures’ constructability. 

shear strength short link steel construction industry

1. Introduction

Short links are W−shape steel sections that are either constructed or rolled with link length ratio, e/(M/V), less than 1.6 (AISC, 2016) [1]; where e represents the link length, M and V represent the plastic moments, and shear capacity, respectively. Short links are widely employed in steel bridges, Eccentric Braced Frames (EBFs) and coupled walls. The short links have several advantages, such as exceptional plastic rotational capacity and plastic shear capacity [2]. However, several studies observed that the AISC formula underestimates the predicted short links’ shear strength [3][4][5][6][7]. The AISC, 2016 [2] Equation (F3–2) estimates the plastic short links’ shear strength using Vp=0.6×Fy (d − 2tf)×tw; where Vp represents the plastic shear strength (N), Fy represents the measured steel yield strength of the web (MPa), d is the link depth (mm), tf and tw 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].
The testing program of McDaniel et al., 2003 [5], which included two full-scale built-up short links, revealed that in terms of degrading the shear strength of tested links, the cutting-edge factor is a brittle fracture on the linked web. As a result, the tested short links exhibited overstrength factors of 1.83 and 1.94. In addition, Dusicka et al., 2010 [8] investigated the effect of steel yield stress 100, 225, integrated steel strength of 100 and 440, 345, 485 MPa for five plate steel shear links. The obtained results illustrate that the steel links with a low grade attained a plastic rotation of 0.2 rad while the conventional links reached 0.12 rad, and the low-grade steel links achieved an overstrength factor considerably higher than conventional links. Moreover, the average overstrength factor for the tested 12 short links (length ratio ranges from 0.58 to 0.97) was 1.9, Ji et al., 2015 [3]. Furthermore, the very short links reached a plastic rotation of 0.14 rad greater than the 8% limit of AISC 341-10 [9][10]. In addition, Ji et al., 2016 [11] found that the average shear strength of four built-up short links reached 2.0.
Similarly, Liu et al., 2017 [4] noticed that the short links’ shear strength was significantly impacted by the web slenderness and link length ratio, and the overstrength ratio for the 12 built-up short links was between 1.35 to 1.5. The link length was critical in the steel links, Okazaki, T. 2004 [7]. The experimental program included 16 link-to-column connections with different link length ratios, where the overstrength ratio varied between 1.05 and 1.47. Bozkurt and Topkaya 2017 [12] discovered that plastic rotation and the overstrength ratio are negatively associated with the link length ratio. The test program included seven short links considering several features (i.e., loading protocol, the link length ratio and stiffeners spacing). In addition, an overstrength ratio of 1.87 to 2.3 was achieved in Bozkurt et al., 2019 [13], where six specimens with a link length of 600 to 800 mm were tested.
To explore the link’s ultimate rotational capacity, shear capacity, buckling of flanges, and web, the analysis of complex finite element of shear links was implemented [3][14][15][16][17][18][19][20]. However, finite element simulation is considered time consuming, especially in the modeling process and validation of the performance of the predicting model. Moreover, FEA requires special experts in the mechanics of materials and computer aided-software engineering. Recently, the huge availability of databases in the wide range of engineering applications paved the way to extensively and successfully use the machine learning tools to help engineers save the cost, time, and efforts. A leaping use of machine learning has been witnessed in the various civil engineering fields over the last decade. While machine learning has been successfully utilized in the civil engineering applications [21][22][23][24][25][26][27][28][29][30][31][32][33], limited studies used machine learning tools to address the shear strength of short links; where a few studies have employed actual experimental databases to validate ML algorithms.

2. Analytical Models

The literature includes three analytical models to assess the shear strength of shear links (i.e., AISC 2016 [1], Corte et al., 2013 [15], and G. Almasabha 2022 [34]). The following discussion summarizes the available models.

2.1. AISC 2016

The AISC 2016 [1] adopted Equation (1) for the assessment of shear strength of links. It is worth mentioning that Equation (1) does not take into consideration the role of link length proportion, the contribution of flanges, and the slenderness ratio of web or flanges.
V p = 0.6 × F y × ( d − 2 t f ) × t w

2.2. Corte et al., 2013

A finite element-based algorithm [15] has been proposed to estimate the overstrength ratio (V0.08/Vy) of wide flange shear links without axial restraint, where Av=(d−tf)tw and Vy=(Fy/√3)(d−tf)tw. 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.
V 0.08 V y = 1 + 1.35 ( A f A v ) ( d e )

2.3. G. Almasabha 2022

This study used the gene expression model to build a mathematical equation for the shear link strength (VGEP) [34]. Various parameters were considered in this equation, such as bf/tf, d/tw, Af/Aw, Af fyflange, Awfyweb, and e/(M/V).
V G E P = ( 1.047   ( e M / V ) 0.416   ) × ( A w F y w e b ) − 0.017 × ( A f A w ) 0.12             × ( A w F y w e b − ( d t w ) 0.6 + ( e M / V ) 0.2 + b f t f )

2.2. ML Models

Although innovative ML algorithms outperform traditional models in most research disciplines [35][36], ML models are still humbly utilized to forecast the short links’ shear strength such as [15][16][17][18][37][38][39], also limited variables with humble datasets have been applied in these studies. On the other hand, these studies’ executed ML models seem somewhat traditional. Therefore, the need to use sophisticated ML models for shear strength prediction of short links to reduce prediction error with better accuracy is becoming required. The XGBOOST technique was recently developed using a tree-based ensemble, a complex gradient boosting with higher processing abilities, and an excellent tool to deal with over-fitting concerns [40]. The learning method uses a boosting framework-based decision tree called LightGBM, released by Microsoft in 2017. It is quicker, uses less memory, and is more accurate than XGBOOST [41]. LightGBM also provided decision rules for category features, which transform factors into one-time multidimensional functionality, saving time and memory [42].

References

  1. AISC. Seismic Provisions for Structural Steel Building; ANSI/AISC 341-16; AISC: Chicago, IL, USA, 2016.
  2. Engelhardt, M.D.; Popov, E.P. Experimental Performance of Long Links in Eccentrically Braced Frames. J. Struct. Eng. 1992, 118, 3067–3088.
  3. Ji, X.; Wang, Y.; Ma, Q.; Okazaki, T. Cyclic Behavior of Very Short Steel Shear Links. J. Struct. Eng. 2015, 142, 04015114.
  4. 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.
  5. 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.
  6. 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.
  7. Okazaki, T. Seismic Performance of Link-To Column Connections in Steel Eccentrically Braced Frames; The University of Texas at Austin: Austin, TX, USA, 2004.
  8. 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.
  9. AISC. Seismic Provisions for Structural Steel Buildings; ANSI/AISC 341-02; AISC: Chicago, IL, USA, 2002.
  10. AISC. Specification for Structural Steel Buildings; ANSI/AISC 360-10; AISC: Chicago, IL, USA, 2010.
  11. Ji, X.; Wang, Y.; Ma, Q.; Okazaki, T. Cyclic Behavior of Replaceable Steel Coupling Beams. J. Struct. Eng. 2016, 143, 04016169.
  12. 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.
  13. 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.
  14. Chao, S.-H.; Khandelwal, K.; El-Tawil, S. Ductile Web Fracture Initiation in Steel Shear Links. J. Struct. Eng. 2006, 132, 1192–1200.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Ohsaki, M.; Nakajima, T. Optimization of link member of eccentrically braced frames for maximum energy dissipation. J. Constr. Steel Res. 2012, 75, 38–44.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. Alshboul, O.; Shehadeh, A.; Tatari, O.; Almasabha, G.; Saleh, E. Multiobjective and multivariable optimization for earthmoving equipment. J. Facil. Manag. 2022.
  28. 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.
  29. 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.
  30. 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.
  31. Alshboul, O.; Shehadeh, A.; Hamedat, O. Governmental Investment Impacts on the Construction Sector Considering the Liquidity Trap. J. Manag. Eng. 2022, 38, 04021099.
  32. 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.
  33. 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.
  34. Almasabha, G. Gene expression model to estimate the overstrength ratio of short links. Structures 2022, 37, 528–535.
  35. 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.
  36. 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.
  37. Cevik, A. Genetic programming based formulation of rotation capacity of wide flange beams. J. Constr. Steel Res. 2007, 63, 884–893.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
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Subjects: Engineering, Civil
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Ghassan Almasabha
,
odey Alshboul
,
Ali Shehadeh
,
Ali Saeed Almuflih
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Revisions: 2 times (View History)
Update Date: 28 Jun 2022
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