Evaluation of the moment–rotation curve of steel beam-to-column joints with flange-plate

  • Seyed Mostafa Shabanian
  • Gholamreza AbdollahzadehEmail author
  • Mohammadreza Davoodi
Original Paper


Beam-to-column connections and their behavior in structures due to its importance in seismic loads are one of the most important parts of steel frames analysis. Thus, the correct understanding of power transmission by beam-to-column connections and more accurate understanding of their behavior are essential for modeling and analysis of steel structures. The common method for determining moment–rotation curve is connection test. In this study, after determining the behavioral curve of laboratory sample of steel beam-to-column connection with the flange plates, the behavior of this sample connection was determined by component-based method, finite element analysis and neural network. In order to assess the accuracy of these methods, moment–rotation curve obtained are validated and compared by the laboratory sample results. The results show that moment–rotation curve of the laboratory sample and analytical methods are close together at an acceptable level and can be used to study the behavior of this type of connection with reasonable accuracy.


Moment–rotation curve Component-based method Finite element analysis Neural network method Beam-to-column connection 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest and this article does not contain any studies with human participants or animals performed by any of the authors.


  1. Abdollahzadeh, G., Shabanian, S. M., & Tavakol, A. (2019). Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading. The Structural Design of Tall and Special Buildings,28(6), e1596.CrossRefGoogle Scholar
  2. Abdollahzadeh, G., Gharavi, S. Y., & Beigy, M. H. (2015). Evaluation of the moment–rotation curve of I beam-to-CFT column connection with endplate, using mechanical modeling. International Journal of Steel Structures,15(4), 911–922.CrossRefGoogle Scholar
  3. Abdollahzadeh, G., & Shabanian, S. M. (2013a). Analytical and Experimental Studies on Behavior of Beam to Column Connections with Flange Plate under Monotonic Loading. Iranica Journal of Energy and Environment (IJEE),4(3), 208–211.Google Scholar
  4. Abdollahzadeh, G., & Shabanian, S. M. (2013b). Investigation the Behavior of Beam to Column Connection with Flange Plate by Using Component Method. Iranica Journal of Energy and Environment (IJEE),4(3), 238–242.Google Scholar
  5. Abdollahzadeh, G., & Shabanian, S. M. (2018). Experimental and numerical analysis of beam to column joints in steel structures. Frontiers of Structural and Civil Engineering,12(4), 642–661.CrossRefGoogle Scholar
  6. Anderson, D., Hines, E. L., Arthur, S. J., & Eiap, E. L. (1997). Application of artificial neural networks to the prediction of minor axis steel connections. Computers & Structures,63(4), 685–692.CrossRefGoogle Scholar
  7. Ballio, G., Calado, L., De Martino, A., Faella, C., & Mazzolani, F. M. (1987). Cyclic behaviour of steel beam-to-column joints experimental research. Costruzioni Metalliche,2, 69–88.Google Scholar
  8. Batebi, Y., Mirzagoltabar, A., Shabanian, S. M., & Fateri, S. (2013). Experimental investigation of shrinkage of nano hair reinforced concrete. Iranica Journal of Energy and Environment,4, 68–72.Google Scholar
  9. Bayo, E., Cabrero, J. M., & Gil, B. (2006). An effective component-based method to model semi-rigid connections for the global analysis of steel and composite structures. Engineering Structures,28(9), 97–108.CrossRefGoogle Scholar
  10. Beigzadeh, R., Rahimi, M., & Shabanian, S. R. (2012). Developing a feed forward neural network multilayer model for prediction of binary diffusion coefficient in liquids. Fluid Phase Equilibria,331, 48–57.CrossRefGoogle Scholar
  11. CEN. (2003). Eurocode 3: Design of steel structures. part 1.8: Design of joints (prEN 1993-1-8). European Committee of Standardization, Brussels.Google Scholar
  12. Chen, W., & Kishi, N. (1989). Semi rigid steel beam- to- column connections: Data base and modeling. Journal of Structural Engineering New York,115(1), 105–119.CrossRefGoogle Scholar
  13. Dang, X., & Tan, Y. (2005). An inner product-based dynamic neural network hysteresis model for piezoceramic actuators. Sensors Actuators,121(2), 535–542.CrossRefGoogle Scholar
  14. Davoodi, M.R., Mahdavi, M & Mostafavian, S.A., 2012. Experimental and analytical determination of dynamic properties of a steel frame with bolted flange joints. Proceedings of International Conference on Engineering and Information Technology “ICEIT2012”, Toronto, Canada, Sep (pp. 17–18).Google Scholar
  15. De Martino, A., Faella, C., & De Mazzolani, F. M. (1984). Simulation of beam-to-column joint behavior under cyclic loads. Costruzioni Metalliche,6, 345–356.Google Scholar
  16. De Stefano, M., De Luca, A., & Astaneh-Asl, A. (1994). Modeling of cyclic moment–rotation response of double-angle connections. Journal of Structural Engineering,120(1), 212–229.CrossRefGoogle Scholar
  17. Del Savio, A. A., Nethercot, D. A., Vellasco, P. C. G. S., Andradec, S. A. L., & Martha, L. F. (2009). Generalised component-based model for beam-to-column connections including axial versus moment interaction. Journal of Constructional Steel Research,65, 1876–1895.CrossRefGoogle Scholar
  18. Durodola, J. F., Li, N., Ramachandra, S., & Thite, A. N. (2017). A pattern recognition artificial neural network method for random fatigue loading life prediction. International Journal of Fatigue,99, 55–67.CrossRefGoogle Scholar
  19. Gawin, D., Lefik, M., & Schrefler, B. A. (2001). ANN approach to sorption hysteresis within a coupled hygro-thermo-mechanical FE analysis. International Journal for Numerical Methods in Engineering,50(2), 299–323.CrossRefGoogle Scholar
  20. Ghaboussi, J., Garrett, J. H., & Wu, X. (1990). Material modeling with neural networks. In: Proceedings of the International Conference on Numerical Methods in Engineering: Theory and Applications 701-717.Google Scholar
  21. Ghaboussi, J., Garrett, J. H., & Wu, X. (1991). Knowledge-based modeling of material behavior with neural networks. Journal of Engineering Mechanics,117(1), 132–153.CrossRefGoogle Scholar
  22. Ghaboussi, J., Pecknold, D. A., Zhang, M., & Haj-Ali, R. M. (1998). Autoprogressive training of neural network constitutive models. International Journal for Numerical Methods in Engineering,42(1), 105–126.CrossRefGoogle Scholar
  23. Ghaboussi, J., & Sidarta, D. E. (1997). New method of material modeling using neural networks. 6th International Symposium on Numerical Models IN geomechanics, 393–400.Google Scholar
  24. Ghaboussi, J., & Sidarta, D. E. (1998). New nested adaptive neural networks (NANN) for constitutive modeling. Computers and Geotechnics,22(1), 29–52.CrossRefGoogle Scholar
  25. Ghaboussi, J., Zhang, M., Wu, X., & Pecknold, D. (1997). Nested adaptive neural network: a new architecture. In: Proceedings of international conference on artificial neural networks in engineering, ANNIE97. 1997.Google Scholar
  26. Debar, H. Becker, M., & Siboni D.. (1992). A neural network component for an intrusion detection system, IEEE Computer Society Symposium, 240–250.Google Scholar
  27. Hassan, M. S., Salawdeh, S., & Goggins, J. (2018). Determination of geometrical imperfection models in finite element analysis of structural steel hollow sections under cyclic axial loading. Journal of Constructional Steel Research,141, 189–203.CrossRefGoogle Scholar
  28. Kaklauskas, G., & Ghaboussi, J. (2001). Stress-strain relations for cracked tensile concrete from RC beam tests. ASCE J struct eng,127(1), 64–73. Scholar
  29. Kataoka, M. N., Ferreira, M. A., Debs, E. L., & Ana Lucia, H. C. (2015). Study on the behavior of beam-column connection in precast concrete structure. Computers and Concrete,16(1), 163–178.CrossRefGoogle Scholar
  30. Kim, J. H., Ghaboussi, J., & Elnashai, A. S. (2010). Mechanical and informational modeling of steel beam-to-column connections. Engineering Structures,32(2), 449–458.CrossRefGoogle Scholar
  31. Lee, K., Li, R., Chen, L., Oh, K., & Kim, K. S. (2014). Cyclic testing of steel column-tree moment connections with various beam splice lengths. Steel and Composite Structures,16(2), 221–231.CrossRefGoogle Scholar
  32. Li, J., Zhang, Z. P., & Li, C. W. (2017). Elastic-plastic stress-strain calculation at notch root under monotonic, uniaxial and multiaxial loadings. Theoretical and Applied Fracture Mechanics,92, 33–46.CrossRefGoogle Scholar
  33. Madas, P. J., & Elnashai, A. S. (1992). A component based model for beam-column connections. In: Proceedings of Tenth World Conference of Earthquake Engineering 4495-4499.Google Scholar
  34. Mahdavi, M., Davoodi, M.R., & Mostafavian, A. (2012). Determination of joint stiffness of a three story steel frame by finite element model updating. In: Proceedings of the 15th World Conference on Earthquake Engineering (15WCEE), Lisbon, Portugal.Google Scholar
  35. Mele, E., Calado, L., & De Luca, A. (2001). Cyclic behaviour of beam-to-column welded connections. Steel and Composite Structures,1(3), 269–282.CrossRefGoogle Scholar
  36. Pidaparti, R. M., & Palakal, M. J. (1993). Material model for composites using neural networks. AIAA Journal,31(8), 1533–1535.CrossRefGoogle Scholar
  37. Shabanian, S. R., & Abdoos, A. A. (2018). A hybrid soft computing approach based on feature selection for estimation of filtration combustion characteristics. Neural Computing and Applications,30(12), 3749–3757.CrossRefGoogle Scholar
  38. Shabanian, S. R., Edrisi, S., & Khoram, F. V. (2017). Prediction and optimization of hydrogen yield and energy conversion efficiency in a non-catalytic filtration combustion reactor for jet A and butanol fuels. Korean Journal of Chemical Engineering,34(8), 2188–2197.CrossRefGoogle Scholar
  39. Shabanian, S. R., Lashgari, S., & Hatami, T. (2016a). Application of intelligent methods for the prediction and optimization of thermal characteristics in a tube equipped with perforated twisted tape. Numerical Heat Transfer Part A Applications,70(1), 30–47.CrossRefGoogle Scholar
  40. Shabanian, S. M., Abdollahzadeh, G. R., & Tavakol, A. S. (2016). Column-base plate connection under monotonic load: Experimental and theoretical analysis. Global Journal of Research In Engineering, 16(3), 43–50.Google Scholar
  41. Tschemmernegg, Ferdinand, & Humer, Christian. (1988). The design of structural steel frames under consideration of the nonlinear behaviour of joints. Journal of Constructional Steel Research,11(2), 73–103.CrossRefGoogle Scholar
  42. Wales, M. W., & Rossow, E. C. (1983). Coupled moment-axial force behavior in bolted joints. Journal of Structural Engineering New York,112(3), 615–635.Google Scholar
  43. Wang, Y. Q., Chang, T., Shi, Y. J., Yuan, H. X., Yang, L., & Liao, D. F. (2014). Experimental study on the constitutive relation of austenitic stainless steel S31608 under monotonic and cyclic loading. Thin Walled Structures,83, 19–27.CrossRefGoogle Scholar
  44. Wu, X., and J. Ghaboussi. “Modelling unloading mechanism and cyclic behavior of concrete with adaptive neural networks”. In: Proceedings, Second Asian-Pacific Conference on Computational Mechanics. Sydney, Australia, 1993.Google Scholar
  45. Xu, S., Li, A., & Wang, H. (2017). Bond properties for deformed steel bar in frost-damaged concrete under monotonic and reversed cyclic loading. Construction and Building Materials,148, 344–358.CrossRefGoogle Scholar
  46. Yun, S., Cox, J., & Sims, H. P. (2006). The forgotten follower: A contingency model of leadership and follower self- leadership. Journal of managerial Psychology,21(4), 374–388.CrossRefGoogle Scholar
  47. Yun, G. J., Ghaboussi, J., & Elnashai, A. S. (2008a). Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections. Computer Methods in Applied Mechanics and Engineering,197(33–40), 2836–2857.CrossRefGoogle Scholar
  48. Yun, G. J., Ghaboussi, J., & Elnashai, A. S. (2008b). A design-variable-based inelastic hysteretic model for beam-column connections. Earthquake Engineering and Structural Dynamics,37(4), 535–555.CrossRefGoogle Scholar
  49. Yun, G. J., Ghaboussi, J., & Elnashai, A. S. (2008c). A new neural network-based model for hysteretic behavior of msterials. International Journal for Numerical Methods in Engineering,73(4), 447–469.MathSciNetCrossRefGoogle Scholar
  50. Zhang, M. M. (1996). Neural network material models determined from structural tests. Doctoral dissertation, Department of Civil Engineering, University of Illinois at Urbana-Champaign.Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Civil EngineeringBabol Noshirvani University of TechnologyBabolIran

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