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Experimental and numerical analysis of beam to column joints in steel structures

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Abstract

The behaviors such as extreme non-elastic response, constant changes in roughness and resistance, as well as formability under extreme loads such as earthquakes are the primary challenges in the modeling of beam-to-column connections. In this research, two modeling methods including mechanical and neural network methods have been presented in order to model the complex hysteresis behavior of beam-to-column connections with flange plate. First, the component-based mechanical model will be introduced in which every source of transformation has been shown only with geometrical and material properties. This is followed by the investigation of a neural network method for direct extraction of information out of experimental data. For the validation of behavioral curves as well as training of the neural network, the experiments were carried out on samples with real dimensions of beam-to-column connections with flange plate in the laboratory. At the end, the combinational modeling framework is presented. The comparisons reveal that the combinational modeling is able to display the complex narrowed hysteresis behavior of the beam-to-column connections with flange plate. This model has also been successfully employed for the prediction of the behavior of a newly designed connection.

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References

  1. Frye MJ, Morris G A. Analysis of flexibility connected steel frames. Canadian Journal of Civil Engineering, 1975, 2(3): 280–291

    Article  Google Scholar 

  2. Ballio G, Calado L, De Martino A, Faella C, Mazzolani F M. Cyclic behaviour of steel beam-to-column joints experimental research. Costruzioni Metalliche, 1987, 2: 69–88

    Google Scholar 

  3. Mele E, Calado L, De Luca A. Cyclic behaviour of beam-to-column welded connections. Steel and Composite Structures, 2001, 1(3): 269–282

    Article  Google Scholar 

  4. De Martino A, Faella C, Mazzolani F M. Simulation of beam-tocolumn joint behavior under cyclic loads. Costruzioni Metalliche, 1984, 6: 345–356

    Google Scholar 

  5. Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

    Article  Google Scholar 

  6. Mashaly E, El-Heweity M, Abou-Elfath H, Osman M. Finite element analysis of beam-to-column joints in steel frames under cyclic loading. Alexandria Engineering Journal, 2011, 50(1): 91–104

    Article  Google Scholar 

  7. Calado L, Simoes da Silva L, Simoes R. Cyclic behavior of steel and composite beam-to-column joints. STESSA, Montreal, 2015, 159–169

    Google Scholar 

  8. Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313

    Article  Google Scholar 

  9. Rassati G A, Leon R T, Noe S. Component modeling of partially restrained composite joints under cyclic and dynamic loading. Journal of Structural Engineering, 2004, 130(2): 343–351

    Article  Google Scholar 

  10. Kataoka M N, Ferreira M A, El Debs, Ana Lucia H. C. Study on the behavior of beam-column connection in precast concrete structure. Computers and Concrete, 2015, 16(1): 163–178

    Article  Google Scholar 

  11. Abdollahzadeh G R, Ghobadi F. Composed Mathematical-Informational modeling of column-base connections. Latin American Journal of Solids and Structures, 2014, 11(8): 1417–1431

    Article  Google Scholar 

  12. Abdollahzadeh G R, Ghobadi F. Mathematical modeling of columnbase connections under monotonic loading. Civil Engineering Infrastructures Journal, 2014, 47(2): 255–272

    Google Scholar 

  13. Abdollahzadeh G R, Yapang Gharavi S, Hosseinali Beygi M. Analytical and Experimental Investigation of I Beam-to-CFT Column Connections under Monotonic Loading. International Journal of Engineering, 2014, 27(2): 293–306

    Google Scholar 

  14. Abdollahzadeh G R, Hashemi S M, Tavakoli H R, Rahami H. Determination of hysteretic behavior of steel end-plate beam to column connection with mechanical and neural network modeling. Arabian Journal for Science and Engineering, 2014, 39(11): 7661–7671

    Article  Google Scholar 

  15. Abdollahzadeh G R, Ghobadi F. Linked mathematical–informational modeling of perforated steel plate shear walls. Thin-walled Structures, 2015, 94: 512–520

    Article  Google Scholar 

  16. Abdollahzadeh G, Gharavi S Y, Beigy M H. Evaluation of the Moment-Rotation Curve of I Beam-to-CFT Column Connection with Endplate, Using Mechanical Modeling. International Journal of Steel Structures, 2015, 15(4): 911–912

    Article  Google Scholar 

  17. Chen W, Kishi N. Semi rigid steel beam- to- column connections: Data base and modeling. Journal of Structural Engineering, 1989, 115(1), 105–119

    Article  Google Scholar 

  18. Hadianfard M A, Rahnema H. Effects of RHS face deformation on the rigidity of beam-column connection. Steel and Composite Structures, 2010, 10(6): 489–500

    Article  Google Scholar 

  19. Swanson J A, Leon R T. Bolted steel connections: tests on T-stub components. Journal of Structural Engineering, 2000, 126(1): 50–56

    Article  Google Scholar 

  20. Abdollahzadeh G, Shabanian S M. Investigation the Behavior of Beam to Column Connection with Flange Plate by Using Component Method. Iranica Journal of Energy and Environment, 2013, 4(3): 238–242

    Google Scholar 

  21. Bencardino F, Condello A. Experimental study and numerical investigation of behavior of RC beams strengthened with steel reinforced grout. Computers and Concrete, 2014, 14(6): 711–725

    Article  Google Scholar 

  22. Bayo E, Cabrero J M, Gil B. An effective component-based method to model semi-rigid connections for the global analysis of steel and composite structures. Engineering Structures, 2006, 28(1): 97–108

    Article  Google Scholar 

  23. Abdollahzadeh G, Shabanian S M. Analytical and Experimental Studies on Behavior of Beam to Column Connections with Flange Plate under Monotonic Loading. Iranica Journal of Energy and Environment, 2013, 4(3): 208–211

    Google Scholar 

  24. Shabanian M, Abdollahzadeh G, Amir Sina Tavakol S. Column- Base Plate Connection under Monotonic Load: Experimental and Theoretical Analysis. Global Journal of Research in Engineering, 2016, 16(3): 3–15

    Google Scholar 

  25. CEN. Eurocode 3: Design of steel structures. part 1.8: Design of joints (prEN 1993–1-8). European Committee of Standardization, Brussels, 2003

    Google Scholar 

  26. Wales MW, Rossow EC. Coupled moment-axial force behavior in bolted joints. Journal of Structural Engineering, 1983, 109(5): 1250–1266

    Article  Google Scholar 

  27. Tschemmernegg F, Humer C. The design of structural steel frames under consideration of the nonlinear behavior of joints. Journal of Constructional Steel Research, 1998, 11(2): 73–103

    Article  Google Scholar 

  28. Madas P J, Elnashai A S. A component based model for beamcolumn connections. In: Proceedings of Tenth World Conference of Earthquake Engineering, 1992, 4495–4499

    Google Scholar 

  29. De Stefano M, De Luca A, Astaneh-Asl A. Modeling of cyclic moment rotation response of double-angle connections. Journal of Structural Engineering, 1994, 120(1): 212–229

    Article  Google Scholar 

  30. Del Savio A A, Nethercot D A, Vellasco P C G S, Andradec S A L, Martha L F. Generalised component-based model for beam-tocolumn connections including axial versus moment interaction. Journal of Constructional Steel Research, 2009, 65(8–9): 1876–1895

    Article  Google Scholar 

  31. Furukawa T, Hoffman M. Accurate cyclic plastic analysis using a neural network material model. Engineering Analysis with Boundary Elements, 2004, 28(3): 195–204

    Article  MATH  Google Scholar 

  32. Beigzadeh R, Rahimi M, Shabanian S R. Developing a feed forward neural network multilayer model for prediction of binary diffusion coefficient in liquids. Fluid Phase Equilibria, 2012, 331: 48–57

    Article  Google Scholar 

  33. Lee K, Li R, Chen L, Kim K S. Cyclic testing of steel column-tree moment connections with various beam splice lengths. Steel and Composite Structures, 2014, 16(2): 221–231

    Article  Google Scholar 

  34. Debar H, Becker M, Siboni D. A neural network component for an intrusion detection system. IEEE Computer Society Symposium, 1992, 240–250

    Google Scholar 

  35. Shabanian S R, Lashgari S, Hatami T. 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, 2016, 70(1): 30–47

    Article  Google Scholar 

  36. Anderson D, Hines E L, Arthur S J, Eiap E L. Application of artificial neural networks to the prediction of minor axis steel connections. Computers & Structures, 1997, 63(4): 685–692

    Article  Google Scholar 

  37. Gawin D, Lefik M, Schrefler B A. ANN approach to sorption hysteresis within a coupled hygro-thermo-mechanical FE analysis. International Journal for Numerical Methods in Engineering, 2001, 50(2): 299–323

    Article  MATH  Google Scholar 

  38. Ghaboussi J, Garrett J H, Wu X. Material modeling with neural networks. Proceedings of the International Conference on Numerical Methods in Engineering: Theory and Applications, 1990, 701–717

    Google Scholar 

  39. Ghaboussi J, Garrett J H Jr, Wu X. Knowledge-based modeling of material behavior with neural networks. Journal of Engineering Mechanics, 1991, 117(1): 132–153

    Article  Google Scholar 

  40. Wu X, Ghaboussi J. Modeling unloading mechanism and cyclic behavior of concrete with adaptive neural networks. In: Proceeding second Asian-Pacific Conference on Computational Mechanics. Sydney, Australia, 1993

    Google Scholar 

  41. Zhang M M. Neural network material models determined from structural tests. Doctoral dissertation, Department of Civil Engineering, University of Illinois at Urbana-Champaign, 1996

    Google Scholar 

  42. Pidaparti R M, Palakal M J. Material model for composites using neural networks. AIAA Journal, 1993, 31(8): 1533–1535

    Article  Google Scholar 

  43. Ghaboussi J, Pecknold D A, Zhang M, Haj-Ali R M. Autoprogressive training of neural network constitutive models. International Journal for Numerical Methods in Engineering, 1998, 42(1): 105–126

    Article  MATH  Google Scholar 

  44. Kaklauskas G, Ghaboussi J. Stress-Strain Relations for Cracked Tensile Concrete From RC Beam Tests. Journal of Structural Engineering, 2001, 127(1): 64–73

    Article  Google Scholar 

  45. Yun G J, Ghaboussi J, Elnashai, A. S. Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections. Computer Methods in Applied Mechanics and Engineering, 2008, 2836–2857

    Google Scholar 

  46. Yun G J, Ghaboussi J, Elnashai A S. A design-variable-based inelastic hysteretic model for beam-column connections. Earthquake Engineering & Structural Dynamics, 2008, 37(4): 535–555

    Article  Google Scholar 

  47. Ghaboussi J, Sidarta D E. New method of material modeling using neural networks. In: the 6th International Symposium on Numerical Models IN geomechanics, 1997, 393–400

    Google Scholar 

  48. Dang X, Tan Y. An inner product-based dynamic neural network hysteresis model for piezoceramic actuators. Sensors and Actuators, 2005, 121(2): 535–542

    Article  Google Scholar 

  49. Yun G J, Ghaboussi J, Elnashai A S. A new neural network-based model for hysteretic behavior of materials. International Journal for Numerical Methods in Engineering, 2007, 73: 447–469

    Article  MathSciNet  MATH  Google Scholar 

  50. Ghaboussi, J., Sidarta, D. E. New nested adaptive neural networks (NANN) for constitutive modeling. Computers and Geotechnics, 1998, 22(1): 29–52

    Article  Google Scholar 

  51. Ghaboussi J, Zhang M, Wu X, Pecknold D. Nested adaptive neural network: A new architecture. In: Proceedings of international conference on artificial neural networks in engineering, ANNIE97, 1997

    Google Scholar 

  52. SAC. Protocol for fabrication, inspection, testing, and documentation of beam-to-column connections tests and other experimental specimens, SAC Steel Project Rep. No. SAC/BD- 97/02, V.1.1, Strategic Air Command, Richmond, Calif, 1997

    Google Scholar 

  53. Kim J, Ghaboussi J, Elnashai A S. Mechanical and informational modeling of steel beam-to-column connections. Engineering Structures, 2010, 32(2): 449–458

    Article  Google Scholar 

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Correspondence to Gholamreza Abdollahzadeh.

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Abdollahzadeh, G., Shabanian, S.M. Experimental and numerical analysis of beam to column joints in steel structures. Front. Struct. Civ. Eng. 12, 642–661 (2018). https://doi.org/10.1007/s11709-017-0457-z

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  • DOI: https://doi.org/10.1007/s11709-017-0457-z

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