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Determination of Hysteretic Behavior of Steel End-Plate Beam-to-Column Connection with Mechanical and Neural Network Modeling

  • Research Article - Civil Engineering
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Abstract

Behavior of beam-to-column connections in steel structures is characterized by the strength, stiffness and stability of them. Accurate modeling of stiffness, strength and ductility of these connections is important, especially when one considers dynamic or seismic loading. In this study, after determining the experimental behavior of the end-plate beam-to-column connection under cyclic loading, behavior of mechanical and neural network models of this connection has been evaluated. To validate the models, hysteresis response of mechanical model and computational intelligence model is compared with the experimental one. In the mechanical model, deformations of the connection are expressed only based on the geometric and material properties. On the other hand, in computational intelligence techniques with neural networks, requisite information is extracted directly from the experimental data. Hence, both models have pros and cons that are discussed in present paper. It is seen that as the mechanical model does not consider all aspects of hysteresis response, this model is not sufficiently accurate compared with neural network model. The neural network model is capable of characterizing pinching and stiffness degradation in hysteresis curve of the connection.

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Authors and Affiliations

  1. Department of Civil Engineering, Babol University of Technology, Babol, Iran

    Gholamreza Abdollahzadeh, Seyed Mojtaba Hashemi & Hamidreza Tavakoli

  2. Department of Basic Sciences, University of Tehran, Tehran, Iran

    Hosein Rahami

Authors
  1. Gholamreza Abdollahzadeh
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  2. Seyed Mojtaba Hashemi
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  3. Hamidreza Tavakoli
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  4. Hosein Rahami
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Correspondence to Gholamreza Abdollahzadeh.

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Abdollahzadeh, G., Hashemi, S.M., Tavakoli, H. et al. Determination of Hysteretic Behavior of Steel End-Plate Beam-to-Column Connection with Mechanical and Neural Network Modeling. Arab J Sci Eng 39, 7661–7671 (2014). https://doi.org/10.1007/s13369-014-1348-4

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  • DOI: https://doi.org/10.1007/s13369-014-1348-4

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