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Seismic Design and Performance Assessment of Steel Frames Considering Joints' Behaviour
Author(s) -
Lemma Melaku,
Rebelo Carlos,
Silva Luís
Publication year - 2021
Publication title -
ce/papers
Language(s) - English
Resource type - Journals
ISSN - 2509-7075
DOI - 10.1002/cepa.1510
Subject(s) - structural engineering , stiffness , opensees , joint (building) , parametric statistics , eurocode , nonlinear system , beam (structure) , seismic analysis , engineering , deck , moment (physics) , seismic loading , framing (construction) , finite element method , mathematics , statistics , physics , classical mechanics , quantum mechanics
Steel joints play an important role in the overall seismic behavior of steel frames. In the case of beam‐to‐column joints, the joints are composed of two regions: the connection where the beam and column are fastened together by mechanical means, and the panel zone, which is the region in the column web where deformation occurs. The deformations in the panel zone of the beam‐to‐column joint could significantly affect the seismic behavior of steel joints. In addition, the stiffness of the connection also plays a role. This paper aims to assess the effect of joint modelling on design and the seismic performance of steel moment resisting frames (MRFs) through nonlinear analyses using different modelling strategies. A parametric study is conducted on three steel moment resisting frames of 3, 6 and 9 storeys. These frames are designed for both gravity and seismic load cases according to the current Eurocodes. Beam‐to‐Column joints are designed and characterized based on the pre‐normative design recommendations obtained in the scope of the Equaljoints project. Consequently, simplified modelling methods that disregard the joints, and refined modelling techniques that account for the joints' dimension and behavior are adopted for three different joint types classified based on stiffness and strength of the joints. A systematic assessment of the performance of each frame is assessed through nonlinear analyses. An integrated design and performance assessment tool is developed in Python and implemented using OpenSees.