Steel and Composite Structures

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Patch loading resistance prediction of plate girders with multiple longitudinal stiffeners using machine learning

  • Carlos Graciano (Departamento de Ingenieria Civil, Universidad Nacional de Colombia, Facultad de Minas, Sede Medellin) ;
  • Ahmet Emin Kurtoglu (Department of Civil Engineering, Igdir University, Sehit Bulent Yurtseven Campus) ;
  • Balazs Kovesdi (Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Structural Engineering) ;
  • Euro Casanova (Universidad del Bio-Bio, Departamento Ingenieria Civil y Ambiental)
  • Received : 2023.06.04
  • Accepted : 2023.11.07
  • Published : 2023.11.25
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Abstract

This paper is aimed at investigating the effect of multiple longitudinal stiffeners on the patch loading resistance of slender steel plate girders. Firstly, a numerical study is conducted through geometrically and materially nonlinear analysis with imperfections included (GMNIA), the model is validated with experimental results taken from the literature. The structural responses of girders with multiple longitudinal stiffeners are compared to the one of girders with a single longitudinal stiffener. Thereafter, a patch loading resistance model is developed through machine learning (ML) using symbolic regression (SR). An extensive numerical dataset covering a wide range of bridge girder geometries is employed to fit the resistance model using SR. Finally, the performance of the SR prediction model is evaluated by comparison of the resistances predicted using available formulae from the literature.

Keywords

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