Steel and Composite Structures

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Prediction of the flexural overstrength factor for steel beams using artificial neural network

  • Guneyisi, Esra Mete (Department of Civil Engineering, Gaziantep University) ;
  • D'niell, Mario (Department of Structures for Engineering and Architecture, University of Naples "Federico II") ;
  • Landolfo, Raffaele (Department of Structures for Engineering and Architecture, University of Naples "Federico II") ;
  • Mermerdas, Kasim (Department of Civil Engineering, Hasan Kalyoncu University)
  • Received : 2013.10.10
  • Accepted : 2014.07.11
  • Published : 2014.09.25
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Abstract

The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.

Keywords

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