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