Assessment of load carrying capacity of castellated steel beams by neural networks

In this paper, load carrying capacity of simply supported castellated steel beams, susceptible to web-post buckling, is studied. The accuracy of the nonlinear finite element (FE) method to evaluate the load carrying capacity and failure mode of the beams is discussed. In view of the high computational burden of the nonlinear finite element analysis, a parametric study is achieved based on FE and an empirical equation is proposed to estimate the web-posts’ buckling critical load of the castellated steel beams. Also as other alternatives to achieve this task, the traditional back-propagation (BP) neural network and adaptive neuro-fuzzy inference system (ANFIS) are employed. In this case, the accuracy of the proposed empirical equation, BP network and ANFIS are examined by comparing their provided results with those of conventional FE analysis. The numerical results indicate that the best accuracy associates with the ANFIS and the neural network models provide better accuracy than the proposed equations.

Research highlights
► Load carrying capacity of simply supported castellated steel beams is investigated.
► An empirical equation is proposed to estimate the load carrying capacity.
► BP and ANFIS neural network models are also examined.
► The accuracy of the neural network models is better than that of the proposed equation.
► ANFIS is superior to the BP model.