BEAM TRANSVERSAL AREA DIMENSIONS OPTIMIZATION USING ARTIFICIAL NEURAL NETWORKS
Palavras-chave:
Artificial Neural Networks, Structural Engineering, Python LanguageResumo
The present work shows the analysis of a numerical experimental test that was performed using
randomized and combined data to study the bending behavior of beams, through the deflection equation
considering plane stresses in two different examples. The first is a cantilever beam with force concentrated at the
free end and the second is a pinned-pinned beam with loading uniformly distributed along the span. The variables
width, height, length, longitudinal modulus of elasticity, deflection, and loading were used as estimated parameters
to calculate the ideal width and height dimensions for each beam and obtain a structural optimization considering
the limits of deformation according to ABNT NBR 6118/2014. The data generation was generated in Excel
spreadsheet format and worked in an Artificial Neural Networks in TensorFlow Python language, with six hidden
layers. In addition, the functions 'mae', 'sgd' and 'loss' were used as optimizers or activation function in TensorFlow.
