This paper discusses the implementation of an Artificial Neural Network (ANN) model for predicting the windinduced, vertical displacements of cable net roofs with a hyperbolic paraboloid shape. The ANN model was calibrated by training and comparing three preliminary ANN topologies. The first one was trained using both experimental pressure coefficients on similar building geometries and wind-induced vertical displacements estimated by Finite Element Method (FEM). This first set was subsequently used to calibrate a second ANN model, which was based on surrogate modeling of pressure coefficients, calculated by polynomial fitting of the experimental data. Further generalization of the pressure coefficients was obtained through parametrization of the polynomial fitting as a function of geometrical properties. Pressure loads were used to calibrate the third ANN, which was employed to synthetically generate vertical displacements of a varied geometrical sample. The ANN-based models exhibit remarkable results; the coefficient of determination between physically-estimated flexible roof displacements and approximated values was usually above 0.90 for all the tested geometries, suggesting that this type of surrogate model is capable of replicating complex, geometrically nonlinear structural behavior.

Examination of artificial neural networks to predict wind-induced displacements of cable net roofs

Fabio Rizzo
;
2021-01-01

Abstract

This paper discusses the implementation of an Artificial Neural Network (ANN) model for predicting the windinduced, vertical displacements of cable net roofs with a hyperbolic paraboloid shape. The ANN model was calibrated by training and comparing three preliminary ANN topologies. The first one was trained using both experimental pressure coefficients on similar building geometries and wind-induced vertical displacements estimated by Finite Element Method (FEM). This first set was subsequently used to calibrate a second ANN model, which was based on surrogate modeling of pressure coefficients, calculated by polynomial fitting of the experimental data. Further generalization of the pressure coefficients was obtained through parametrization of the polynomial fitting as a function of geometrical properties. Pressure loads were used to calibrate the third ANN, which was employed to synthetically generate vertical displacements of a varied geometrical sample. The ANN-based models exhibit remarkable results; the coefficient of determination between physically-estimated flexible roof displacements and approximated values was usually above 0.90 for all the tested geometries, suggesting that this type of surrogate model is capable of replicating complex, geometrically nonlinear structural behavior.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/246769
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