Utilization of artificial neural networks for global sensitivity analysis of model output

Utilization of artificial neural networks for global sensitivity analysis of model output

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The paper deals with the application of artificial neural networks to sensitivity measurement of the output quantity to the variability of input quantities. The original nonlinear FEM model calculates ultimate load-bearing capacity of a T-shaped prestressed concrete roof girder. Latin hypercube sampling algorithm is used to generate samples of input variables. The global Sobol sensitivity analysis is proposed to understand the effect of the input variability on the quantity of interest. The outputs of the Sobol sensitivity analysis are verified by subsequent two sensitivity analyses. The first studies show that artificial neural networks are very promising for effective evaluation of global sensitivity analysis. Artificial neural networks do not eliminate mutual interaction among input quantities; it is a very important piece of knowledge connected with maintaining the satisfactory accurateness of the reliability computation.

The paper deals with the application of artificial neural networks to sensitivity measurement of the output quantity to the variability of input quantities. The original nonlinear FEM model calculates ultimate load-bearing capacity of a T-shaped prestressed concrete roof girder. Latin hypercube sampling algorithm is used to generate samples of input variables. The global Sobol sensitivity analysis is proposed to understand the effect of the input variability on the quantity of interest. The outputs of the Sobol sensitivity analysis are verified by subsequent two sensitivity analyses. The first studies show that artificial neural networks are very promising for effective evaluation of global sensitivity analysis. Artificial neural networks do not eliminate mutual interaction among input quantities; it is a very important piece of knowledge connected with maintaining the satisfactory accurateness of the reliability computation.

artificial neural networks, Sobol, global sensitivity analysis, interactions, reliability

artificial neural networks, Sobol, global sensitivity analysis, interactions, reliability

KALA, Z.; LEHKÝ, D.; NOVÁK, D.

2020

24.07.2019

American Institute of Physics Inc.

MELVILLE, USA

978-0-7354-1854-7

AIP Conference Proceedings

0094-243X

AIP conference proceedings

2116

Spojené státy americké

120005-1

120005-4

4

https://aip.scitation.org/doi/abs/10.1063/1.5114107

http://hdl.handle.net/