Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT156381,
  author="Zdeněk {Kala} and David {Lehký} and Drahomír {Novák}",
  title="Utilization of artificial neural networks for global sensitivity analysis of model output",
  annote="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.",
  address="American Institute of Physics Inc.",
  booktitle="AIP Conference Proceedings",
  chapter="156381",
  doi="10.1063/1.5114108",
  howpublished="online",
  institution="American Institute of Physics Inc.",
  year="2019",
  month="july",
  pages="120005-1--120005-4",
  publisher="American Institute of Physics Inc.",
  type="conference paper"
}