A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Fang, Sheng-En; Ren, Wei-Xin y Perera Velamazán, Ricardo (2012). A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation. "Mechanical Systems and Signal Processing", v. 33 ; pp. 83-96. ISSN 0888-3270. https://doi.org/10.1016/j.ymssp.2012.06.028.

Descripción

Título: A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation
Autor/es:
  • Fang, Sheng-En
  • Ren, Wei-Xin
  • Perera Velamazán, Ricardo
Tipo de Documento: Artículo
Título de Revista/Publicación: Mechanical Systems and Signal Processing
Fecha: Noviembre 2012
Volumen: 33
Materias:
Palabras Clave Informales: Stochastic model updating; Parameter variability; Analysis of variance; Response surface models; Monte Carlo simulation
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Mecánica Estructural y Construcciones Industriales [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Más información

ID de Registro: 22650
Identificador DC: http://oa.upm.es/22650/
Identificador OAI: oai:oa.upm.es:22650
Identificador DOI: 10.1016/j.ymssp.2012.06.028
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0888327012002634
Depositado por: Memoria Investigacion
Depositado el: 11 Mar 2014 17:14
Ultima Modificación: 21 Abr 2016 17:38
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