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Title: Feature extraction for structural dynamics model validation

This study focuses on defining and comparing response features that can be used for structural dynamics model validation studies. Features extracted from dynamic responses obtained analytically or experimentally, such as basic signal statistics, frequency spectra, and estimated time-series models, can be used to compare characteristics of structural system dynamics. By comparing those response features extracted from experimental data and numerical outputs, validation and uncertainty quantification of numerical model containing uncertain parameters can be realized. In this study, the applicability of some response features to model validation is first discussed using measured data from a simple test-bed structure and the associated numerical simulations of these experiments. issues that must be considered were sensitivity, dimensionality, type of response, and presence or absence of measurement noise in the response. Furthermore, we illustrate a comparison method of multivariate feature vectors for statistical model validation. Results show that the outlier detection technique using the Mahalanobis distance metric can be used as an effective and quantifiable technique for selecting appropriate model parameters. However, in this process, one must not only consider the sensitivity of the features being used, but also correlation of the parameters being compared.
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [3] ;  [2]
  1. Los Alamos National Laboratory
  2. UNIV OF TOKYO
  3. UNIV OF SHEFFIELD
Publication Date:
OSTI Identifier:
1040824
Report Number(s):
LA-UR-10-07534; LA-UR-10-7534
TRN: US201211%%193
DOE Contract Number:
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: IMAC XXIX A Conference and Exposition on Structural Dynamics ; January 31, 2011 ; Jacksonville, FL
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; DETECTION; METRICS; SENSITIVITY; SPECTRA; STATISTICAL MODELS; STATISTICS; VALIDATION; VECTORS