Feature Extraction for Structural Dynamics Model Validation Mayuko Nishio1, Francois Hemez2, Keith Worden3, Gyuhae Park4, Nobuo Takeda5, Charles Farrar6 1 University of Tokyo, Dept. Civil Engineering, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan, nishio@bridge.t.u-tokyo.ac.jp 2 Los Alamos National Laboratory, XCP-1, Los Alamos, NM 87545, hemez@lanl.gov 3 University of Sheffield, Dept. Mechanical Engineering, Mappin St., Sheffield S1 3JD, UK, k.worden@sheffield.ac.uk 4 Los Alamos National Laboratory, INST-OFF, Los Alamos, NM 87545, gpark@lanl.gov 5 University of Tokyo, Dept. Advanced Energy, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan, takeda@smart.k.u-tokyo.ac.jp 6 Los Alamos National Laboratory, INST-OFF, Los Alamos, NM 87545, farrar@lanl.gov ABSTRACT 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 timeseries 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. 1. Introduction The purpose of structural model validation is to assess whether a numerical model, such as a finite element model, has adequate predictive capability for the model’s intended purpose by comparing analytical predicted and experimentally observed structural responses quantities. In constructing numerical models of structures, many quantities are assigned based on incomplete and/or unavailable knowledge of their true value. Uncertainties can result from measurement error, environmental while others are due to lack-of-knowledge about the actual structural condition; i.e., materials, loads, friction, energy dissipation (damping), and boundary condition. Therefore, it is important to assess whether the assumptions used in the modeling process provide accurate simulations on the intended purpose of the model. T. Proulx (ed.), Linking Models and Experiments, Volume 2, Conference Proceedings of the Society for Experimental Mechanics Series 5, 153 variability, allowable manufacturing tolerances and variability associated with assembly procedures; DOI 10.1007/978-1-4419-9305-2_10, © The Society for Experimental Mechanics, Inc. 2011
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