Identification of structural parameters and boundary conditions using a minimum number of measurement points

Abstract

This article proposes a novel methodology that uses mathematical and numerical models of a structure to build a data set and determine crucial nodes that possess the highest sensitivity. Regression surfaces between the structural parameters and structural output features, represented by the natural frequencies of the structure and local transmissibility, are built using the numerical data set. A description of a possible experimental application is provided, where sensors are mounted at crucial nodes, and the natural frequencies and local transmissibility at each natural frequency are determined from the power spectral density and the power spectral density ratios of the sensor responses, respectively. An inverse iterative process is then applied to identify the structural parameters by matching the experimental features with the available parameters in the myriad numerical data set. Three examples are presented to demonstrate the feasibility and efficacy of the proposed methodology. The results reveal that the method was able to accurately identify the boundary coefficients and physical parameters of the Euler-Bernoulli beam as well as a highway bridge model with elastic foundations using only two measurement points. It is expected that the proposed method will have practical applications in the identification and analysis of restored structural systems with unknown parameters and boundary coefficients.

Keywords

structural model validation / eigenvalue problem / response surface / inverse problems

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Acknowledgements

The research described in this paper was funded by the Mid-America Transportation Center through a grant from the US Department of Transportation’s University Transportation Centers Program (Grant No. DOT 69A3551747107). The contents reflect the views of the authors, who are responsible for the veracity and accuracy of the information presented herein and are not necessarily representative of the views of the sponsoring agencies.

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