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Published February 2015 | Published
Journal Article Open

Prediction of Wave Propagation in Buildings Using Data from a Single Seismometer

  • 1. ROR icon California Institute of Technology

Abstract

Crowd‐sourced seismic networks in buildings collect important scientific data, in addition to allowing a diverse audience to visualize the vibrations of buildings. Visualization of a building's deformation requires spatiotemporal interpolation of motions from seismometers that are located wherever the crowd places them. In many cases, a crowd‐sourced building network may actually be just a single seismometer. A method to rapidly estimate the total displacement response of a building based on limited observational data, in some cases from only a single seismometer, is presented. In general, the earliest part of the response is simulated by assuming a vertically propagating shear wave. Later motions are simulated using mode shapes derived from a beam model (a shear beam, or more generally a Timoshenko beam), the parameters of which are determined from the ratios of the modal frequencies and the building's exterior dimensions. The method is verified by (1) comparing predicted and actual records from a 54‐story building in downtown Los Angeles, California, and (2) comparing finite‐element simulations of the 17‐story University of California, Los Angeles (UCLA) Factor building. The response of each of these buildings can be simulated with a simple shear beam. The importance of including the traveling wave part of the solution depends on the characteristics of the base ground shaking; the traveling wave becomes more apparent as the excitation becomes more impulsive. The method can be straightforwardly applied to multiple instrumented buildings, resulting in a tool to visualize linear elastic motions of those buildings.

Copyright and License

© 2015 Seismological Society of America.

Acknowledgement

This work is partially supported by the National Science Foundation (EAR-1027790), George Housner Earthquake Engineering Research Endowment (EAS-41212), Fred L. Hartley Family Foundation, and the Croucher Foundation. Their support is gratefully acknowledged. The authors are grateful to an anonymous reviewer and M. I. Todorovska for their constructive comments, which significantly improved the article.

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Created:
August 22, 2023
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