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Published August 2007 | Published
Journal Article Open

Propagating waves in the steel, moment-frame factor building recorded during earthquakes

Abstract

Wave-propagation effects can be useful in determining the system identification of buildings such as the densely instrumented University of California, Los Angeles, Factor building. Waveform data from the 72-channel array in the 17-story moment-resisting steel frame Factor building are used in comparison with finiteelement calculations for predictive behavior. The high dynamic range of the 24-bit digitizers allows both strong motions and ambient vibrations to be recorded with reasonable signal-to-noise ratios. A three-dimensional model of the Factor building has been developed based on structural drawings. Observed displacements for 20 small and moderate, local and regional earthquakes were used to compute the impulse response functions of the building by deconvolving the subbasement records as representative input motions at its base. The impulse response functions were then stacked to bring out wave-propagation effects more clearly. The stacked data are used as input into theoretical dynamic analysis simulations of the building's response.

Additional Information

© 2007 Seismological Society of America. Manuscript received 3 July 2006. We are deeply appreciative of comments provided by Roel Snieder, Paul Davis, and an anonymous reviewer whose detailed comments greatly improved this manuscript. We also thank Swami Krishnan for advice on building the ETABS model and Lucy Jones for use of USGS Pasadena office facilities. This work was supported by the National Science Foundation through the Center for Embedded Networked Sensing under Grant CCF-0120778 and the Southern California Earthquake Center under Cooperative Agreement EAR-0106924 and USGS Cooperative Agreement 02HQAG0008. Operations and maintenance of the Factor seismic array were supported by personnel and funds from the U.S. Geological Survey Advanced National Seismic System (Grant 05HQGR0159) program and the NSF Center for Embedded Networked Sensing at UCLA.

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August 22, 2023
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October 17, 2023