Abstract
A new blind identification technique is devised within a probabilistic framework to identify site effects from ground surface accelerograms. Ground response analysis is an essential part of most seismic assessment and ground motion characterization efforts and is fraught with inherent complexities due to epistemic as well as aleatory uncertainties. Whereas an extensive collection of analytical and numerical techniques is available to analyze ground responses, their accuracy depends on a priori knowledge of site properties and the availability of appropriate input motions. In the probabilistic blind identification method proposed herein, the site response is identified using only the ground surface accelerations recorded at multiple stations, under the assumption that the unknown input motion is similar for all these stations. The few competing site response identification methods rely on the strategic selection of a reference station and the present approach obviates this limitation and additionally yields the uncertainty of the identified results.
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Notes
\(E\{ .\}\) is expected value.
Although it is possible to devise a second-order EKF by selecting the first three Taylor expansion terms, the computational expense of such a technique would be immense, as it would require the calculation of a Hessian matrix.
We did not use normalization that are commonly used in classic definitions of RMSE, because \(\ddot{x}_{b} \left[ n \right]\) is the same for all cases.
Data is publicly accessible through Center for Engineering Strong Motion Data (CESMD): www.strongmotioncenter.org.
Channels #6 and #5 measure foundation responses of CSMIP#58411 and #58412 along the east–west direction, respectively.
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Acknowledgements
Major support for this research was provided by the California Department of Conservation/California Geological Survey (CGS) under Research Contract No. 1015-973, which is gratefully acknowledged. The authors would also like to acknowledge Profs. Mojtaba Mahsuli, Steven Glaser, Gerald Enzner, and the anonymous reviewers for their valuable comments and guidance. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
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Ghahari, S.F., Abazarsa, F. & Taciroglu, E. Probabilistic blind identification of site effects from ground surface signals. Bull Earthquake Eng 16, 1079–1104 (2018). https://doi.org/10.1007/s10518-017-0253-0
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DOI: https://doi.org/10.1007/s10518-017-0253-0