This series includes technical reports prepared by faculty, students and staff who are associated with the John A. Blume Earthquake Engineering Center at Stanford University. While the primary focus of Blume Center is earthquake engineering, many of the reports in this series encompass broader topics in structural engineering and materials, computational mechanics, geomechanics, structural health monitoring, and engineering life-cycle risk assessment. Each report includes acknowledgments of the specific sponsors for the report and underlying research. In addition to providing research support, the Blume Center provides administrative support for maintaining and disseminating the technical reports. For more information about the Blume Center and its activities, see https://blume.stanford.edu.
The main topics of this research are: development of methods for determining vulnerability curves of structures under seismic loading, formulation of loss estimation procedures for specific structures and regions, use of pattern recognition and fuzzy set theory in the evaluation of seismic intensity and damage forecasting.
After a detailed review of currently available damage assessment models, vulnerability relationships are derived for both single and groups of structures. The derivation includes consideration of uncertainties of loading (demand) of structural resistance (capacity) and modeling. Quantifiable and nonquantifiable factors (such as construction quality and architectural details) which influence the performance of the structures are considered with probability and fuzzy set models, respectively.
Two methods of identifying earthquake intensity are presented and compared. The first method is based on the theory of pattern recognition where a discriminative function is developed based on Bayes' criterion. The second method applies the theory of fuzzy sets. These methods are used for the purpose of intensity classification after an earthquake and also for checking past classifications. Damage severity versus intensity relations consistent with the actual damage data and intensity classification are derived.
Boissonnade, A.C. and Shah, H.C.. (1985). Use of Pattern Recognition and Fuzzy Sets in Seismic Risk Analysis. John A. Blume Earthquake Engineering Center Technical Report 67. Stanford Digital Repository. Available at: http://purl.stanford.edu/fk842wt4870
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