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Use of Pattern Recognition and Fuzzy Sets in Seismic Risk Analysis

Author/Creator:
Boissonnade, AC (Author)
Shah, HC (Author)
Date created:
January 1985
Type of resource:
Text
Genre:
Technical report
Format:
Book
Abstract:
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.
Preferred Citation:
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
Collection:
John A. Blume Earthquake Engineering Center Technical Report Series
Related item:
John A. Blume Earthquake Engineering Center
Subject:
damage detection
seismic design
seismic performance
Use and reproduction:
User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
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