System Identification and Information Processing in Seismic Vulnerability Analysis of Structures

  • C. C. ThielJr.
  • A. C. Boissonnade
Conference paper


Seismic vulnerability analysis of structures generally involves processing many judgments which are based on experience and much data which is ill-defined or conjectural. A cascade model is presented for combining site hazard, design, architectural and building characteristics data that incor-porate both specific and vague information. The cascade model is Bayesian in approach in that new information is used to update prior estimates of vulnerability. The development of the seismic vulnerability evaluation model depends on rules encoded as a set of relations between linguistic variables (for example IF (antecedent 1, antecedent 2, . . .) THEN (conse-quence 1, consequence 2, . . .)). These statements are combined through a fuzzy set system identification model.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Applied Technology Council, Proceedings (1984), “Seminar on Earthquake Ground Motion and Building Damage Potential,” San Francisco, CA.Google Scholar
  2. Boissonnade, A.C., Dong, W.M., Shah, H.C. and Wong, F.C. (1985), “Identification of Fuzzy Systems in Civil Engineering,” Proceedings of International Symposium on Fuzzy Mathematics in Earthquake Researches, Beijing, China, pp. 48–71.Google Scholar
  3. Dong, W.M. and Shah, H.C. (1985), “Vertex Method for Computing Function of Fuzzy Variables,” Submitted to Journal of Fuzzy Sets and Systems.Google Scholar
  4. Dubois, D.H., Prade, H. (1980), Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.zbMATHGoogle Scholar
  5. EERI (1984), “Glossary of Terms for Probabilistic Seismic Risk and Hazard Analysis,” Earthquake Spectral, Vol. 1, No. 1.Google Scholar
  6. Kaufmann, A. and Gupta, M.M. (1985), Introduction to Fuzzy Arithmetic: Theory and Applications, Van Nostrand Reinhold Company, New York.zbMATHGoogle Scholar
  7. Mamdani, E.H. and Assilian, S. (1975), “An Experimental in Linguistic Synthesis with a Fuzzy Logic Controller,” Int. J. Man-Machine Studies 7, pp. 1–13.CrossRefzbMATHGoogle Scholar
  8. Pedrycz, W. (1984), “An Identification Algorithm in Fuzzy Relational Systems,” J. Fuzzy Sets and Systems 13, pp. 153–167.CrossRefzbMATHMathSciNetGoogle Scholar
  9. Thiel, C.C. and Boissonnade, A.C. (1984), “Divergence Between Estimated Building Vulnerability and Observed Damage: a Fuzzy Set Theory Reconciliation,” in Proceeding of the ATC-10 Con-ference, Applied Technology Council, Palo Alto, California.Google Scholar
  10. Uniform Building Code (1982), International Conference of Building Officials, Whittier, California.Google Scholar
  11. Zadeh, L.A. (1964), “Fuzzy Sets,” J. Fuzzy Sets and Systems, Memo, EREL., No. 64–44, University of California, Berkeley.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • C. C. ThielJr.
    • 1
  • A. C. Boissonnade
    • 1
  1. 1.Stanford UniversityUSA

Personalised recommendations