Journal of Seismology

, Volume 11, Issue 3, pp 299–310 | Cite as

Estimating the confidence of earthquake damage scenarios: examples from a logic tree approach

Original Article


Earthquake loss estimation is now becoming an important tool in mitigation planning, where the loss modeling usually is based on a parameterized mathematical representation of the damage problem. In parallel with the development and improvement of such models, the question of sensitivity to parameters that carry uncertainties becomes increasingly important. We have to this end applied the capacity spectrum method (CSM) as described in FEMA HAZUS-MH. Multi-hazard Loss Estimation Methodology, Earthquake Model, Advanced Engineering Building Module. Federal Emergency Management Agency, United States (2003), and investigated the effects of selected parameters. The results demonstrate that loss scenarios may easily vary by as much as a factor of two because of simple parameter variations. Of particular importance for the uncertainty is the construction quality of the structure. These results represent a warning against simple acceptance of unbounded damage scenarios and strongly support the development of computational methods in which parameter uncertainties are propagated through the computations to facilitate confidence bounds for the damage scenarios.


Seismic risk Vulnerability Damage scenarios Uncertainties Capacity spectrum method 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ambraseys N, Simpson K, Bommer J (1996) Prediction of horizontal response spectra in Europe. Earthq Eng Struct Dyn 25:371–400CrossRefGoogle Scholar
  2. Antoniou S, Pinho R (2004) Development and verification of a displacement-based adaptive pushover procedure. J Earthq Eng 8(5):643–661CrossRefGoogle Scholar
  3. ATC-40 (1996) Applied technology council, seismic evaluation and retrofit of concrete buildings, vol 1. California, United StatesGoogle Scholar
  4. Bommer JJ, Abrahamson NA (2006) Why do modern probabilistic seismic-hazard analyses often lead to increased hazard estimates? Bull Seismol Soc Am 96(6):1967–1999CrossRefGoogle Scholar
  5. Bommer J, Spence R, Erdik M, Tabuchi S, Aydinoglu N, Booth E, del Re D, Peterken O (2002) Development of an earthquake loss model for Turkish catastrophe insurance. J Seismol 6(3):431–446CrossRefGoogle Scholar
  6. Bommer JJ, Scherbaum F, Bungum H, Cotton F, Sabetta F, Abrahamson NA (2005) On the use of logic trees for ground-motion prediction equations in seismic-hazard analysis. Bull Seismol Soc Am 95(2):377–389CrossRefGoogle Scholar
  7. Budnitz RJ, Apostolakis G, Boore DM, Cluff LS, Coppersmith KJ, Cornell CA, Morris PA (1997) Recommendations for probabilistic seismic hazard analysis: guidance on uncertainty and use of experts. U.S. Nuclear Regulatory Commission Report NUREG/CR-6372Google Scholar
  8. Calvi GM, Pinho R, Magenes G, Bommer JJ, Restrepo-Velez LF, Crowley H (2006) Development of seismic vulnerability assessment methodologies over the past 30 years. ISET J Earthq Technol 43(3):75–104Google Scholar
  9. Coburn A, Spence R (2002) Earthquake protection, 2nd edn. John Wiley & SonsGoogle Scholar
  10. Cotton F, Scherbaum F, Bommer JJ, Bungum H (2006) Criteria for selecting and adjusting ground-motion models for specific target regions: application to Central Europe and rock sites. J Seismol 10:137–156CrossRefGoogle Scholar
  11. Crowley H, Pinho R, Bommer JJ (2004) A probabilistic displacement-based vulnerability assessment procedure for earthquake loss estimation. Bull Earthq Eng 2:173–219CrossRefGoogle Scholar
  12. Crowley H, Bommer JJ, Pinho R, Bird J (2005) The impact of epistemic uncertainty on an earthquake loss model. Earthq Eng Struct Dyn 34:1653–1685CrossRefGoogle Scholar
  13. Douglas J, Bungum H, Scherbaum F (2006) Ground-motion prediction equations for southern Spain and southern Norway obtained using the composite model perspective. J Earthq Eng 10(1):33–72CrossRefGoogle Scholar
  14. Fajfar P (1999) Capacity spectrum method based on inelastic demand spectra. Earthq Eng Struct Dyn 28:979–993CrossRefGoogle Scholar
  15. FEMA (2003) HAZUS-MH. Multi-hazard loss estimation methodology, earthquake model, advanced engineering building module. Federal Emergency Management Agency, United StatesGoogle Scholar
  16. Freeman SA, Nicoletti JP, Tyrell JV (1975) Evaluations of existing buildings for seismic risk - a case study of Puget Sound Naval Shipyard, Bremerton, Washington. In: Proceedings of U.S. national conference on earthquake engineering, Berkeley, United States, pp 113–122Google Scholar
  17. Giovinazzi S (2005) The vulnerability assessment and the damage scenario in seismic risk analysis. PhD. Thesis, Technical University Carolo-Wilhelmina at Braunschweig, Braunschweig, Germany and University of Florence, Florence, ItalyGoogle Scholar
  18. Grossi PA (2000) Quantifying the uncertainty in seismic risk and loss estimation. In: Proceedings of the second Euroconference on global change and catastrophe risk management: earthquake risks in Europe, Austria, pp 1–13Google Scholar
  19. Grünthal G (ed) (1998) European macroseismic scale 1998 (updated MSK-scale). Cahiers du Centre Européen de Geodynamique et de Séismologie, vol 7. LuxembourgGoogle Scholar
  20. ICBO (1997) 1997 Uniform building code (UBC), structural engineering design provisions. International conference of building officials, vol 2. Whittier, CaliforniaGoogle Scholar
  21. Kircher CA, Nassar AA, Kustu O, Holmes WT (1997a) Development of building damage functions for earthquake loss estimation. Earthq Spectra 13(4):663–682CrossRefGoogle Scholar
  22. Kircher CA, Reitherman RK, Whitman RV, Arnold C (1997b) Estimation of earthquake losses to buildings. Earthq Spectra 13(4):703–720CrossRefGoogle Scholar
  23. Molina S, Lindholm CD (2005) A logic tree extension of the capacity spectrum method developed to estimate seismic risk in Oslo, Norway. J Earthq Eng 9(6):877–897CrossRefGoogle Scholar
  24. Newmark NM, Hall WJ (1982) Earthquake spectra and design. Earthquake Engineering Research Institute, EERIGoogle Scholar
  25. Pinho R, Bommer JJ, Glaister S (2002) A simplified approach to displacement-based earthquake loss estimation analysis. In: Proceedings of the 12th European conference on earthquake engineering, pp 1–11Google Scholar
  26. Porter KA, Beck JL, Shaikhutdinov RV (2002) Sensitivity of building loss estimates to major uncertain variables. Earthq Spectra 18(4):719–743CrossRefGoogle Scholar
  27. Scherbaum F, Bommer JJ, Bumgum H, Cotton F, Abrahamson NA (2005) Composite ground-motion models and logic trees: methodology, sensitivities and uncertainties. Bull Seismol Soc Am 95(5):1575–1593CrossRefGoogle Scholar
  28. Sousa ML, Campos Costa A, Carvalho A, Coelho E (2004) An automatic seismic scenario loss methodology integrated on a geographic information system. Proceedings of the 13th world conference on earthquake engineering, Vancouver, Canada, Paper No. 2526 (on CD)Google Scholar
  29. Stepp JC, Wong I, Whitney J, Quittemeyer R, Abrahamson NA, Toro G, Youngs R, Coppersmith K, Savy J, Sullivan T, the Yucca Mountain PSHA Project Members (2001) Probabilistic seismic hazard analyses for ground motions and fault displacements at Yucca Mountain, Nevada. Earthq Spectra 17(1):113–151CrossRefGoogle Scholar
  30. Toro GR, Abrahamson NA, Schneider JF (1997) Model of strong ground motions from earthquakes in central and eastern North America: best estimates and uncertainties. Seismol Res Lett 68:41–57Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Departamento de Ciencias de la Tierra y del Medio Ambiente, Facultad de CienciasUniversidad de AlicanteAlicanteSpain
  2. 2.NORSAR/ICGKjellerNorway

Personalised recommendations