Advertisement

Influence of ground-motion correlation on probabilistic assessments of seismic hazard and loss: sensitivity analysis

  • Vladimir SokolovEmail author
  • Friedemann Wenzel
Original Research Paper

Abstract

Recent studies have shown that the proper treatment of ground-motion variability and, particularly, the correlation of ground motion are essential for the estimation of the seismic hazard, damage and loss for distributed portfolios. In this work we compared the effects of variations in the between-earthquake correlation and in the site-to-site correlation on probabilistic estimations of seismic damage and loss for the extended objects (hypothetical portfolio) and critical elements (e.g. bridges) of a network. Taiwan Island has been chosen as a test case for this study because of relatively high seismicity and previous experience in earthquake hazard modelling. The hazard and loss estimations were performed using Monte Carlo approach on the basis of stochastic catalogues and random ground-motion fields. We showed that the influence of correlation on parameters of seismic hazard, characteristics of loss distribution and the probability of damage depend, on one hand, on level of hazard and probability level of interest (return period) and, on the other hand, the relative influence of each type of correlation is not equal.

Keywords

Probabilistic seismic hazard and loss estimation Ground-motion correlation 

References

  1. Abrahamson NA, Youngs RR (1992) A stable algorithm for regression analysis using the random effects model. Bull Seism Soc Am 82: 505–510Google Scholar
  2. Abrahamson N, Atkinson G, Boore D, Bozorgnia Y, Campbell K, Chiou B, Idriss IM, Silva W, Youngs R (2008) Comparison of the NGA ground-motion relations. Earthq Spectra 24(1): 45–66. doi: 10.1193/1.2924363 CrossRefGoogle Scholar
  3. Bal IE, Bommer JJ, Stafford PJ, Crowley H, Pinho R (2010) The influence of geographical resolution of urban exposure data in an earthquake loss model for Istanbul. Earthq Spectra 26(3): 619–634. doi: 10.1193/1.3459127 CrossRefGoogle Scholar
  4. Bazzurro P, Luco N (2005) Accounting for uncertainty and correlation in earthquake loss estimation. In: Proceedings of 9’ International Conference on Safety and Reliability of Engineering Systems and Structures (ICOSSAR) 2005, Rome, ItalyGoogle Scholar
  5. Bommer JJ, Crowley H (2006) The influence of ground-motion variability in earthquake loss modelling. Bull Earthquake Eng 4(3): 231–248. doi: 10.1007/s10518-006-9008-z CrossRefGoogle Scholar
  6. Boore DM, Joyner WB, Fumal TE (1997) Equations for estimating horizontal response spectra and peak acceleration from western North American earthquakes: a summary of recent work. Seismol Res Lett 68: 128–153Google Scholar
  7. Boore DM, Gibbs JF, Joyner WB, Tinsley JC, Ponti DJ (2003) Estimated ground motion from the 1994 Northridge, California, Earthquake at the site of the Interstate 10 and La Cienega Boulevard Bridge collapse, West Los Angeles, California. Bull Seism Soc Am 93: 2737–2751. doi: 10.1785/0120020197 CrossRefGoogle Scholar
  8. Bradley B (2010) Site-specific and spatially distributed ground-motion prediction of acceleration spectrum intensity. Bull Seism Soc Am 100: 792–801. doi: 10.1785/0120090157 CrossRefGoogle Scholar
  9. Brillinger DR, Preisler HK (1984) An exploratory analysis of the Joyner-Boore attenuation data. Bull Seism Soc Am 74: 1441–1450Google Scholar
  10. Brillinger DR, Preisler HK (1985) Further analysis of the Joyner-Boore attenuation data. Bull Seism Soc Am 75: 611–614Google Scholar
  11. Campbell KW, Thenhaus PC, Barnard TP, Hampson DB (2002) Seismic hazard model for loss estimation and risk management in Taiwan. Soil Dyn Earthq Eng 22: 743–754CrossRefGoogle Scholar
  12. Cheng CT, Chiou SJ, Lee CT, Tsai YB (2007) Study of probabilistic seismic hazard maps of Taiwan after Chi-Chi earthquake. J GeoEng 2(1):19–28 http://www.sinotech.org.tw/gerc-ctr/2007.files/papers_pdf/cheng/2007-8.pdf Google Scholar
  13. Cheng CT, Lee CT, Lin PS, Lin BS, Tsai YB, Chiou SJ (2010) Probabilistics earthquake hazard in metropolitan Taipei and its surrounding regions. Terrest Atmos Oceanic Sci 21(3): 429–446. doi: 10.3319/TAO.2009.11.11.01(TH) CrossRefGoogle Scholar
  14. Cornell CA (1968) Engineering seismic risk analysis. Bull Seism Soc Am 58: 1583–1606Google Scholar
  15. Crowley H, Bommer JJ, Pihno R, Bird J (2005) The impact of epistemic uncertainty on an earthquake loss model. Earthq Eng Struct Dyn 34: 1653–1685. doi: 10.1002/eqe.498 CrossRefGoogle Scholar
  16. Crowley H, Bommer JJ (2006) Modelling seismic hazard in earthquake loss models with spatially distributed exposure. Bull Earthquake Eng 4: 249–273. doi: 10.1007/s10518-006-9011-4 CrossRefGoogle Scholar
  17. Crowley H, Bommer JJ, Stafford PJ (2008) Recent developments in the treatment of ground-motion variability in earthquake loss model. J Earthq Eng 12(S): 71–80. doi: 10.1080/13632460802013529 CrossRefGoogle Scholar
  18. Douglas J (2003) Earthquake ground motion estimation using strong-motion records: a review of equations for estimation of peak ground acceleration and spectral ordinates. Earth Sci Rev 61: 43–104CrossRefGoogle Scholar
  19. Douglas J (2006) Errata and additions to “Ground motion estimation equations 1966–2003”, BRGM/RP-54603-FRGoogle Scholar
  20. Ebel JE, Kafka AL (1999) A Monte Carlo approach to seismic hazard analysis. Bull Seism Soc Am 89: 854–866Google Scholar
  21. Evans JR, Baker JW (2006) Spatial correlation of ground motions in NGA data. American Geophysical Union, Fall Meeting 2006, abstract #S12B-01Google Scholar
  22. FEMA: (2003) HAZUS-MH MRS, Technical manual. Federal Emergency Management Agency, Washington, DCGoogle Scholar
  23. Gardner JK, Knopoff L (1974) Is the sequence of earthquakes in Southern California with aftershocks removed Poissonian? Yes. Bull Seism Soc Am 64: 1363–1367Google Scholar
  24. Goda K, Hong HP (2008a) Spatial correlation of peak ground motions and response spectra. Bull Seism Soc Am 98: 354–365. doi: 10.1785/0120070078 CrossRefGoogle Scholar
  25. Goda K, Hong HP (2008b) Estimation of seismic loss for spatially distributed buildings. Earthq Spectra 24: 889–910. doi: 10.1193/1.2983654 CrossRefGoogle Scholar
  26. Goda K, Hong HP (2009) Deaggregation of seismic loss of spatially distributed buildings. Bull Earthquake Eng 7: 255–272. doi: 10.1007/s10518-008-9093-2 CrossRefGoogle Scholar
  27. Goda K, Atkinson GM (2009) Probabilistic characterization of spatially correlated response spectra for earthquakes in Japan. Bull Seism Soc Am 99: 3003–3020. doi: 10.1785/0120090007 CrossRefGoogle Scholar
  28. Goda K, Atkinson GM (2010) Intraevent spatial correlation of ground-motion parameters using SK-net data. Bull Seism Soc Am 100: 3055–3067. doi: 10.1785/0120100031 CrossRefGoogle Scholar
  29. Hok S, Wald DJ (2003) Spatial variability of peak strong ground motions: implications for ShakeMap interpolations. EOS Trans Am Geophys Union 84(46): F1121Google Scholar
  30. Hong HP, Zhang Y, Goda K (2009) Effect of spatial correlation on estimated ground motion-prediction equations. Bull Seism Soc Am 99: 928–934. doi: 10.1785/0120080172 CrossRefGoogle Scholar
  31. Jayaram N, Baker JW (2009) Correlation model for spatially-distributed ground-motion intensities. Earthq Eng Struct Dyn 38: 1687–1708. doi: 10.1002/eqe.922 CrossRefGoogle Scholar
  32. Johnson ME (1987) Multivariate statistical simulation. Wiley Series in Probability and Mathematical Statistics. Los Alamos National Laboratory, Los AlamosGoogle Scholar
  33. Joyner WB, Boore DM (1993) Methods for regression analysis of strong-motion data. Bull Seism Soc Am 83: 469–487Google Scholar
  34. Kawakami H, Mogi H (2003) Analyzing spatial intraevent variability of peak ground accelerations as a function of separation distance. Bull Seism Soc Am 93: 1079–1090. doi: 10.1785/0120020026 CrossRefGoogle Scholar
  35. Lee R, Kiremidjian AS, Stergiou E (2004) Uncertainty and correlation of network components losses for a spatially distributed system. In: Proceedings of 13th world conference on earthquake engineering, Vancouver, Canada, August 1–6; Paper 989Google Scholar
  36. Lee R, Kiremidjian AS (2007) Uncertainty and correlation for loss assessment of spatially distributed systems. Earthq Spectra 23: 753–770. doi: 10.1193/1.2791001 CrossRefGoogle Scholar
  37. Liao WI, Loh CH, Tsai KC (2006) Study of the fragility of building structures in Taiwan. Nat Hazards 37(1-2): 55–69. doi: 10.1007/s11069-005-4656-x CrossRefGoogle Scholar
  38. Liao WI, Loh CH (2004) Preliminary study of the fragility curves for highway bridges in Taiwan. J Chin Inst Eng 27(3): 367–375CrossRefGoogle Scholar
  39. Lin KW, Wald D, Worden B, Shakal AF (2006) Progress toward quantifying CISN ShakeMap uncertainty. In: Eighth national conference on earthquake engineering, San Francisco, California, April 18–21, 2006Google Scholar
  40. Lin PS, Lee CT (2008) Ground motion attenuation relationships for subduction-zone earthquakes in Northeastern Taiwan. Bull Seism Soc Am 98: 220–240. doi: 10.1785/0120060002 CrossRefGoogle Scholar
  41. Li C, Chiu HC (1989) A simple method to estimate the seismic moment from seismograms. Proc Geol Soc China 32: 197–207Google Scholar
  42. Loh CH, Yeh YT, Jean WY, Yeh YH (1991) Probabilistic seismic risk analysis in the Taiwan area based on PGA and spectral amplitude attenuation formulas. Eng Geol 30: 277–304CrossRefGoogle Scholar
  43. Loh CH, Jean WY (1997) Seismic zoning on ground motion in Taiwan area. In: Proceedings of 14th international conference on soil mechanics and foundation engineering, Germany, 6–12 September, 1997, pp 71–79Google Scholar
  44. Mander JB (1999) Fragility curve development for assessing the seismic vulnerability of highway bridges. University at Buffalo, State University of New York, MCEER Research Progress and Accomplishments, Research Summary 1997–1999. Available in http://mceer.buffalo.edu/publications/resaccom/99-sp01/chl0mand.pdf
  45. McVerry GH, Rhoades DA, Smith WD (2004) Joint hazard of earthquake shaking at multiple locations. In: Proceedings of 13th world conference on earthquake engineering, Vancouver, Canada, August 1–6, 2004. Paper 646Google Scholar
  46. Molas GL, Anderson R, Seneviratna P, Winkler T (2006) Uncertainty of portfolio loss estimates for large earthquakes. In: Proceedings of first European conference on earthquake engineering and seismology, Geneva, Switzerland, 3–8 September 2006: Paper 1117Google Scholar
  47. Molchan GM, Dmitrieva OE (1992) Aftershock identification: methods and new approaches. Geophys J Int 109: 501–516CrossRefGoogle Scholar
  48. Musson RMW (1999) Determination of design earthquakes in seismic hazard analysis through Monte Carlo simulation. J Earthq Eng 3(4): 463–474CrossRefGoogle Scholar
  49. Musson RMW (2000) The use of Monte Carlo simulations for seismic hazard assessment in the UK. Ann Geofis 43(1): 1–9Google Scholar
  50. Park J, Bazzurro P, Baker JW (2007) Modeling spatial correlation of ground motion intensity measures for regional seismic hazard and portfolio loss estimations. In: Kanda, Takada, Furuta (eds) Applications of statistics and probability in civil engineering. Taylor & Francis Group, London, pp 1–8Google Scholar
  51. Porter K, Beck JL, Shaikhitdinov RV (2002) Sensitivity of building loss estimates to major uncertain variables. Earthq Spectra 18: 719–743. doi: 10.1193/1.1516201 CrossRefGoogle Scholar
  52. Rhoades DA, McVerry GH (2001) Joint hazard of earthquake shaking at two or more locations. Earthq Spectra 17(4): 697–710. doi: 10.1193/1.1423903 CrossRefGoogle Scholar
  53. Robinson D, Dhu T, Schneider J (2006) Practical probabilistic seismic risk analysis: a demonstration of capability. Seismol Res Lett 77(4): 453–459. doi: 10.1785/gssrl.77.4.453 CrossRefGoogle Scholar
  54. Sokolov V, Loh CH, Wen KL (2001) Site-dependent input ground motion estimations for the Taipei area: a probabilistic approach. Probab Eng Mech 16(2): 177–191CrossRefGoogle Scholar
  55. Sokolov V, Wenzel F, Jean WY, Wen KL (2010) Uncertainty and spatial correlation of earthquake ground motion in Taiwan. Terrest Atmos Oceanic Sci (TAO) 21(6): 905–921. doi: 10.3319/TAO.2010.05.03.01(T) CrossRefGoogle Scholar
  56. Sokolov V, Wenzel F (2010) Influence of spatial correlation of strong ground-motion on uncertainty in earthquake loss estimation. Earthq Eng Struct Dyn (Online first). doi: 10.1002/eqe.1074
  57. Stergiou E, Kiremidjian AS (2006) Treatment of uncertainties in seismic risk analysis of transportation systems. The John A Blume Earthquake Engineering Center, Report 154Google Scholar
  58. Tsai CCP, Loh CH, Yeh YT (1987) Analysis of earthquake risk in Taiwan based on seismitectonic zones. Memoir Geol Soc China 9: 413–446Google Scholar
  59. Tsai CCP, Chen YH, Liu CH (2006) The path effect in ground-motion variability: an application of the variance-component technique. Bull Seism Soc Am 96: 1170–1176. doi: 10.1785/0120050155 CrossRefGoogle Scholar
  60. Wang JH, Liu CC, Tsai YB (1989) Local magnitude determined from a simulated Wood-Anderson seismograph. Tectonophysics 166: 15–26CrossRefGoogle Scholar
  61. Wang M, Takada T (2005) Macrospatial correlation model of seismic ground motions. Earthq Spectra 21(4): 1137–1156. doi: 10.1193/1.2083887 CrossRefGoogle Scholar
  62. Wesson RL, Perkins DM (2001) Spatial correlation of probabilistic earthquake ground motion and loss. Bull Seism Soc Am 91: 1498–1515. doi: 10.1785/0120000284 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Geophysical InstituteKarlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations