Ground Motion Selection for Performance-Based Engineering: Effect of Target Spectrum and Conditioning Period

  • Jack W. Baker
  • Ting Lin
  • Curt B. Haselton
Part of the Geotechnical, Geological and Earthquake Engineering book series (GGEE, volume 32)


This chapter presents a study of the impact of conditioning period on structural analysis results obtained from ground motions selected using the Conditional Spectrum concept. The Conditional Spectrum provides a quantitative means to model the distribution of response spectra associated with ground motions having a target spectral acceleration at a single conditioning period . One previously unresolved issue with this approach is how to condition this target spectrum for cases where the structure of interest is sensitive to excitation at multiple periods due to nonlinearity and multi-mode effects. To investigate the impact of conditioning period , we perform seismic hazard analysis, ground motion selection , and nonlinear dynamic structural analysis to develop a “risk-based” assessment of a 20-story concrete frame building. We perform this assessment using varying conditioning period s and find that the resulting structural reliabilities are comparable regardless of the conditioning period used for seismic hazard analysis and ground motion selection . This is true as long as a Conditional Spectrum (which carefully captures trends in means and variability of spectra) is used as the ground motion target, and as long as the analysis goal is a risk-based assessment that provides the annual rate of exceeding some structural limit state (as opposed to computing response conditioned on a specified ground motion intensity level). Theoretical arguments are provided to support these findings, and implications for performance-based earthquake engineering are discussed.


Ground motion selection Seismic risk assessment Nonlinear analysis 



This work was supported in part by the NEHRP Consultants Joint Venture (a partnership of the Consortium of Universities for Research in Earthquake Engineering and Applied Technology Council), under Contract SB134107CQ0019, Earthquake Structural and Engineering Research, issued by the National Institute of Standards and Technology, for project ATC-82. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the NEHRP Consultants Joint Venture. The authors also acknowledge the contributions of Jared DeBock and Fortunato Enriquez in conducting the structural analyses used in this study.


  1. Abrahamson NA, Yunatci AA (2010) Ground motion occurrence rates for scenario spectra. In: Fifth international conference on recent advances in geotechnical earthquake engineering and soil dynamics, San Diego, paper no. 3.18, 6pGoogle Scholar
  2. Applied Technology Council (2011) ATC-58, guidelines for seismic performance assessment of buildings, 75% draft. Applied Technology Council, Redwood City, 266pGoogle Scholar
  3. Baker JW (2011) Conditional mean spectrum: tool for ground motion selection. J Struct Eng 137(3):322–331CrossRefGoogle Scholar
  4. Baker JW, Cornell CA (2005a) A vector-valued ground motion intensity measure consisting of spectral acceleration and epsilon. Earthq Eng Struct Dyn 34(10):1193–1217CrossRefGoogle Scholar
  5. Baker JW, Cornell CA (2005b) Vector-valued ground motion intensity measures for probabilistic seismic demand analysis (Report no. 150). John A. Blume Earthquake Engineering Center, Stanford, 321pGoogle Scholar
  6. Baker JW, Cornell CA (2006) Spectral shape, epsilon and record selection. Earthq Eng Struct Dyn 35(9):1077–1095CrossRefGoogle Scholar
  7. Baker JW, Jayaram N (2008) Correlation of spectral acceleration values from NGA ground motion models. Earthq Spectra 24(1):299–317CrossRefGoogle Scholar
  8. Boore DM, Atkinson GM (2008) Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods between 0.01 s and 10.0 s. Earthq Spectra 24(1):99–138CrossRefGoogle Scholar
  9. Bradley BA (2010) A generalized conditional intensity measure approach and holistic ground-motion selection. Earthq Eng Struct Dyn 39(12):1321–1342Google Scholar
  10. Chiou B, Darragh R, Gregor N, Silva W (2008) NGA project strong-motion database. Earthq Spectra 24(1):23–44CrossRefGoogle Scholar
  11. Cornell CA, Krawinkler H (2000) Progress and challenges in seismic performance assessment. PEER Center News 3(2):1–3Google Scholar
  12. Federal Emergency Management Agency (2009) Quantification of building seismic performance factors (FEMA P695, ATC-63). FEMA P695, prepared by the Applied Technology Council, 421pGoogle Scholar
  13. Haselton C, Baker JW (2006) Ground motion intensity measures for collapse capacity prediction: choice of optimal spectral period and effect of spectral shape. In: Proceedings, 8th national conference on earthquake engineering, San Francisco, p 10Google Scholar
  14. Haselton CB, Deierlein GG (2007) Assessing seismic collapse safety of modern reinforced concrete moment frame buildings. Pacific Earthquake Engineering Research Center, BerkeleyGoogle Scholar
  15. ICC (2003) International building code 2003. International Code Council, ICC (distributed by Cengage Learning)Google Scholar
  16. Jayaram N, Lin T, Baker JW (2011) A computationally efficient ground-motion selection algorithm for matching a target response spectrum mean and variance. Earthq Spectra 27(3):797–815CrossRefGoogle Scholar
  17. Krawinkler H, Miranda E (2004) Performance-based earthquake engineering. In: Bozorgnia Y, Bertero VV (eds) Earthquake engineering: from engineering seismology to performance-based engineering. CRC Press, Boca RatonGoogle Scholar
  18. Lin T (2012) Advancement of hazard consistent ground motion selection methodology. PhD thesis, Dept. of Civil and Environmental Engineering, Stanford University, StanfordGoogle Scholar
  19. Lin T, Harmsen SC, Baker JW, Luco N (2013) Conditional spectrum computation incorporating multiple causal earthquakes and ground motion prediction models. Bull Seismol Soc Am 103(2A):1103–1116.Google Scholar
  20. Luco N, Cornell CA (2007) Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthq Spectra 23(2):357–392CrossRefGoogle Scholar
  21. NIST (2011) Selecting and scaling earthquake ground motions for performing response-history analyses. NIST GCR 11-917-15, Prepared by the NEHRP Consultants Joint Venture for the National Institute of Standards and Technology, GaithersburgGoogle Scholar
  22. OpenSEES (2011) Open system for earthquake engineering simulation. Pacific Earthquake Engineering Research Center, Accessed 20 Jun 2011
  23. Shome N, Cornell CA (1999) Probabilistic seismic demand analysis of nonlinear structures (Report no. RMS35). PhD thesis, RMS program, Stanford, p 320Google Scholar
  24. Shome N, Luco N (2010) Loss estimation of multi-mode dominated structures for a scenario of earthquake event. In: 9th US National and 10th Canadian conference on earthquake engineering, Toronto, p 10Google Scholar
  25. Shome N, Cornell CA, Bazzurro P, Carballo JE (1998) Earthquakes, records, and nonlinear responses. Earthq Spectra 14(3):469–500CrossRefGoogle Scholar
  26. USGS (2008) Interactive deaggregation tools. United States Geological Survey, Accessed 20 Jun 2011

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Civil and Environmental Engineering, John A. Blume Earthquake Engineering CenterStanford UniversityStanfordUSA
  2. 2.Department of Civil, Construction and Environmental EngineeringMarquette UniversityMilwaukeeUSA
  3. 3.Department of Civil EngineeringCalifornia State UniversityChicoUSA

Personalised recommendations