This article investigates the correlation coefficients of ground-motion intensity measures for ground motions containing near-fault directivity velocity pulses. These correlation coefficients are necessary to quantify conditional multivariate intensity measure distributions and generate realizations from them for ground-motion selection. The empirical correlations between intensity measures representing ground-motion amplitude, frequency content, duration, and cumulative effects are calculated using the RotD50 definition and compared with published models. The impact of intensity measure definition as in RotD50, RotD100, and the geometric mean is also scrutinized. The sensitivity of the results to the considered ground motion set and the reference ground motion model are addressed in the computations. The results are compared with those from only non-directivity and mixed data sets based on the NGA-West2 database. The results indicate that the adopted data set has the largest influence on the variability of the empirically computed correlation coefficients. Given the multiple sources that contribute to uncertainty in these calculations, the authors conclude that existing models for predicting median correlation coefficients based on mixed data sets are sufficient for use with directivity, non-directivity, and mixed ground motions.
AbrahamsonNASilvaWJKamaiR (2014) Summary of the ASK14 Ground Motion Relation for Active Crustal Regions. Earthquake Spectra30: 1025–1055.
2.
AfshariKStewartJP (2016) Physically parameterized prediction equations for significant duration in active crustal regions. Earthquake Spectra32: 2057–2081.
3.
AkkarSBommerJJ (2007) Empirical prediction equations for Peak Ground Velocity derived from Strong-Motion Records from Europe and the Middle East. Bulletin of the Seismological Society of America97: 511–530.
4.
AkkarSSandıkkayaMAAyBÖ (2014) Compatible ground-motion prediction equations for damping scaling factors and vertical-to-horizontal spectral amplitude ratios for the broader Europe region. Bulletin of Earthquake Engineering12: 517–547.
5.
AngAHSTangWH (1975) Probability Concepts in Engineering Planning and Design. New York: John Wiley & Sons.
6.
ASCE 7-16 (2017) Minimum Design Loads and Associated Criteria for Buildings and Other Structures. Reston, VA: American Society of Civil Engineers.
7.
BakerJW (2007) Quantitative classification of near-fault ground motions using wavelet analysis. Bulletin of the Seismological Society of America97: 1486–1501.
8.
BakerJWBradleyBA (2017) Intensity measure correlations observed in the NGA-West2 database, and dependence of correlations on rupture and site parameters. Earthquake Spectra33: 145–156.
9.
BakerJWCornellAC (2005) A vector-valued ground motion intensity measure consisting of spectral acceleration and epsilon. Earthquake Engineering and Structural Dynamics34: 1193–1217.
10.
BakerJWCornellAC (2006a) Correlation of response spectral values for multicomponent ground motions. Bulletin of the Seismological Society of America96: 215–227.
11.
BakerJWCornellAC (2006b) Spectral shape, epsilon and record selection. Earthquake Engineering and Structural Dynamics35: 1077–1095.
12.
BakerJWJayaramN (2008) Correlation of spectral acceleration values from NGA ground motion models. Earthquake Spectra24: 299–317.
13.
BazzurroPCornellA (2002) Vector-valued probabilistic seismic hazard analysis. In: 7th U.S. national conference on earthquake engineering, Boston, MA, 21–25 July.
14.
BeyerKBommerJJ (2006) Relationships between median values and between aleatory variabilities for different definitions of the horizontal component of motion. Bulletin of the Seismological Society of America96: 1512–1522.
15.
BommerJJStaffordPJAlarcónJE (2009) Empirical equations for the prediction of the significant, bracketed, and uniform duration of earthquake ground motion. Bulletin of the Seismological Society of America99: 3217–3233.
16.
BooreDM (2010) Orientation-independent, nongeometric-mean measures of seismic intensity from two horizontal components of motion. Bulletin of the Seismological Society of America100: 1830–1835.
17.
BooreDMStewartJPSeyhanEAtkinsonGM (2014) NGA-West2 equations for predicting PGA, PGV, and 5% Damped PSA for shallow crustal earthquakes. Earthquake Spectra30: 1057–1085.
18.
BradleyBA (2010) Site-specific and spatially distributed ground-motion prediction of acceleration spectrum intensity. Bulletin of the Seismological Society of America100: 792–801.
19.
BradleyBA (2011a) Empirical equations for the prediction of displacement spectrum intensity and its correlation with other intensity measures. Soil Dynamics and Earthquake Engineering31: 1182–1191.
20.
BradleyBA (2011b) Correlation of significant duration with amplitude and cumulative intensity measures and its use in ground motion selection. Journal of Earthquake Engineering15: 809–832.
21.
BradleyBA (2012a) A ground motion selection algorithm based on the generalized conditional intensity measure approach. Soil Dynamics and Earthquake Engineering40: 48–61.
22.
BradleyBA (2012b) Empirical correlations between peak ground velocity and spectrum-based intensity measures. Earthquake Spectra28: 17–35.
23.
BradleyBA (2012c) Empirical correlations between cumulative absolute velocity and amplitude-based ground motion intensity measures. Earthquake Spectra28: 37–54.
24.
BradleyBACubrinovskiMMacRaeGADhakalRP (2009) Ground-motion prediction equation for SI based on spectral acceleration equations. Bulletin of the Seismological Society of America99: 277–285.
25.
CampbellKWBozorgniaY (2010) A ground motion prediction equation for the horizontal component of cumulative absolute velocity (CAV) based on the PEER-NGA strong motion database. Earthquake Spectra26: 635–650.
26.
CampbellKWBozorgniaY (2012) A comparison of ground motion prediction equations for Arias intensity and cumulative absolute velocity developed using a consistent database and functional form. Earthquake Spectra28: 931–941.
27.
CampbellKWBozorgniaY (2014) NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra. Earthquake Spectra30: 1087–1115.
28.
ChampionCLielA (2012) The effect of near-fault directivity on building seismic collapse risk. Earthquake Engineering and Structural Dynamics41: 1391–1409.
29.
ChioccarelliEIervolinoI (2010) Near-source seismic demand and pulse-like records: A discussion for L’Aquila earthquake. Earthquake Engineering and Structural Dynamics39: 1039–1062.
30.
ChiouBS-JYoungsRR (2014) Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra. Earthquake Spectra30: 1117–1153.
EfronBTibshiraniRJ (1993) An Introduction to the Bootstrap. New York: Chapman & Hall.
34.
Foulser-PiggottRStaffordPJ (2012) A predictive model for Arias intensity at multiple sites and consideration of spatial correlations. Earthquake Engineering and Structural Dynamics41: 431–451.
35.
GodaKAtkinsonGM (2009) Probabilistic characterization of spatially correlated response spectra for earthquakes in Japan. Bulletin of the Seismological Society of America99: 3003–3020.
36.
GülerceZAbrahamsonNA (2010) Vector-valued probabilistic seismic hazard assessment for the effects of vertical ground motions on the seismic response of highway bridges. Earthquake Spectra26: 999–1016.
37.
InoueTCornellA (1990) Seismic Hazard Analysis of Multi-Degree-of-Freedom Structures, Reliability of Marine Structures, RMS-8. Stanford, CA: Stanford University.
38.
JayaramNBakerJW (2010) Considering spatial correlation in mixed-effects regression and the impact on ground-motion models. Bulletin of the Seismological Society of America100: 3295–3303.
39.
KohrangiMPapadopoulosANBazzurroPVamvatsikosD (2020) Correlation of spectral acceleration values of vertical and horizontal ground motion pairs. Earthquake Spectra36: 2112–2128.
40.
KothaSRBindiDCottonF (2017) Site-corrected magnitude- and region-dependent correlations of horizontal peak spectral amplitudes. Earthquake Spectra33: 1415–1432.
41.
KulkarniRBYoungsRRCoppersmithKJ (1984) Assessment of confidence intervals for results of seismic hazard analysis. In: Proceedings of the eighth world conference on earthquake engineering, Vol. 1, pp. 263–270. Tokyo, Japan: International Association for Earthquake Engineering.
42.
LucoNCornellCA (2007) Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthquake Spectra23: 357–392.
43.
NEHRP (2003) NEHRP Recommended Provisions for Seismic Regulations for New Buildings and Other Structures, Part1: Provisions, FEMA 450. Washington, DC: Federal Emergency Management Agency.
44.
ShahiSKBakerJW (2014a) An efficient algorithm to identify strong-velocity pulses in multicomponent ground motions. Bulletin of the Seismological Society of America104: 2456–2466.
StewartJPDouglasJJavanbargMBozorgniaYAbrahamsonNABooreDMCampbellKWDelavaudEErdikMStaffordPJ (2015) Selection of ground motion prediction equations for the Global Earthquake Model. Earthquake Spectra31: 19–45.
47.
TarbaliKBradleyBA (2015) Ground motion selection for scenario ruptures using the generalized conditional intensity measure (GCIM) method. Earthquake Engineering and Structural Dynamics44: 1601–1621.
48.
TarbaliKBradleyBABakerJW (2018) Consideration and propagation of ground motion selection epistemic uncertainties to seismic performance metrics. Earthquake Spectra34(2): 587–610.
49.
TarbaliKBradleyBABakerJW (2019) Ground motion selection in the near-fault region considering directivity-induced pulse effects. Earthquake Spectra35(2): 759–786.
50.
TravasarouTBrayJDAbrahamsonNA (2003) Empirical attenuation relationship for Arias intensity. Earthquake Engineering and Structural Dynamics32: 1133–1155.
51.
WangG (2011) A ground motion selection and modification method capturing response spectrum characteristics and variability of scenario earthquakes. Soil Dynamics and Earthquake Engineering31: 611–625.
52.
WangGDuW (2012) Empirical correlations between cumulative absolute velocity and spectral accelerations from NGA ground motion database. Soil Dynamics and Earthquake Engineering43: 229–236.
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