A realistic assessment of building economic losses and collapse induced by earthquakes requires the monitoring of several response measures, both story-specific and global. The prediction of such response measures benefits from using multiple ground motion intensity measures (IMs) that are, in general, correlated. To allow the inclusion of multiple IMs in the risk assessment process, it is necessary to have a practical tool that computes the vector-valued hazard of all such IMs at the building site. In this paper, vector-valued probabilistic seismic hazard analysis (VPSHA) is implemented here as a post-processor to scalar PSHA results. A group of candidate scalar and vector IMs based on spectral acceleration values, ratios of spectral acceleration values, and spectral accelerations averaged over a period range are defined and their hazard evaluated. These IMs are used as structural response predictors of three-dimensional (3-D) models of reinforced concrete buildings described in a companion paper (Kohrangi et al. 2016).
Get full access to this article
View all access options for this article.
References
1.
AkkarS.SandikkayaM. A. and AyB. Ö.2014. Compatible ground-motion prediction equations for damping scaling factors and vertical-to-horizontal spectral amplitude ratios for the broader European region, Bulletin of Earthquake Engineering12, 517–547.
2.
AzarbakhtA.MousaviM.NourizadehM. and ShahriM.2014. Dependence of correlations between spectral accelerations at multiple periods on magnitude and distance, Earthquake Engineering and Structural Dynamics43, 1193–1204.
3.
BakerJ. W. and CornellC. A.2008. Vector-valued intensity measures for pulse-like near-fault ground motions, Engineering Structures30, 1048–1057.
4.
BakerJ. W. and CornellC. A.2006. Which spectral acceleration are you using?, Earthquake Spectra22, 293–312.
5.
BakerJ. W. and JayaramN.2008. Correlation of spectral acceleration values from NGA ground motion models, Earthquake Spectra24, 299–317.
6.
BazzurroP.1998. Probabilistic Seismic Demand Analysis, Ph.D. Dissertation, Department of Civil and Environmental Engineering, Stanford University.
7.
BazzurroP. and CornellC. A.1999. Disaggregation of seismic hazard, Bulletin of Seismological Society of America89, 501–529.
8.
BazzurroP. and CornellC. A.2001. Vector-valued probabilistic seismic hazard analysis, Seismological Research Letters72, 273.
9.
BazzurroP. and CornellC. A.2002. Vector-valued probabilistic seismic hazard analysis (VPSHA), in Proceedings of 7th U.S. NCEE, Boston, MA.
BazzurroP.TothongP. and ParkJ.2009. Efficient approach to vector-valued probabilistic seismic hazard analysis of multiple correlated ground motion parameters, in 10th International Conference on Structural Safety and Reliability (ICOSSAR09), Osaka, Japan.
12.
BianchiniM.DiotalleviP. and BakerJ. W.2010. Prediction of inelastic structural response using an average of spectral accelerations, in 10th International Conference on Structural Safety and Reliability (ICOSSAR09), Osaka, Japan.
13.
BojórquezE. and IervolinoI.2011. Spectral shape proxies and nonlinear structural response, Soil Dynamics and Earthquake Engineering31, 996–1008.
14.
BooreD. M. and AtkinsonG. M.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, Earthquake Spectra24, 99–138.
15.
BradleyB.2011a. Correlation of significant duration with amplitude and cumulative intensity measures and its use in ground motion selection, Journal of Earthquake Engineering15, 809–832.
16.
BradleyB.2011b. Empirical correlation of PGA, spectral accelerations and spectrum intensities from active shallow crustal earthquakes, Earthquake Engineering & Structural Dynamics40, 1707–1721.
17.
BradleyB.2012a. Empirical correlations between peak ground velocity and spectrum-based intensity measures, Earthquake Spectra28, 37–54.
18.
BradleyB.2012b. Empirical correlations between peak ground velocity and spectrum-based intensity measures, Earthquake Spectra28, 17–35.
19.
CordovaP. P.DeierleinG. G.MehannyS. S. and CornellC. A.2000. Development of a two-parameter seismic intensity measure and probabilistic assessment procedure, Paper read at The Second US-Japan Workshop on Performance-Based Earthquake Engineering Methodology for Reinforced Concrete Building Structures, 13 September 1999, Maui, HI.
20.
CornellC. A. and KrawinklerH.2000. Progress and challenges in seismic performance assessment, PEER Center News, Pacific Earthquake Engineering Research Center, University of California, Berkeley.
21.
FajfarP.VidicT. and FishingerM.1990. A measure of earthquake motion capacity to damage medium-period structures, Soil Dynamics and Earthquake Engineering9, 236–242.
22.
FEMA-P-58, 2012. Federal Emergency Management Agency: Seismic Performance Assessment of Buildings, prepared by the Applied Technology Council for the Federal Emergency Management Agency (edited by FEMA), Washington, D.C.
23.
GenzA. and BertzF.1999. Numerical computation of Multivariate t probabilities with application to power calculation of multiple contrasts, J. Statist. Comput. Simul63, 361–378.
24.
GenzA. and BertzF.2012. Comparison of methods for computation of multivariate t probabilities, J. Comp. Graph. Stat11, 950–971.
25.
GiardiniD.WössnerJ. and DanciuL.2013. Seismic Hazard Harmonization in Europe (SHARE): Online data resource, Swiss Seismol. Serv., ETH Zurich, Zurich, Switzerland, SHARE, available at http://www.efehr.org:8080/jetspeed/.
26.
GodaK. and HongH. P.2008. Spatial correlation of peak ground motions and response spectra, Bulletin of the Seismological Society of America98, 354–365.
27.
GülerceZ. and AbrahamsonN. A.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.
28.
InoueT.1990. Seismic Hazard Analysis of Multi-Degree-of-Freedom Structures, Dept. of Civil and Environmental Engineering, Stanford University.
29.
JayaramN. and BakerJ. W.2008. Statistical tests of the joint distribution of spectral acceleration values, Bulletin of the Seismological Society of America98, 2231–2243.
30.
KazantziA. K. and VamvatsikosD.2015. Intensity measure selection for vulnerability studies of building classes, Earthquake Engineering and Structural Dynamics44, 2677–2694.
KohrangiM.BazzurroP. and VamvatsikosD.2016. Vector and scalar IMs in structural response estimation, Part II: Building demand assessment, Earthquake Spectra32, 1525–1543.
33.
KutnerM. H.NachtsheimC. J. and NeterJ.2004. Applied Linear Regression Models, 4th Edition, McGraw-Hill, New York.
34.
LucoN. and CornellC. A.2007. Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions, Earthquake Spectra23, 357–392.
35.
MonelliD.PaganiM.WeatherillG.SilvaV. and CrowleyH.2012. The hazard component of OpenQuake: The calculation engine of the Global Earthquake Model, in Proceedings of the 15th World Conference on Earthquake Engineering. Lisbon, Portugal.
36.
TothongP. and LucoN.2007. Probabilistic seismic demand analysis using advanced ground motion intensity measures, Earthquake Engineering & Structural Dynamics36, 1837–1860.
37.
VamvatsikosD. and CornellC. A.2005. Developing efficient scalar and vector intensity measures for IDA capacity estimation by incorporating elastic spectral shape information, Earthquake Engineering & Structural Dynamics34, 1573–1600.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
