Dependence of correlations between spectral accelerations at multiple periods on magnitude and distance

In this paper the dependence of correlations between spectral accelerations at multiple periods on Magnitude (M) and Distance (R) has been investigated. For this purpose, a relatively large dataset of ground motion records (GMRs), containing 1551 records with a wide range of seismic characteristics, was selected. It is shown that the difference in the correlation coefficient is statistically meaningful when the general GMR dataset is divided into two subsets based on an arbitrary M or R. The observed difference is more meaningful in the case of magnitude when compared with distance. The general dataset of GMRs was then divided into four separate subsets based on optimum values of M and R, so that the four obtained subsets were given the greatest dissimilarity in terms of the correlation coefficients. The correlation coefficients between spectral accelerations at multiple periods were calculated in the case of the four subsets, and compared with the available correlations in the literature. The conditional mean spectrum was also calculated by means of the conventional correlation coefficients, as well as by using the proposed M and R dependent correlation coefficients. The results show that, despite the commonly available findings in the literature, this dependence is significant and should not be neglected in the conditional spectra calculation process.

[1]  J. Baker,et al.  Spectral shape, epsilon and record selection , 2006 .

[2]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[3]  C. Cornell,et al.  Correlation of Response Spectral Values for Multicomponent Ground Motions , 2006 .

[4]  R. Mcguire.  Probabilistic seismic hazard analysis and design earthquakes: Closing the loop , 1995, Bulletin of the Seismological Society of America.

[5]  J. Baker,et al.  Correlation of Spectral Acceleration Values from NGA Ground Motion Models , 2008 .

[6]  N. A. Abrahamson,et al.  A stable algorithm for regression analyses using the random effects model , 1992, Bulletin of the Seismological Society of America.

[7]  Farzad Naeim,et al.  On the Use of Design Spectrum Compatible Time Histories , 1995 .

[8]  Khalid M. Mosalam,et al.  PACIFIC EARTHQUAKE ENGINEERING RESEARCH CENTER , 2009 .

[9]  K. Campbell,et al.  NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 s , 2008 .

[10]  Ian G. Main,et al.  Earthquake Hazard Analysis: Issues and insights , 1992 .

[11]  N. Abrahamson,et al.  Orientation-Independent Measures of Ground Motion , 2006 .

[12]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[13]  N. Null.  Minimum Design Loads for Buildings and Other Structures , 2003 .

[14]  J. Baker,et al.  Statistical Tests of the Joint Distribution of Spectral Acceleration Values , 2008 .

[15]  Jack W. Baker,et al.  Conditional Mean Spectrum: Tool for Ground-Motion Selection , 2011 .

[16]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[17]  C. Cornell,et al.  Disaggregation of seismic hazard , 1999 .

[18]  J. Baker,et al.  Correlation of response spectral values in Japanese ground motions , 2011 .

[19]  Jack W. Baker,et al.  Vector-valued ground motion intensity measures for probabilistic seismic demand analysis , 2005 .

[20]  R. Mcguire.  Seismic Hazard and Risk Analysis , 2004 .

[21]  Julian J. Bommer,et al.  Hazard-consistent earthquake scenarios , 2000 .