Copula-based joint probability function for PGA and CAV: a case study from Taiwan

Yun Xu, Xiao Song Tang, J. P. Wang, H. Kuo-Chen

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

This study aims to develop a joint probability function of peak ground acceleration (PGA) and cumulative absolute velocity (CAV) for the strong ground motion data from Taiwan. First, a total of 40,385 earthquake time histories are collected from the Taiwan Strong Motion Instrumentation Program. Then, the copula approach is introduced and applied to model the joint probability distribution of PGA and CAV. Finally, the correlation results using the PGA-CAV empirical data and the normalized residuals are compared. The results indicate that there exists a strong positive correlation between PGA and CAV. For both the PGA and CAV empirical data and the normalized residuals, the multivariate lognormal distribution composed of two lognormal marginal distributions and the Gaussian copula provides adequate characterization of the PGA-CAV joint distribution observed in Taiwan. This finding demonstrates the validity of the conventional two-step approach for developing empirical ground motion prediction equations (GMPEs) of multiple ground motion parameters from the copula viewpoint.

Original languageEnglish
Pages (from-to)2123-2136
Number of pages14
JournalEarthquake Engineering and Structural Dynamics
Volume45
Issue number13
DOIs
StatePublished - 25 Oct 2016

Keywords

  • CAV
  • copulas
  • correlation
  • joint probability function
  • PGA

Fingerprint

Dive into the research topics of 'Copula-based joint probability function for PGA and CAV: a case study from Taiwan'. Together they form a unique fingerprint.

Cite this