Some Discussions on Data-Driven Testing of Ground-Motion Prediction Equations under the Turkish Ground-Motion Database

Kale Ö.

JOURNAL OF EARTHQUAKE ENGINEERING, vol.23, no.1, pp.160-181, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23 Issue: 1
  • Publication Date: 2019
  • Doi Number: 10.1080/13632469.2017.1323047
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.160-181
  • Keywords: Ground-Motion Prediction Equations, Residual Analysis, Seismic Hazard Assessment, Likelihood Methods, Euclidean Distance, SEISMIC-HAZARD ANALYSIS, MODEL SELECTION, RANKING, EUROPE, TURKEY, GMPES, PGV, MAGNITUDE, REGIONS, UPDATE
  • TED University Affiliated: Yes


Data-driven testing of ground-motion prediction equations (GMPEs) can help researchers in the ranking and selection of GMPEs for seismic hazard assessment. This paper presents and discusses the details of the major features of well-known testing methods which employ likelihood and Euclidean distance concepts. A set of local, regional, and global GMPEs are assessed under a ground-motion database. The data-driven testing of GMPEs is supported by thorough residual analyses under the database, providing valuable insights into the testing of GMPEs. The researchers are advised to evaluate all options together instead of focusing on a specific tool to build well-structured ground-motion logic trees.