An Updated Shear Wave Velocity-Based Seismic Soil Liquefaction Triggering Database


ILGAÇ M., Kayen R. E. , Wood C., ÇETİN K. Ö.

GeoCongress on State of the Art and Practice in Geotechnical Engineering, Charlottetown, Canada, 20 - 23 March 2022, vol.334, pp.318-328 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 334
  • Doi Number: 10.1061/9780784484043.031
  • City: Charlottetown
  • Country: Canada
  • Page Numbers: pp.318-328
  • Middle East Technical University Affiliated: Yes

Abstract

The small-strain shear wave velocity (V-s) is a means to assess the triggering of seismic soil liquefaction since the early 1990s, and the size and quality of these data sets have grown enormously in the past decades. Based on these data, researchers from 1991 to the present have developed V-s-based seismic soil liquefaction triggering relationships. The authors revisited the V-s-based database to update the case histories with the current state of knowledge and include new case histories (e. g., 2011 Tohoku earthquake, 2010-2011 New Zealand-Canterbury earthquakes, etc.). This paper presents the updated Vs-based database, along with the details and statistics of the case history database. The updated database consists of (1) 537 case histories, (2) new earthquake events, (3) multiple inversion method, (4) assessment of nearby standard penetration test (SPT) and cone penetration test (CPT) data, (5) dispersion curves, (6) multiple shear wave velocity profiles, (7) digitized Vs profiles of the literature case histories, (8) implementation of the improved selection of unit weight, (9) new parameters related to site information (e.g., V-s30m, etc.), (10) standard protocol to process data in an unbiased manner, (11) uncertainties of each input parameter, and (12) and geological site classification. Every input parameter was revisited, reanalyzed, and updated. The new database is organized to develop new probabilistic shear wave velocity-based seismic soil liquefaction triggering relationships using Bayesian analysis and system reliability methods.