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A new approach to site classification

  • With increasing amount of strong motion data, Ground Motion Prediction Equation (GMPE) developers are able to quantify empirical site amplification functions (delta S2S(s)) from GMPE residuals, for use in site-specific Probabilistic Seismic Hazard Assessment. In this study, we first derive a GMPE for 5% damped Pseudo Spectral Acceleration (g) of Active Shallow Crustal earthquakes in Japan with 3.4 <= M-w <= 7.3 and 0 <= R-JB <= 600km. Using k-mean spectral clustering technique, we then classify our estimated delta S2S(s)(T = 0.01 - 2s) of 588 wellcharacterized sites, into 8 site clusters with distinct mean site amplification functions, and within-cluster site-tosite variability similar to 50% smaller than the overall dataset variability (phi(S2S)). Following an evaluation of existing schemes, we propose a revised data-driven site classification characterized by kernel density distributions of V-s30, V-s10, H-800, and predominant period (T-G) of the site clusters.

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Author details:Sreeram Reddy KothaORCiDGND, Fabrice Pierre CottonORCiDGND, Dino BindiORCiD
DOI:https://doi.org/10.1016/j.soildyn.2018.01.051
ISSN:0267-7261
ISSN:1879-341X
Title of parent work (English):Soil Dynamics and Earthquake Engineering
Subtitle (English):Mixed-effects Ground Motion Prediction Equation with spectral clustering of site amplification functions
Publisher:Elsevier
Place of publishing:Oxford
Publication type:Article
Language:English
Date of first publication:2018/02/17
Publication year:2018
Release date:2021/11/17
Tag:Empirical site amplification functions; Ground Motion Prediction Equation; Mixed-effects regression; Site classification; Spectral clustering analysis
Volume:110
Number of pages:12
First page:318
Last Page:329
Funding institution:SIGMA2 Project (EDF); SIGMA2 Project (CEA); SIGMA2 Project (PGE); SIGMA2 Project (SwissNuclear); SIGMA2 Project (Areva); SIGMA2 Project (CEZ); SIGMA2 Project (CRIEPI)
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften
DDC classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer review:Referiert
Publishing method:Open Access / Green Open-Access
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