A new approach to site classification: Mixed-effects Ground Motion Prediction Equation with spectral clustering of site amplification functions. Issue 110 (July 2018)

• Record Type: Journal Article
• Authors: Kotha, Sreeram Reddy; Cotton, Fabrice; Bindi, Dino
• Is Part Of: Soil dynamics and earthquake engineering. Issue 110(2018)
• Page Start: 318
• Page End: 329
• Publication Date: 2018-07
• Subjects: Mixed-effects regression -- Ground Motion Prediction Equation -- Site classification -- Spectral clustering analysis -- Empirical site amplification functions; Soil dynamics -- Periodicals; Earthquake engineering -- Periodicals; Sols -- Dynamique -- Périodiques; Génie parasismique -- Périodiques; 624.176205
• DOI: 10.1016/j.soildyn.2018.01.051
• Languages: English
• ISSNs: 0267-7261

Abstract

Abstract: With increasing amount of strong motion data, Ground Motion Prediction Equation (GMPE) developers are able to quantify empirical site amplification functions ( Δ S 2 S 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 < 600 km . Using k-mean spectral clustering technique, we then classify our estimated Δ S 2 S s ( T = 0.01 – 2 s ) of 588 well-characterized sites, into 8 site clusters with distinct mean site amplification functions, and within-cluster site-to-site variability ~ 50% smaller than the overall dataset variability (ϕS2S ). Following an evaluation of existing schemes, we propose a revised data-driven site classification characterized by kernel density distributions of Vs30, Vs10, H800, and predominant period (TG ) of the site clusters Highlights: Ground Motion Prediction Equation for Active Shallow Crustal earthquakes in Japan. GMPE site-specific random-effects serve as empirical site-response functions. Spectral clustering identifies site classes with distinct response to seismic action. Vs30 alone is incapable of distinguishing high frequency response of site classes. Combination of Vs10, Vs30 and H800 ranges efficiently characterize 8 site classes.