New conditional, scenario-based, and traditional peak ground velocity models for interface and intraslab subduction zone earthquakes

Author:

Liu Chenying1ORCID,Macedo Jorge1

Affiliation:

1. Georgia Institute of Technology, Atlanta, GA, USA

Abstract

The Pacific Earthquake Engineering Research (PEER) Center next-generation attenuation relationships for subduction zone earthquakes (NGA-Sub) ground motion database is used to develop new conditional ground motion models (CGMMs), several scenario-based ground motion models (GMMs), and a traditional GMM to estimate the peak ground velocity ( PGV) for subduction zone (interface, intraslab) earthquakes. The PGV estimate in the CGMMs is conditioned on the rupture distance ( Rrup), magnitude ( Mw), time-averaged shear wave velocity in the top 30 m ( Vs30) and pseudo-spectral acceleration PSA( TPGV). The period TPGVin the CGMMs is magnitude dependent to account for the magnitude dependence of the earthquake source corner frequency in the Fourier amplitude spectrum. Several scenario-based models are developed by combining the CGMM with PSA GMMs to directly estimate PGV given an earthquake scenario and site condition. Scenario-based models capture the complex ground motion effects in the underlying PSA GMMs and ensure the consistency with a design PSA spectrum, which is desired in engineering practice. In addition, a traditional PGV GMM is developed using Bayesian hierarchical regression. Finally, we compare all of these models and find that the scenario-based models are consistent with the traditional model developed in this study giving confidence to their use. The conditional and traditional PGV GMMs developed in this study benefit the performance-based design of engineering systems affected by subduction earthquakes when PGV is an important intensity measure.

Funder

georgia institute of technology

Publisher

SAGE Publications

Subject

Geophysics,Geotechnical Engineering and Engineering Geology

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1. Seismic Vulnerability of a Tailings Dam Affected by Subduction Earthquakes;International Journal of Civil Infrastructure;2024

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3. Machine learning-based models for estimating seismically-induced slope displacements in subduction earthquake zones;Soil Dynamics and Earthquake Engineering;2022-09

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