Earthquake Spectra

Papers
(The H4-Index of Earthquake Spectra is 23. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
Self-centering seismic-resistant structures: Historical overview and state-of-the-art71
PEER NGA-East database63
NGA-subduction global ground motion models with regional adjustment factors58
EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow57
NGA-East Ground-Motion Characterization model part I: Summary of products and model development50
ShakeMap operations, policies, and procedures46
NGA-Subduction research program43
An open-source site database of strong-motion stations in Japan: K-NET and KiK-net (v1.0.0)41
A framework for operationalizing the assessment of post-earthquake functional recovery of buildings36
An analytical framework to assess earthquake-induced downtime and model recovery of buildings36
Application of discrete wavelet transform in seismic nonlinear analysis of soil–structure interaction problems34
EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers, part II: Regional simulations of building response31
Rapid earthquake loss assessment based on machine learning and representative sampling30
CPT-based liquefaction case histories compiled from three earthquakes in Canterbury, New Zealand29
How well can we predict earthquake site response so far? Site-specific approaches27
Summary of the Abrahamson and Gulerce NGA-SUB ground-motion model for subduction earthquakes26
Ground motion prediction equations for significant duration using the KiK-net database26
The quest for resilience: The Chilean practice of seismic design for reinforced concrete buildings26
Data-driven seismic response prediction of structural components25
Response spectral matching of horizontal ground motion components to an orientation-independent spectrum (RotDnn)24
Capturing epistemic uncertainty in site response24
Analytical fragility relation for buried cast iron pipelines with lead-caulked joints based on machine learning algorithms24
Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake23
Toward functional recovery performance in the seismic design of modern tall buildings23
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