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Intra-Event Spatial Correlations for Cumulative Absolute Velocity, Arias Intensity, and Spectral Accelerations Based on Regional Site Conditions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Influence of spatial correlation of strong ground motion on uncertainty in earthquake loss estimation
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A predictive model for Arias intensity at multiple sites and consideration of spatial correlations
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A simple ground‐motion prediction model for cumulative absolute velocity and model validation
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Empirical attenuation relationship for Arias Intensity
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Correlation model for spatially distributed ground‐motion intensities
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Robust estimation of the variogram: I
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Influence of ground-motion correlation on probabilistic assessments of seismic hazard and loss: sensitivity analysis
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A criterion for determining exceedance of the operating basis earthquake
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Regression models for estimating coseismic landslide displacement
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Site classification and Vs30 estimation of free-field TSMIP stations using the logging data of EGDT
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Assessment of soil liquefaction incorporating earthquake characteristics
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Assessment of Liquefaction Potential during Earthquakes by Arias Intensity
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Multivariate Statistical Simulation
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Geostatistics for Natural Resources Evaluation
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Macrospatial Correlation Model of Seismic Ground Motions
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Ground Motion Intensity Measures for Liquefaction Hazard Evaluation
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Uncertainty and Correlation for Loss Assessment of Spatially Distributed Systems
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Ground‐Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%‐Damped PSA at Spectral Periods between 0.01 s and 10.0 s
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NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 s
inferredcites →https://doi.org/10.1193/1.2894832openalex-snapshot
An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra
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Summary of the Abrahamson & Silva NGA Ground‐Motion Relations
inferredcites →https://doi.org/10.1193/1.3457158openalex-snapshot
A Ground Motion Prediction Equation for the Horizontal Component of Cumulative Absolute Velocity (CAV) Based on the PEER‐NGA Strong Motion Database
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A Comparison of Ground Motion Prediction Equations for Arias Intensity and Cumulative Absolute Velocity Developed Using a Consistent Database and Functional Form
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Estimated Ground Motion From the 1994 Northridge, California, Earthquake at the Site of the Interstate 10 and La Cienega Boulevard Bridge Collapse, West Los Angeles, California
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Spatial Correlation of Peak Ground Motions and Response Spectra
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Considering Spatial Correlation in Mixed-Effects Regression and the Impact on Ground-Motion Models
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Intraevent Spatial Correlation of Ground-Motion Parameters Using SK-net Data
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Model Validations and Comparisons of the Next Generation Attenuation of Ground Motions (NGA-West) Project
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PGA and PGV Spatial Correlation Models Based on European Multievent Datasets
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An exploratory analysis of the Joyner-Boore attenuation data
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Further analysis of the Joyner-Boore attenuation data
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A stable algorithm for regression analyses using the random effects model
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Methods for regression analysis of strong-motion data
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Statistics for Spatial Data, Revised Edition.
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Uncertainty and Spatial Correlation of Earthquake Ground Motion in Taiwan
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Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities
inferred← citeshttps://doi.org/10.3390/ijgi7070244openalex-snapshot
Shaking Maps Based on Cumulative Absolute Velocity and Arias Intensity: The Cases of the Two Strongest Earthquakes of the 2016–2017 Central Italy Seismic Sequence
inferred← citeshttps://doi.org/10.4401/ag-8349openalex-snapshot
Spatial Correlation of Peak Ground Motions and Pseudo-Spectral Acceleration Based on the Sarpol-e-Zahab Mw 7.3, 2017 Earthquake Data
inferred← citeshttps://doi.org/10.71846/17-wcee-1358openalex-snapshot
INSIGHTS INTO SPATIALLY CORRELATED GROUND MOTION INTENSITY MEASURES USING ITALIAN EARTHQUAKES
inferred← citeshttps://doi.org/10.7717/peerj-cs.2416openalex-snapshot
Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq
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