Machine learning‐based peak ground acceleration models for structural risk assessment using spatial data analysis

Predicting peak time‐domain ground‐motion parameters, such as peak ground acceleration (PGA), peak ground velocity, and peak ground displacement at a specific location, is challenging because of the limited number of recorded ground motions and the complexity of ground‐motion prediction equations. T...

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Veröffentlicht in:	Earthquake engineering & structural dynamics Jg. 53; H. 1; S. 152 - 178
Hauptverfasser:	Saleem, Nadia, Mangalathu, Sujith, Ahmed, Bilal, Jeon, Jong‐Su
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bognor Regis Wiley Subscription Services, Inc 01.01.2024
Schlagworte:	Acceleration Algorithms Analysis Bridges Complexity Data analysis Decision trees Earthquake damage Feature selection Geographic information systems Geographical information systems Ground motion hyperparameter tuning Information systems Learning algorithms Machine learning machine learning for spatial data Modelling Performance evaluation PGA classification Prediction models Rank tests Remote sensing Risk assessment seismic analysis Seismic hazard Seismic surveys Seismic velocities Spatial analysis Spatial data Spatial dependencies Velocity Vulnerability Wave velocity
ISSN:	0098-8847, 1096-9845
Online-Zugang:	Volltext
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