Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme

Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme

Abstract Aftershocks can cause additional damage or even lead to the collapse of structures already weakened by a mainshock. Scarcity of in-situ recorded aftershock accelerograms heightens the need to develop synthetic aftershock ground motions. These synthesized motions are crucial for assessing th...

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Bibliographic Details
Main Authors: Xiaohui Yu, Meng Wang, Chaolie Ning, Kun Ji
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Mainshock-aftershock sequence
Automated machine learning(AutoML)
Artificial aftershock ground motions
Spectral accelerations
Peak ductility demands
Online Access:https://doi.org/10.1038/s41598-024-84668-7
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https://doi.org/10.1038/s41598-024-84668-7

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