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Identification of Far-Field Long-Period Ground Motions Using Phase Derivatives


Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2019
Page(s): 1-20
DOI: 10.1155/2019/1065830

The characteristics of long-period ground motions are of significant concern to engineering communities largely due to resonance-induced responses of long-period structures to far-field long-period ground motions which are excited by the existence of distant sedimentary basins. Classifications of records enable applications of far-field long-period ground motions in seismology and engineering practices, such as attenuation models and dynamic analysis of structures. Accordingly, the study herein aims to develop an approach for identifying the far-field long-period ground motions in terms of the later-arriving long-period surface waves. Envelope delays derived from phase derivatives are employed to determine the later-arriving long-period components on the basis of phase dispersion. A quantitative calibration for long-period properties is defined in terms of the ratio of energy from later-arriving long-period components to the total energy of a ground motion. In order to increase the accuracy of candidate far-field long-period records caused by sediments, recording stations within basins or plains are collected from the K-NET and KiK-net strong-motion networks. Subsequently, the motions are manually classified into two categories in order to form a training dataset by visual examinations on their velocity waveform. The two predictive variables, including the corner frequency obtained from envelope delays and the corresponding energy ratio, are used for the establishment of the classification criterion. Furthermore, the analysis of classification results provides insight into the causes for discrepancy and verifies the effectiveness of the proposed method. Finally, comparisons of the mean normalized acceleration response spectrum with respect to the predictors, as well as the local site effects, are performed.

Copyright: © 2019 Minghui Dai et al.

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

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