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Pseudo-Spectral Damping Reduction Factors for the Himalayan Region Considering Recorded Ground-Motion Data

Ground-motion prediction equations that are used to predict acceleration values are generally developed for a 5% viscous damping ratio. Special structures and structures that use damping devices may have damping ratios other than the conventionally used ratio of 5%. Hence, for such structures, the i...

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Detalles Bibliográficos
Autores principales: P., Anbazhagan, Uday, Anjali, Moustafa, Sayed S. R., Al-Arifi, Nassir S. N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017774/
https://www.ncbi.nlm.nih.gov/pubmed/27611854
http://dx.doi.org/10.1371/journal.pone.0161137
Descripción
Sumario:Ground-motion prediction equations that are used to predict acceleration values are generally developed for a 5% viscous damping ratio. Special structures and structures that use damping devices may have damping ratios other than the conventionally used ratio of 5%. Hence, for such structures, the intensity measures predicted by conventional ground-motion prediction equations need to be converted to a particular level of damping using a damping reduction factor (DRF). DRF is the ratio of the spectral ordinate at 5% damping to the ordinate at a defined level of damping. In this study, the DRF has been defined using the spectral ordinate of pseudo-spectral acceleration and the effect of factors such as the duration of ground motion, magnitude, hypocenter distance, site classification, damping, and period are studied. In this study, an attempt has also been made to develop an empirical model for the DRF that is specifically applicable to the Himalayan region in terms of these predictor variables. A recorded earthquake with 410 horizontal motions was used, with data characterized by magnitudes ranging from 4 to 7.8 and hypocentral distances up to 520 km. The damping was varied from 0.5–30% and the period range considered was 0.02 to 10 s. The proposed model was compared and found to coincide well with models in the existing literature. The proposed model can be used to compute the DRF at any specific period, for any given value of predictor variables.