Faculty Publications

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Author: search.filters.author.Ramkrishnan, R.Subject: search.filters.subject.Equations of motion

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    Probabilistic seismic hazard analysis of North and Central Himalayas using regional ground motion prediction equations
    (Springer Science and Business Media Deutschland GmbH, 2021) Ramkrishnan, R.; Kolathayar, S.; Sitharam, T.G.
    Recently developed region-specific GMPEs are used for a comprehensive seismic hazard analysis (SHA) of the North and Central Himalayas (NCH) using a probabilistic approach considering two source models. Vulnerable seismic sources in the areas are identified based on the Seismotectonic Atlas (Dasgupta et al. 2000), published by the Geological Survey of India. An up to date, homogenized and declustered earthquake catalogue is compiled from various sources, with earthquake data since 250 BC, to create a new digitized seismotectonic representation of the region. Regional seismic zones having similar seismicity are recognized based on the Gutenberg-Richter (GR) parameters and the region is delineated into 5 seismic zones. The study area is divided into grids of size 0.05° × 0.05° and the hazard in terms of Peak Ground Acceleration (PGA) at the centre of each grid point is estimated and presented as hazard maps for individual seismic sources, maximum of all sources, and average of both sources. From the current study, it could be concluded that the PGA estimated in the regions is comparatively higher than what is reported in the codal provisions for seismic zonation and estimation of design horizontal acceleration for the region. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
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    Strong Motion Data Based Regional Ground Motion Prediction Equations for North East India Based on Non-Linear Regression Models
    (Taylor and Francis Ltd., 2022) Ramkrishnan, R.; Kolathayar, S.; Sitharam, T.G.
    Existing Ground Motion Prediction Equations (GMPE) in practice for North East India have been developed using limited or simulated datasets of recorded ground motions. The current study presents the development of a new GMPE based on a well-established model considering actual recorded ground motion data comprising of acceleration, magnitude, and hypocentral distances. A larger dataset with magnitudes ranging from 4.2 to 6.9 and up to 640 kms, with a total of 204 recordings is used in non-linear multiple-regression. The newly developed GMPE could predict ground acceleration realistically over larger ranges of distance and magnitudes, compared to existing GMPEs. © 2020 Taylor & Francis Group, LLC.