Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sarit Chanda"'
Publikováno v:
Structures. 34:4977-4985
Estimating ground motion characteristics at various locations as a function of fault characteristics is useful for the proper damage assessment and risk mitigation strategies. This paper explores the application of machine learning approaches to pred
Publikováno v:
Journal of Seismology. 25:1339-1346
A multi-layer perceptron (MLP) technique is used to train on the response spectra for various strike angles, dip angles, and rake angles. Fixing the magnitude and depth of the earthquakes, the 3-component ground motion is simulated with the help of S
Publikováno v:
Journal of Bridge Engineering. 27
Autor:
Surendra Nadh Somala, Sarit Chanda
Publikováno v:
Pure and Applied Geophysics. 178:1959-1976
Traditional approaches require a velocity model to compute travel times for estimating the location of earthquakes. Moreover, the velocity model typically assumed is layered in nature, ignoring the perturbations around the background velocity model.
Publikováno v:
Izvestiya, Atmospheric and Oceanic Physics. 56:1315-1325
The region of Garhwal is one of the most seismically active areas in India. Many destructive earthquakes have occurred there in the past. The current seismic activity in Garhwal is also high. The region is characterized by a high population density;
Autor:
Surendra Nadh Somala, Sarit Chanda
Publikováno v:
Lecture Notes in Civil Engineering ISBN: 9783030803117
Lecture Notes in Civil Engineering
Lecture Notes in Civil Engineering
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75c472b12e2703cc542c6cec4e78bbad
https://doi.org/10.1007/978-3-030-80312-4_82
https://doi.org/10.1007/978-3-030-80312-4_82
Autor:
K. S. K. Karthik Reddy, M. C. Raghucharan, Surendra Nadh Somala, Sarit Chanda, Vasudeo Chaudhari
Publikováno v:
Journal of South American Earth Sciences. 109:103253
Chile is rocked by inslab, interface as well as crustal events. Duration estimates based on Chilean strong motion flatfile is used to predict total duration as well as significant-duration. We use six different machine learning algorithms k-nearest n