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Kumar M.,National Institute of Rock Mechanics | Bhatt M.R.,Vellore Institute of Technology | Samui P.,Vellore Institute of Technology
International Journal of Geomechanics | Year: 2014

The elastic modulus (Ej) of a jointed rock mass is an important parameter for rock mechanics. This study examines the capability of Gaussian process regression (GPR) for determination of the Ej of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3), and elastic modulus (Ei) of intact rock are considered as inputs of the GPR. The output of the GPR is the Ej of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted Ej of jointed rock masses is obtained from the GPR. The results show that the developed GPR is a promising tool for the prediction of the Ej of jointed rock masses. © 2014 American Society of Civil Engineers. Source


Rajendran C.P.,Jawaharlal Nehru Centre for Advanced Scientific Research | John B.,National Institute of Rock Mechanics | Rajendran K.,Indian Institute of Science
Journal of Geophysical Research B: Solid Earth | Year: 2015

The Himalaya has experienced three great earthquakes during the last century - 1934 Nepal-Bihar, 1950 Upper Assam, and arguably the 1905 Kangra. Focus here is on the central Himalayan segment between the 1905 and the 1934 ruptures, where previous studies have identified a great earthquake between thirteenth and sixteenth centuries. Historical data suggest damaging earthquakes in A.D. 1255, 1344, 1505, 1803, and 1833, although their sources and magnitudes remain debated. We present new evidence for a great earthquake from a trench across the base of a 13 m high scarp near Ramnagar at the Himalayan Frontal Thrust. The section exposed four south verging fault strands and a backthrust offsetting a broad spectrum of lithounits, including colluvial deposits. Age data suggest that the last great earthquake in the central Himalaya most likely occurred between A.D. 1259 and 1433. While evidence for this rupture is unmistakable, the stratigraphic clues imply an earlier event, which can most tentatively be placed between A.D. 1050 and 1250. The postulated existence of this earlier event, however, requires further validation. If the two-earthquake scenario is realistic, then the successive ruptures may have occurred in close intervals and were sourced on adjacent segments that overlapped at the trench site. Rupture(s) identified in the trench closely correlate with two damaging earthquakes of 1255 and 1344 reported from Nepal. The present study suggests that the frontal thrust in central Himalaya may have remained seismically inactive during the last ~700 years. Considering this long elapsed time, a great earthquake may be due in the region. ©2015. American Geophysical Union. All Rights Reserved. Source


Rajendran C.P.,Jawaharlal Nehru Centre for Advanced Scientific Research | John B.,National Institute of Rock Mechanics | Rajendran K.,Indian Institute of Science | Sanwal J.,Jawaharlal Nehru Centre for Advanced Scientific Research
Journal of Seismology | Year: 2016

The great 1934 Himalayan earthquake of moment magnitude (Mw) 8.1 generated a large zone of ground failure and liquefaction in north Bihar, India, in addition to the earthquakes of 1833 (Mw ~7.7) and 1988 (Mw 6.7) that have also impacted this region. Here, we present the results of paleoliquefaction investigations from four sites in the plains of north Bihar and one in eastern Uttar Pradesh. The liquefaction features generated by successive earthquakes were dated at AD 829–971, 886–1090, 907–1181, 1130–1376, 1112–1572, 1492–1672, 1733–1839, and 1814–1854. One of the liquefaction events dated at AD 829–971, 886–1090, and 907–1181 may correlate with the great earthquake of AD ~1100, recognized in an earlier study from the sections across the frontal thrust in central eastern Nepal. Two late medieval liquefaction episodes of AD 1130–1376 and 1492–1672 were also exposed in our sites. The sedimentary sections also revealed sandblows that can be attributed to the 1833 earthquake, a lesser magnitude event compared to the 1934. Liquefactions triggered by the 1934 and 1988 earthquakes were evident within the topmost level in some sections. The available data lead us to conjecture that a series of temporally close spaced earthquakes of both strong and large types, not including the infrequent great earthquakes like the 1934, have affected the Bihar Plains during the last 1500 years with a combined recurrence interval of 124 ± 63 years. © 2016 Springer Science+Business Media Dordrecht Source


Bansal A.R.,CSIR - Central Electrochemical Research Institute | Dimri V.P.,CSIR - Central Electrochemical Research Institute | Babu K.K.,National Institute of Rock Mechanics
Journal of Seismology | Year: 2013

We analyzed the seismicity of northeastern Himalayan region of latitude (25 to 32° N) and longitude (86-97° E). The US Geological Survey catalogue is used in this study for a period from 1973 to June 2011. The seismicity of the region is modeled using epidemic type aftershock sequence (ETAS) model. The region is divided in three parts: (1) whole region, (2) subregion I, and (3) subregion II. The magnitude of completeness is found to be 4. 6 for all the three regions. The ETAS parameters for all the regions are found same within the standard errors. There is no significant change observed in the seismicity since 1973 based on the ETAS modeling. © 2012 Springer Science+Business Media B.V. Source


Kumar M.,National Institute of Rock Mechanics | Samui P.,Vellore Institute of Technology | Naithani A.K.,Indian National Institute of Engineering
International Journal of Advances in Soft Computing and its Applications | Year: 2013

This article adopts machine learning techniques Relevance Vector Machine (RVM), Gaussian Process Regression (GPR) and Minimax Probability Machine Regression (MPMR)} for determination of Uniaxial Compressive Strength (UCS) and the Modulus of Elasticity (E) of Travertine samples. Point load index (Is(50)), porosity (n), P-wave velocity (Vp), and Schmidt hammer rebound number (Rn) have been taken as inputs of the RVM, GPR and MPMR model. The outputs of RVM, MPMR and GPR are UCS and E. The developed RVM gives equations for prediction UCS and E. The performance of GPR, MPMR and RVM has been compared with the Artificial Neural Network (ANN) models. The simulation results show that the proposed methods give encouraging performance for prediction of UCS and E of Travertine samples. Source

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