Browsing by Author "V.H.R. Pandey"

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Assessment of Karmi Landslide Zone, Bageshwar, Uttarakhand, India
(Springer, 2020) V.N. Tiwari; V.H.R. Pandey; Ashutosh Kainthola; P.K. Singh; K.H. Singh; T.N. Singh
Slope instability is a big challenge for the population in mountainous regions. It poses a threat to life, economy, and infrastructure. For the safety of people, various prevention and precautions are taken and hence many scientific studies are going on. In the present study, the stability of the Karmi landslides zone, Bageshwar, Uttarakhand, India is assessed. Karmi village lies quite close to the northern border of India and the excavated roads are the only means of commute. The area lies in a tectonically active lesser Himalayan zone with high relief. Slope geometry was extracted using a total station, and seven different slope geometries were plotted. Soil and rock mass samples were taken and evaluated from various field and laboratory investigations. The direct shear test was performed to assess the friction angle and cohesion of the soil and rock mass. Numerical simulations viz., finite element analysis and probabilistic analysis have been applied on all seven slope sections and found that the mean and median safety factor of all the modeled slopes was 0.78 and 0.81, respectively. The study ascertains that the whole area to be quite vulnerable to failure, especially during rains, since the pore pressure build-up diminishes the shear strength of the slope forming material. Possible mitigation measures have been suggested based on the examined instability of the hill slopes. © 2020, Geological Society of India.
Empirical and Numerical Evaluation of a Cut Slope Near Rishikesh, India
(Springer Science and Business Media Deutschland GmbH, 2022) V.H.R. Pandey; Ashutosh Kainthola; T.N. Singh
The stability analysis of cut slopes along any transportation corridor is necessary to safeguard people’s and societal interests. The present work presents assessment of a steep rock cut slope near Rishikesh, along a national highway in Uttarakhand, India. The work details empirical and numerical examination of the slope stretching approximately 20 m in length along the road. The field investigation has been undertaken to ascertain discontinuities conditions, their orientations, spacing between them, geological strength index as well as slope geometries. Three joint sets were recorded with spacing of 10–120, 5–45, 6–35 cm respectively, with slope angle of 75° and slope height equal to 65 m. Moreover, the rock samples were taken in laboratory to further discern required geotechnical parameters such as unconfined compressive strength, Young’s modulus, and Poisson’s ratio etc. The empirical and numerical techniques were applied to examine the slope’s health. Q-slope and Slope Mass Rating were the employed empirical method. Besides, the finite element approach was adopted to assess the slope stability numerical. Finally, outcomes of all these scientific assessments were compared with each other and ground reality. The Q-slope values achieved was 1.58 for the concerned slope, while the SMR value was 37. Finite element simulation yielded a safety factor of 1.6 for the dry condition. Furthermore, kinematic analysis of slope shows the possibility of planar and wedge modes of failures. Keeping in view the attained results, the slope should be excavated at an angle of 69°, while also making provisions for drainage of rain water. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Numerical Analysis of a Collapsed Tunnel: A case study from NW Himalaya, India
(Springer, 2022) A. Srivastav; V.H.R. Pandey; A. Kainthola; P.K. Singh; V. Dangwal; T.N. Singh
The present research explores the effect of various support system on deformation in a shallow tunnel from North-Western Himalaya, India. The deformation measurements assessed through field investigation were utilized to simulate the pre-existing and the present ground scenario, and furthermore in the evaluation of elastic parameters such as Young’s modulus and Poisson’s ratio through finite element back analysis. Back-analysed model predicted a Young’s modulus of 32 MPa and Poisson’s ratio of 0.3. Afterwards, the effect on the deformation of surrounding groundmass for unexcavated and the present condition were numerically ascribed at three different stages of excavation. The numerical application of forepoling and steel liners showed a marked reduction in the tunnel deformation. The combined effect of applied support methods was also enumerated and compared with individual stabilization method. This assessment may aid engineers and planners working in similar geological conditions, by making the excavation safer, stable and economical. © 2021, Indian Geotechnical Society.
Physico-Mechanical Characteristics of Vindhyan Sandstone, India
(Springer, 2022) V. Chaudhary; A. Srivastav; V.H.R. Pandey; Ashutosh Kainthola; S.K. Tiwari; S.B. Dwivedi; T.N. Singh
Quick and reliable estimation of intact rock strength parameters is vital for the excavation and stability measurement. The present research details the assessment of a few physico-mechanical parameters of sandstone rocks from Eastern India, and their statistical correlation and swift prediction. The dataset consists of 150 experimentally evaluated values for dry density, porosity, uniaxial compressive strength, tensile strength and Young’s modulus. Afterwards, the data were analyzed in the statistical environment “R” for correlation and distribution. For the ease of usage and implementation, density and porosity have been used as explanatory variables for the prediction of strength attributes. Initially, univariate linear regression models were devised, which yielded a coefficient of determination ranging between, 0.5 to 0.73. However, the r2 increased, in a range between 0.69 and 0.74, when multivariate analysis using the same independent variables was performed. In the present work, investigations and analysis have been done to predict the uniaxial compressive strength, tensile strength and Young’s modulus with dry density and porosity. Moreover, the statistical significance of the study has been discussed and compared with previous work. The present research can be used as a means to quickly and economically estimate strength parameters in the absence of a sophisticated testing setup. © 2021, The Institution of Engineers (India).
Recent Developments in Machine Learning and Flyrock Prediction
(Springer Science and Business Media Deutschland GmbH, 2022) Ramesh Murlidhar Bhatawdekar; Ashutosh Kainthola; V.H.R. Pandey; Singh Trilok Nath; Edy Tonnizam Mohamad
The blasting techniques are employed in mining and underground works to loosen the rock mass and ease the excavation. The blasting practices are economical and swifter in terms of their engineering application, however, they are of major environmental and safety concerns. The major issues related to blasting are flyrock, air over pressure, and ground vibrations etc. The rock fragments of rockmass are thrown outward after blasting, which can be threat to workers and machineries involved in the work, and sometimes nearby human settlements can be its victim. Therefore, an accurate prediction of the flyrock distance is the needed by mining practitioners. Earlier, experts have developed several empirical methods based on certain known parameters to assess flyrock distance. However, with time they become irrelevant and were easily replaced with advanced machine learning algorithm. The present study reviews some of these latest publications (2019–2021) examining flyrocks through artificial intelligent technique. The study incorporates types of machine learning models employed, input parameters used and number of datasets supporting the models. The input parameters were further classified according to rock-mass properties, blast design at site, and explosives responsible for blasting. Moreover, to compare the reliability of the model coefficient of correlation of the testing data of the all the documented model were evaluated. Rock density, rock mass rating and Shmidt hammer rebound number (SHRN) were found to be uncertain parameters. Artificial Neural Network (ANN) and other hybrid models for prediction of flyrock were compared. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.