Assessment of Karmi Landslide Zone, Bageshwar, Uttarakhand, India

Abstract

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.

Hill slope stability examination along Lower Tons valley, Garhwal Himalayas, India

“…Hence, both FEM and FDM achieve factor of safety (FOS) by reducing the shear strength of soil/rock mass until the condition of slope failure arrives (Dawson et al 1999). In other words, cohesion and angle of internal friction are simultaneously and continuously weakened, multiple times by a factor (strength reduction factor or SRF) until the slope instability arrives to the condition as given in Equations ( 1)-( 6) (Zhao et al 2015;Tiwari et al 2020).…”

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

“…Landslides represent a severe disaster globally, inflicting catastrophic socio-economic losses on communities residing in mountainous regions [1,2]. Moreover, the environmental issues associated with these phenomena are myriad [3]. In recent years, the frequency of landslides and slope instabilities has sharply increased, primarily due to the cumulative effects of rapidly changing climate conditions and heightened anthropogenic disturbances [4,5].…”