Evaluating cut slope failure by numerical analysis—a case study

Abstract

Slope failure is very common phenomenon in hilly regions, especially in young techno active mountainous like Himalayas. It is hazardous because of the accompanying progressive movement of the slope-forming material. In order to minimize the landslide effects, slope failure analysis and stabilization requires in depth understanding of the process that governs the behavior of the slope. The present article mainly deals with the analysis of the stability of road cut slopes of Rudraprayag Area, Uttarakhand, India. The area experiences local as well as regional slides every year. Extensive field study was carried out along the road cut slopes. Laboratory experiments were conducted to determine the various Physio-mechanical properties of rock mass. These properties have been used as input parameters for the numerical simulation of slope using FLAC3D (Fast Lagrangian Analysis of Continua) including geological discontinuities. The computed deformations and the stress distribution along the failure surface are compared with the field observations. The study indicates that the overall slope is unstable except at the location E where slope is critically stable. The effects of instability have been thoroughly considered and remedial measures have been recommended.

Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India

“…Fell et al 2008 considered the landslide susceptibility for identification of landslide prone sites and their relation to the set of causal factors in that area. The landslide susceptibility mapping generally involves two methods (I) qualitative which is based on expert knowledge and the landslide inventory development (Saha et al, 2002) such as analytical hierarchy process (AHP) used by many researchers (Komac 2006;Kayastha et al 2012;Wu et al 2016;Kumar and Anabalgan 2016;Achour et al 2017) (II) quantitative methods including bivariate and multivariate modeling methods for statistical evaluation of landslides occurrences (Yin and Yan 1988;Kumar et al 1993;Anbalagan and Singh 1996;Dai and Lee, 2002;Saha et al 2005; Lee and Sambath 2006;Mathew et al 2007;Dahal et al 2008;Singh et al 2008;Pradhan and Lee 2010;Rozos et al 2011;Yalcin et al 2011;Kayastha et al 2013;Bijukchhen et al 2013;Anbalagan et al, 2015;Rawat et al 2015;Sharma and Mahajan 2018;Chen et al 2016). In India landslide susceptibility mapping for Gharwal and Kumaun region of Uttarakhand has been carried out by Pachauri and Pant 1992;Gupta et al 1999;Anabalgan et al, 2008;Anbalagan et al, 2015;Anabalgan, 2016 whereas, Sarkar andKanungo, 2004;Sarkar et al 2013; have mapped the landslides of Darjeeling Himalaya for statistical correlation with the causal factors.…”

Finite Element Analysis of Rock Slope Stability Using Shear Strength Reduction Method

“…Economics could be improved by steepening the slope thereby reducing the amount of waste excavation; however, excessive steepening of slope could result in failure leading to loss of life and damage to property (Singh et al 1989;Singh and Singh 1992). The factors, which mainly influence the stability of a typical open-pit slope, are the shear strength parameters of slope forming material, the presence and characteristics of discontinuities in the slope mass and the groundwater conditions (Singh and Monjezi 2000;Singh et al 2008).…”

Development of an adaptive relevance vector machine approach for slope stability inference

“…However, these methods in themselves are different from each other with varying assumptions of slip surface and internal forces among slices. The calculated factor of safety will be probably different for the same slope if it is calculated by different methods [25,[27][28][29][30]. These limitations urge the application of other new techniques in approaching this problem.…”