Abstract
The stability of slopes is always under severe thre ats in many parts of Western Ghats, especially in K allarCoonoor hill road stretch, causing disruption, loss of human life and economy. To minimize the instability of soil slope in between Kallar-Coonoor, a critical evaluation of roads is required. The stability of slopes depends on the soil shear strength parameters such as Cohesion, Angle of internal friction, Unit weigh t of soil and Slope geometry. The stability of a slope i s measured by its factor of safety using geometric and shear strength parameter based on infinite slopes. In this present study, investigation was carried ou t at 32 locations in the above said hill road stretch to es timate the factor of safety of the slope determined by MohrCoulomb theory based on shear strength parameter calculated from direct shear test which is a conventi onal procedure for this study. Back Propagation Artifici al Neural Network (BP-ANN) Model is used to predict the factor of safety. The input parameters for the (BP-ANN) are chosen as Cohesion, Angle of internal friction, Density and Slope angle and the factor of safety as output. Out of the parameters of 32 loca tions, the study of BP-ANN is trained with parameters of f irst 25 locations. Factor of safety was calculated for the remaining 7 locations. The results obtained in BP-A NN method were compared with that of conventional method and observed a good agreement between these two methods. The results obtained from these two methods were also compared with the details of actu al field Landslide occurred and indicates 71.4% of conventional method locations matching with the physical occurrences and 85.7% of BP-ANN predicted vulnerable locations match with the physically obse rved landslide locations.
Highlights
Damages tohuman life, properties and it is a well known region for frequent landslide occurrence during
Comparison of these results indicates that the conventional method shows 71.4% accuracy, while in Back Propagation Artificial Neural Network (BP-Artificial Neural Network (ANN)) predicts the slope stability with 85.7% accuracy
It was learnt from the present study that the hill slope of study area mainly constituted by the cohesion less soil, which provides more conducive environment to landslide occurrence especially during heavy rainfall
Summary
Damages tohuman life, properties and it is a well known region for frequent landslide occurrence during. Especially in hilly area and it is defined as the natural or There is no drainage system in this stretch which leads to manmade failure of soil mass (Ganapathy et al, 2010) It progressive disintegration of the structure of the soil mass occurs mainly in the hill slopes, embankment or cuttings of and results in landslide. The area in which bed dip towards the slope with dip angle greater than 45° are unsafe (Arora, 1988; Liang and Zhang, 2012; Neaupane and Achet, 2004) and more prone to landslide These facts warrant a separate detailed scientific study on the slope stability of this road stretch. It was observed that factor of safety predicted by BP-ANN closely matches with physically occurred landslides
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