Journal of Computer Science


P. Nalina, T. Meenambal and R. Sathyanarayan Sridhar

DOI : 10.3844/jcssp.2014.1107.1114

Journal of Computer Science

Volume 10, Issue 7

Pages 1107-1114


The stability of slopes is always under severe threats in many parts of Western Ghats, especially in Kallar-Coonoor 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 weight of soil and Slope geometry. The stability of a slope is measured by its factor of safety using geometric and shear strength parameter based on infinite slopes. In this present study, investigation was carried out at 32 locations in the above said hill road stretch to estimate the factor of safety of the slope determined by Mohr-Coulomb theory based on shear strength parameter calculated from direct shear test which is a conventional procedure for this study. Back Propagation Artificial 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 locations, the study of BP-ANN is trained with parameters of first 25 locations. Factor of safety was calculated for the remaining 7 locations. The results obtained in BP-ANN 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 actual 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 observed landslide locations.


© 2014 P. Nalina, T. Meenambal and R. Sathyanarayan Sridhar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.