Open AccessA comparative study of different kinematics - based methods to predict the time of slope failure
Author(s) -
Shimol Philip,
R. Rakendu,
Joe G. Philip
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1116/1/012158
Subject(s) - landslide , kinematics , scale (ratio) , slope stability , geology , acceleration , stability (learning theory) , displacement (psychology) , warning system , geotechnical engineering , computer science , econometrics , geography , cartography , mathematics , machine learning , psychology , telecommunications , physics , classical mechanics , psychotherapist
Landslide forecasting is considered one of the key components of early warning systems. Predicting landslide failure at the slope-scale is a major scientific challenge, but on the other hand, it can mitigate the consequences of slope failures in terms of both human lives and economic losses. Therefore, landslide forecasting is a subject worthy of further research efforts due to its social implications. Landslide forecasting consists of the prediction of a slope failure in spatial and/or temporal terms. Temporal prediction can be performed at a regional/global scale or on a slope-scale. This paper focuses on the different methods for temporal prediction of landslides on a slope scale based on kinematic parameters such as displacement and its derivatives (velocity and acceleration). These kinematic parameters are directly related to the stability conditions of the moving mass. This paper accurately explains the correlation between the kinematics and the collapse time of a slope. Also, the present study systematically explains and compares the methods such as the empirical and semi-empirical methods for forecasting the failure time of slopes with their corresponding advantages and limitations. Finally, a detailed outline of the future technology and risks in this area are also introduced in this paper.