Minimising the effects of landslides
Whether triggered by natural or human causes, landslides cause death and financial ruin. In contrast, slow-moving landslides are long-lived and do not necessarily pose a threat to public safety. However, these creeping masses of earth can inflict damage to infrastructure networks. The overall aim of the 'Mechanism, modeling and forecasting of landslide displacements' (FOLADIS) project was to become better aware of landslide mechanisms, triggering factors and reliable forecasting methods for disaster prevention and mitigation. The focus was on landslides mostly involving cohesive soils — earth such as clay or granite that has an innate stickiness. This soil also has the ability to be moulded or shaped and is hard to break up when dry. Team members gathered slow-moving landslide case histories from around the world to identify the common triggering factors and failure mechanisms, as well as to predict slope movements. These case histories were then further examined to forecast particular types of slope movements and to evaluate slope stability. Predicting the failure time — time of occurrence — of landslides significantly improves the ability to evaluate and manage its risk. Laboratory tests examined how precise failure time prediction approaches are. Researchers created threshold slope displacement rates for use in developing varied-degree alarm levels and early warning systems. Landslide time of failure forecasting models are key to minimising the negative impacts of this natural hazard. FOLADIS outcomes will enhance processes and tools that predict the time of potential landslides and help avert the devastating effects. The project has contributed to research on the physical nature of landslides and led to increased public awareness of matters concerning the natural hazard.
Keywords
Landslides, human encroachment, slow-moving landslides, forecasting, landslide displacements, disaster prevention, cohesive soils, slope movements, failure time prediction, early warning systems, forecasting models, natural hazard
