Landslides pose a severe threat to the man-made and natural environment. As a result, many different artificial structures have been employed to attempt to mitigate the damage from these hazards. However, natural systems, such as forests of trees, can also play a defensive role by dissipating energy and reducing flow speed. This study uses a small-scale model forest to investigate the effectiveness of such sustainable measures. It examines how granular flows interact with arrays of tree structures at different densities, comparing the results to flow down a bare plane. Using image analysis of high-speed camera recordings, we track the lateral spread-out area and the speed of the flows. The results show that the trees have a strong decelerating effect in the downstream direction, and also slow the rate of lateral spread out. The knowledge gained from these experiments can be applied to the field scale, and could provide useful practical guidelines for alternative, sustainable hazard mitigation in mountainous areas.
Granular flows through a model-scale forest: Influence of tree density and implications for landslide mitigation
Lecture Notes in Civil Engineering
2019-10-11
6 pages
Article/Chapter (Book)
Electronic Resource
English
Flow-obstacle Interaction , Hazard Mitigation , Mass Movement , Granular Flow , Small-scale Experiments Engineering , Geoengineering, Foundations, Hydraulics , Sustainable Development , Landscape/Regional and Urban Planning , Structural Materials , Building Construction and Design , Transportation Technology and Traffic Engineering
Landslide mitigation in an urban setting: a case history
British Library Conference Proceedings | 2004
|A Performance Based Design Framework for Landslide Mitigation Measures
British Library Conference Proceedings | 2002
|Transportation Research Record | 2001
|Mitigation strategies of Cavon Grande landslide in Civita di Bagnoregio (Italy)
British Library Conference Proceedings | 2004
|Modeling dam break granular flows
HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2010
|