Back close

Physical Modelling of Rainfall Induced Landslides Using Laboratory Flume Tests

Dept/Center/Lab: Amrita Center for Wireless Networks and Applications (AWNA)

Project Incharge:Dr. Sabari Ramesh
Physical Modelling of Rainfall Induced Landslides Using Laboratory Flume Tests

Laboratory flume tests (small and large) will be carried out on scaled slopes prepared using soils collected from different landslide prone areas. The soil slopes will be prepared at density corresponding to field density and using the hydraulic system in the flume, slopes of different angles can be modelled. Rainfall of different intensities are generated using a rainfall simulation system consisting of nozzles and pressure gauges. The time to failure of slopes from the start of rainfall, duration and intensity of rainfall are noted down, thereby generating intensity-duration thresholds for different landslide prone areas. Based on the failure pattern different mitigation measures can also be suggested.

Amrita Team Members : Prof Maneesha V Ramesh, AWNA

Name of the Indian Collaborators

  • Dr. Rakesh J pillai, Associate Professor, IIT Palakkad
  • Ms. Akhila Priya S, Research Scholar, AWNA

Related Projects

Influence of Boron Carbide Addition on Neutron Shielding Ability of Cement Mortar Mix
Influence of Boron Carbide Addition on Neutron Shielding Ability of Cement Mortar Mix
Rotating machine degradation monitoring based on multi sensor signal fusion using deep learning models
Rotating machine degradation monitoring based on multi sensor signal fusion using deep learning models
Multi site Grant Management System
Multi site Grant Management System
Enhancing the Quantification of Wetland Methane Emissions by Data Assimilation and Remote Sensing Techniques to Improve Understanding of the Terrestrial Carbon Cycle
Enhancing the Quantification of Wetland Methane Emissions by Data Assimilation and Remote Sensing Techniques to Improve Understanding of the Terrestrial Carbon Cycle
Cloud based hybrid pattern recognition approach for fault diagnosis of motor driven rotating machines using motor current signature analysis
Cloud based hybrid pattern recognition approach for fault diagnosis of motor driven rotating machines using motor current signature analysis
Admissions Apply Now