Forecasting Landslide Occurrences Under Heavy Rainfall using the Novel Soil Moisture Extended Cohesive Damage Element (SMECDE) Method

Landslides cause extensive human suffering and financial losses worldwide. Damage to the 89,000 km of National and State Highways in the region are estimated to cost 1bn USD every year. Landslides are commonly initiated by heavy rainfall to causes critical reduction of soil cohesion along weak interfaces in stratified terrain. Accurate spatial and temporal forecasting for mitigation is a global challenge. There is an urgent need to establish global digital networks to forecast landslides caused by heavy rainfall and their potential damage areas to increase resilience and enable planning to mitigate extensive human and financial losses. This project ambitiously aims to develop and validate a novel methodology (Soil Moisture Extended Cohesive Damage Element (SMECDE) Method) for significantly improved understanding of landslide failure mechanisms and to predict landslide propagation based on weather forecasts. This will be achieved by integrating a Soil Moisture Decohesion Model (SMDM), which combines lateral and vertical variation in soil moisture with a predictive Extended Cohesive Damage Element (ECDE) approach.