Slope failures cause considerable social and economic harm. In the UK, impacts are principally economic, through the damage and disruption to our transportation (e.g. rail) and utilities (e.g. dams) networks, costing over £100 million per year. Further afield, in mountainous areas with high rainfall impacts are considerably greater, including much greater loss of life. Conventional landslide monitoring is still heavily reliant on rainfall information, which is of limited use at the local scale, or surface observations (e.g. walkover surveys or remote sensing), which can only identify the surface expression of failure, by which time it is often too late to take remedial action.
Geophysical imaging technologies are able to ‘see-inside’ unstable slopes to identify precursors to slope failure. Although proof-of-concept has been developed for several key techniques (e.g. geoelectrics, seismics), further work is required to translate geophysical observations into quantitative geotechnical information (e.g. moisture-content, pore-pressure, shear-strength) that can be used directly by engineers to assess, predict and manage landslide hazard. Our hypothesis is that suitably calibrated geophysical models can enable quantitative assessments of slope condition at the spatial scales required for landslide early-warning.
The specific aim of this project is to explore and improve the resolution and reliability of geophysically-derived slope condition information in order to inform: (1) monitoring installation design; (2) sampling strategy; (3) geotechnical models of slope stability; and (4) early warning threshold determination. We will use geophysical infrastructure and data from our landslide observatories in the UK, coupled with laboratory testing to relate geophysical and geotechnical parameters. Furthermore, the project has the potential to contribute novel monitoring and modelling approaches to sites identified by the CASE partners, Atkins. The overarching objective is to shift the geophysical monitoring of landslides (in both natural and engineered slopes) from the research sphere towards providing quantitative decision-support information for hazard management.
Applicants should have strong numerical abilities and hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Earth Science, Physics, Engineering, Environmental Science, Natural Sciences.