In response to industry and community demands, the modelling and management of geotechnical risk has become a major issue in the design of all forms of physical infrastructure. Indeed, the natural variability in geological deposits, coupled with the existing uncertainty in loading conditions and limited on-site data, make this a very challenging aspect for government authorities and engineering practice. There is a compelling case to extend traditional deterministic design procedures to a stochastic framework. Areas of research within the PRCGSE that pertain to Geo-Risk include the development of new stochastic limit and shakedown analysis techniques to predict the load capacity of geostructures under static and cyclic loads and the risk-based prediction of slope stability and landslides and their effect on onshore and seabed infrastructure (such as pipelines).

Find out more about some of the current projects related to this theme:


Rockfall presents a significant risk to users and infrastructure in coastal areas, mountainous areas and along transport corridors of Australia, including in New South Wales. The PRCGSE is conducting world class research on all the aforementioned aspects of rockfall.

Proximity Remote Sensing and Continuous Monitoring

The PRCGSE is conducting innovative world class research to understand the limits of current technologies and to develop new low-cost effective systems for continuous monitoring.

Bayesian back analysis for settlement prediction of embankments built on soft soils

The Bayesian approach is a statistically based method that finds the most probable fit to the monitoring data conditional on the adopted geotechnical material parameters and measurement errors.

Data-driven predictive railway maintenance for preventing track failure

Every year, railway organisations spend large amounts of money on rail track maintenance and renewal works to ensure proper serviceability of the railroad network.

Efficient early warning modelling for predicting landslides

Read about the PRCGSE's proposed early warning model for predicting landslides due to heavy rainfall.