SAFE RisCCS

We specialise in the development of data-driven methods for forecasting and estimation of risks associated with hazards where geology and weather interact.

A key area of expertise that informs our research on risk assessment is that of early prediction of failure in complex materials and structures — in particular, soil and rock bodies.

We achieve this by extracting hidden patterns on “dynamics” (the time evolution) of trigger events and motions in the nascent, precursory stages of failure – from data. Our platform comprises an amalgam of complex network analytics and AI techniques which can detect emerging order, relationships and subtle regime changes from an optimal subset of failure features. Initially, these features are highly disordered, but as failure approaches, the system partitions into subregions each exhibiting distinct dynamics.

Our capability to decode data, by mapping them to complex networks or a relevant feature state space, is what allows us to extract reliable indicators of where and when failure will likely occur.

A deep knowledge of the fundamental origins of failure at the microstructure level – combined with our analytics tools designed specifically for complex systems – makes our platform for early prediction of failure unique. From this, we can extract actionable intelligence on the future course of failure – irrespective of system size and material microstructure.