Structural Integrity,
Health Monitoring
& Prognosis

How materials and structures behave when loading becomes nonlinear and severe — large deformation, fracture, impact and blast. The programme runs from material calibration on coupons through to full-scale experimental validation, pairing every test with an analytical or numerical model so that protection can be designed predictively rather than by trial.

Detecting, locating and sizing damage from sensor networks installed on operating structures, then predicting how much life remains. Sensors give a signal that depends on damage; numerical models supply the simulated experience needed to interpret that signal. The goal is safer operation at lower maintenance cost.

A digital twin is a set of models, both physics-based and data-driven, that update themselves in real time and follow the life of the physical system they represent. It must carry information about how degradation develops, and run fast enough to keep up with operation. The programme builds twins at component, platform and fleet level.

Modern systems are interconnected, multiphysical and heavily instrumented, and the signals they produce are too complex for hand-built features. Deep learning handles that, but labelled data for damaged conditions is scarce and black-box predictions are hard to trust. The programme works on both problems: embedding physics to cut the data requirement, and making predictions explainable.