Online and Continual Learning for Structural Health Monitoring

In this thesis proposal, I explored a framework for online continual learning in structural health monitoring (SHM). By leveraging global modelling, the approach captures richer representations of time-series data.

A key component is the Recurrent Rauch-Tung-Striebel (R-RTS) smoother, which enables online parameter updates and real-time learning. I also presented preliminary advances in continual learning through parameter intervention.

Source: CIV-ML’s Seminars