Abstract

Real-world structures operate under varying environmental and operational conditions, causing their dynamic properties to evolve over time. This non-stationary behaviour—observable across time scales from instantaneous to life-long—poses a major challenge for modelling, monitoring, and prognosis of structural systems: how can we model and manage these changes to ensure reliable damage detection and remaining useful life assessment? In this talk, we explore the primary sources of non-stationarity in structural dynamics and how they manifest in vibration data. Then, a parametric Bayesian time-series modelling framework is introduced, which provides compact, accurate, and uncertainty-aware representations of non-stationary dynamics. This approach enables computationally efficient analysis while enhancing robustness for SHM , operational vibration monitoring, and digital twin applications. Simulation, prediction, and the development of damage diagnosis algorithms are demonstrated on the basis of the proposed Bayesian modelling framework. Lastly, a discussion on future perspectives, open questions, and opportunities that will shape next-generation modelling and SHM strategies is provided.