Adaptive digital twins for predictive decision-making: online Bayesian learning of transition dynamics

This work shows how adaptivity can enhance the value realization of digital twins (DTs) in civil engineering. We focus on adapting the state transition models within DTs represented through probabilistic graphical models. The bi-directional interaction between the physical and virtual domains is modeled using dynamic Bayesian networks. By treating state transition probabilities as random variables endowed with conjugate priors, we enable hierarchical online learning of transition dynamics from one state to another through effortless Bayesian updates. We provide the mathematical framework to account for a larger class of distributions with respect to the current literature on DTs. To compute dynamic policies with precision updates, we solve parametric Markov decision processes through reinforcement learning. The proposed adaptive DT framework enjoys enhanced personalization, increased robustness, and improved cost-effectiveness. We assess our approach on a case study involving structural health monitoring and maintenance planning of a railway bridge.