Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
This work addresses predictive decision-making for civil infrastructure maintenance, representing an incremental improvement through online adaptation of existing digital twin methods.
This paper tackles the problem of enhancing digital twins in civil engineering by developing an adaptive framework that learns transition dynamics online through Bayesian updates, resulting in improved personalization, robustness, and cost-effectiveness as demonstrated in a structural health monitoring case study for a railway bridge.
This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models within digital twins 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 a 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. To compute dynamic policies with precision updates we solve parametric Markov decision processes through reinforcement learning. The proposed adaptive digital twin 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.