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

TL;DR

This paper introduces an adaptive digital twin framework using online Bayesian learning and dynamic Bayesian networks to improve decision-making in civil engineering applications.

Contribution

It develops a hierarchical Bayesian approach for online learning of transition dynamics within digital twins, enhancing personalization and robustness.

Findings

Improved decision-making accuracy in structural health monitoring.

Enhanced robustness and cost-effectiveness of digital twin applications.

Abstract

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 on digital twins. 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.