The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

翻译：数字孪生概念为实现土木工程系统的状态维修与预测性维修范式提供了极具吸引力的机遇，从而能够降低生命周期成本、提升系统安全性及可用性。本文提出一种面向土木工程结构健康监测、维护与管理规划的预测性数字孪生方法。采用概率图模型对资产-孪生耦合动力学系统进行编码，该模型可充分考虑所有相关不确定性来源。具体而言，时变观测-决策流通过动态贝叶斯网络建模。通过将传感数据与深度学习模型相融合，实现实时结构健康诊断。数字孪生状态以序贯贝叶斯推断方式持续更新，并用于动态决策框架中指导维护与管理行动的最优规划。离线阶段包括通过降阶数值模型生成训练数据集，以及计算依赖健康状态的控制策略。该方法在两个合成案例（悬臂梁与铁路桥梁）中进行了评估，展示了健康感知数字孪生的动态决策能力。