Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM) approach is adopted in the current work. In this approach the prognosis problem is considered as an information-sharing problem where data from past structures are exploited to make more accurate inferences regarding currently-degrading structures. For a given population, there may exist restrictions on the resources available to conduct monitoring; thus, the current work studies the problem of allocating such resources within a population of degrading structures with a view to maximising the damage-prognosis accuracy. The challenges of the current framework are mainly associated with the inference of outliers on the level of damage evolution, given partial data from the damage-evolution phenomenon. The current approach considers an initial population of structures for which damage evolution is extensively observed. Subsequently, a second population of structures with evolving damage is considered for which two monitoring systems are available, a low-availability and high-fidelity (low-uncertainty) one, and a widely-available and low-fidelity (high-uncertainty) one. The task of the current work is to follow an active-learning approach to identify the structures to which the high-fidelity system should be assigned in order to enhance the predictive capabilities of the machine-learning model throughout the population.

翻译：损伤预后可以说是结构健康监测（SHM）中最具挑战性的任务之一。为应对损伤预后中的常见问题，本研究采用了一种基于群体的结构健康监测（PBSHM）方法。在此方法中，预后问题被视为一个信息共享问题，通过利用历史结构的数据来对当前正在退化的结构做出更准确的推断。对于给定的群体，可用于实施监测的资源可能存在限制；因此，本研究探讨了在退化结构群体中分配此类资源的问题，旨在最大化损伤预后的准确性。当前框架面临的挑战主要在于，给定损伤演化现象的部分数据，如何推断损伤演化水平上的异常值。本方法首先考虑一个初始结构群体，对其损伤演化过程进行了广泛观测。随后，考虑第二个具有演化损伤的结构群体，该群体可使用两种监测系统：一种是低可用性、高保真度（低不确定性）的系统，另一种是广泛可用、低保真度（高不确定性）的系统。本研究的任务是遵循主动学习方法，确定应将高保真度系统分配给哪些结构，从而在整个群体范围内增强机器学习模型的预测能力。