Population-based structural health monitoring (PBSHM), seeks to address some of the limitations associated with data scarcity that arise in traditional SHM. A tenet of the population-based approach to SHM is that information can be shared between sufficiently-similar structures in order to improve predictive models. Transfer learning techniques, such as domain adaptation, have been shown to be a highly-useful technology for sharing information between structures when developing statistical classifiers for PBSHM. Nonetheless, transfer-learning techniques are not without their pitfalls. In some circumstances, for example if the data distributions associated with the structures within a population are dissimilar, applying transfer-learning methods can be detrimental to classification performance -- this phenomenon is known as negative transfer. Given the potentially-severe consequences of negative transfer, it is prudent for engineers to ask the question `when, what, and how should one transfer between structures?'. The current paper aims to demonstrate a transfer-strategy decision process for a classification task for a population of simulated structures in the context of a representative SHM maintenance problem, supported by domain adaptation. The transfer decision framework is based upon the concept of expected value of information transfer. In order to compute the expected value of information transfer, predictions must be made regarding the classification (and decision performance) in the target domain following information transfer. In order to forecast the outcome of transfers, a probabilistic regression is used here to predict classification performance from a proxy for structural similarity based on the modal assurance criterion.

翻译：基于人群的结构健康监测（PBSHM）旨在解决传统SHM中因数据稀缺而产生的局限性。PBSHM方法的核心原则是，信息可以在足够相似的结构之间共享，以改进预测模型。已有研究表明，迁移学习技术（如领域自适应）在构建PBSHM统计分类器时，是实现结构间信息共享的高效技术。然而，迁移学习技术并非没有缺陷。在某些情况下，例如当人群中结构的数据分布不相似时，应用迁移学习方法反而会损害分类性能——这种现象被称为负迁移。鉴于负迁移可能带来的严重后果，工程师明智的做法是提出这样的问题：“何时、在结构之间传递什么以及如何传递？”本文旨在以代表性SHM维护问题为背景，结合领域自适应技术，针对模拟结构群体中的分类任务，演示一种迁移策略决策过程。该迁移决策框架基于信息传递预期价值的概念。为了计算信息传递的预期价值，必须对信息传递后目标领域中的分类（及决策性能）进行预测。为了预测迁移结果，本文采用概率回归方法，基于模态保证准则作为结构相似性的代理指标，预测分类性能。