Structural Health Monitoring (SHM) technologies offer much promise to the risk management of the built environment, and they are therefore an active area of research. However, information regarding material properties, such as toughness and strength is instead measured in destructive lab tests. Similarly, the presence of geometrical anomalies is more commonly detected and sized by inspection. Therefore, a risk-optimal combination should be sought, acknowledging that different scenarios will be associated with different data requirements. Value of Information (VoI) analysis is an established statistical framework for quantifying the expected benefit of a prospective data collection activity. In this paper the expected value of various combinations of inspection, SHM and testing are quantified, in the context of supporting risk management of a location of stress concentration in a railway bridge. The Julia code for this analysis (probabilistic models and influence diagrams) is made available. The system-level results differ from a simple linear sum of marginal VoI estimates, i.e. the expected value of collecting data from SHM and inspection together is not equal to the expected value of SHM data plus the expected value of inspection data. In summary, system-level decision making, requires system-level models.

翻译：结构健康监测（SHM）技术为建筑环境风险管理带来了巨大潜力，因此成为活跃的研究领域。然而，材料属性（如韧性和强度）的信息通常通过破坏性实验室测试获取。同样，几何异常的存在更常见于通过检测来识别和量化。因此，应当寻求风险最优的组合方案，同时认识到不同场景将对应不同的数据需求。信息价值（VoI）分析是量化预期数据采集活动预期收益的成熟统计框架。本文以某铁路桥应力集中位置的风险管理为背景，量化了检测、SHM与试验的不同组合预期价值。本分析所使用的Julia代码（概率模型与影响图）已开放获取。系统层面的结果与边际VoI估计值的简单线性累加存在差异，即同时采集SHM与检测数据的预期价值不等于SHM数据预期价值与检测数据预期价值之和。总之，系统级决策需要系统级模型。