MODEL‐ASSISTED PROBABILISTIC RELIABILITY ASSESSMENT FOR STRUCTURAL HEALTH MONITORING SYSTEMS

This paper describes a model‐assisted probabilistic methodology to ensure the reliability of SHM systems for damage detection, localization, and sizing. A hierarchical approach is presented that attempts to minimize the number of samples, the length of time, and degree of full‐scale testing required for statistically meaningful characterization results. The feasibility of applying this approach to typical sensing methods found in SHM systems is investigated, and additional challenges concerning model reliability and uncertainty propagation are addressed.