The holy grail of structural health monitoring is the quantitative linkage between data and decisions. While structural health monitoring has shown continued growth over the past several decades, there is a persistent chasm between structural health monitoring and the ability of structure owners to make asset management decisions based on structural health monitoring data. This is in part due to the historical structural health monitoring paradigm cast as a problem of estimating structural state and detecting damage by monitoring changes in structural properties (namely, reduced stiffness). For most operational structures, deterioration does not necessarily correspond to changes in structural properties with structures operating in their elastic regimes even when deteriorated. For structures like bridges, upkeep decisions are based on federally mandated condition ratings assigned during visual inspection. Since condition ratings are widely accepted in practice, the authors propose that condition ratings serve as lower limit states (i.e. limit states below yielding) with long-term monitoring data used to quantify these lower limit states in terms of the reliability index. This article presents a method to quantify the reliability index values corresponding to the lower limit states described by existing condition ratings. Once the reliability index thresholds are established, the data-driven reliability index of the in-service asset can be monitored continuously and explicitly mapped to a condition rating at any time. As an illustrative example, the proposed framework for tracking structural performance is implemented with long-term monitoring data collected on a pin-and-hanger assembly on the Telegraph Road Bridge, which is a highway bridge located in Monroe, MI. The successful implementation of the proposed method on the Telegraph Road Bridge results in a human-independent and truly data-driven decision-making strategy that is synergistic with the state of practice, eliminates risks associated with infrequent visual inspections, and expands condition ratings to encompass the entire measurable domain of damage that may exist in an asset.
A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data
