This study aims to present a decision-making framework that combines bridge cable tension monitoring data with expert opinions. Dealing with uncertainty and lack of information in structural health monitoring of bridges (BSHM) is often a challenge for practical applications. This study proposes a framework that incorporates expert opinions when faced with uncertain and lack of information.
As part of an exploratory study, long-term monitoring of cable tension is conducted by installing accelerometers on a cable-stayed bridge. The cable tension, identified from acceleration data, is used to model monitoring uncertainty and probability density under normal conditions. Subsequently, the probability of failures is calculated using reliability analysis. Decisions are then made using a Bayesian hypothesis test. In the decision-making process, both the optimal decision plan for the target structure and the life-cycle cost, including risks, are calculated.
By modeling monitoring uncertainty with one year of monitoring data, the feasibility of an evidence-based decision-making framework is examined. Future studies are expected to demonstrate the cost-effectiveness of this framework in bridge maintenance strategies.