Bayesian Decision-Making Process Including Structural Health Monitoring Data Quality for Bridge Management

The article introduces a decision-making framework and process for including the data quality (DQ) of structural health monitoring (SHM) for bridge management. The decision-making process relies on Bayesian and utility theories. Maintenance of existing bridges can benefit from SHM to obtain data on the bridge condition, which helps suggest maintenance decisions. Thus, the data quality plays a crucial role in preventing bridge-strengthening action when unnecessary and not intervening when needed. However, no management strategy or decision-making process has integrated the data quality yet. Aiming to fill those gaps, (Makhoul, 2022) offered data quality indicators and metrics. Then, this article extends the work to provide an updated general assessment procedure for existing bridge structures and a decision-making process to embed the SHM DQ. The decision-making process for SHM data quality uses the Bayesian and utility theory and considers uncertainties. It selects the optimal decision and evaluates the value of DQ assessment for monitoring strategies. Finally, a monitored bridge is used as a case study to apply the process, and data quality variation effect on the decision is analyzed. Results and comparisons are offered, and accordingly, the bridge owner is recommended to invest right from the start in good DQ for SHM.