Cost-Effective Lifetime Structural Health Monitoring Based on Availability

The state of a structural system subjected to deterioration processes is changing continuously. This state cannot be reliably predicted without considering both aleatory and epistemic uncertainties. To reduce the epistemic uncertainty, inspection and structural health monitoring (SHM) should be performed, and the performance prediction model should be updated periodically. Continuous monitoring is needed to reliably assess and predict the performance of structures. However, due to limited financial resources, continuous monitoring is not practical. Therefore, a cost-effective SHM strategy is necessary. In this paper, the probability that the performance prediction model based on monitoring data is usable in the future is computed by using the statistics of extremes and availability theory. This probability represents the availability of the monitoring data over nonmonitoring periods. The monitoring cost and availability can be found by solving a biobjective optimization problem. This problem consists in simultaneously minimizing the total monitoring cost and maximizing the availability of the monitoring data for performance prediction. Pareto solutions associated with monitoring duration and prediction duration are obtained. The proposed approach is applied to an existing bridge.