Structural health monitoring (SHM) systems have become a popular technology to collect information about the condition of structural assets. SHM is typically paired with traditional onsite inspection and maintenance (I&M). Incorporating SHM information in I&M planning is non-trivial, but is key to an improved prediction of the structural condition and to save costs on I&M. In this paper, we propose a framework to combine SHM information with visual inspections and repair actions. It utilizes a hierarchical dynamic Bayesian network (DBN) to probabilistically model the deterioration in the structural system, including all component interactions. The DBN calculates the evolution of the system probability of failure given the monitoring information and I&M actions. By attributing discounted costs to I&M actions and to the consequences of failure, we calculate the expected life cycle-cost for a given I&M strategy by simulating monitoring and inspection histories. The cost optimization of I&M strategies is then approached with a heuristic approach. Finally, the value of information of the SHM system is calculated by comparing the optimal strategies with and without monitoring system.
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