Bridge deterioration and aging are important problems in the United States. According to the infrastructure report card from the American Society of Civil Engineers, as of 2016, almost one in 11 (9.1%) bridges are structurally deficient and approximately 4 out of 10 (40%) bridges are older than 50 years. Rehabilitation cost for these bridges are estimated to be about $123 billion, pointing to the need for proper bridge management plans. There are many bridge management systems in the world. All of these lack of an integrated SHM system and are subject to criticism of being subjective. Condition-Based Maintenance (CBM) coupled with the Structural Health Monitoring (SHM) data can be used to actively manage bridges while minimizing subjective effects.

The current research work consisted of two primary tasks. The first task was to update the current automated CBM-SHM framework developed at the Bridge Engineering Center (BEC) at Iowa State University (ISU), by improving its current load rating calculation process. The current load rating approach underestimates the rating factor of a bridge by 20% to 40%. The load rating calculation process was improved by developing a relationship between moment of inertia and flexural strength of bridges. An extensive experimental program was conducted to validate the relationship. The proposed method may significantly improve the rating factor of a bridge.

The second task was to develop a novel condition rating prediction model to predict future condition ratings of the bridges. The condition rating information in the National Bridge Inventory (NBI) database was used in this development. The research group developed two different types of future condition rating prediction models, Current Practice Model (CPM) and Deterioration Prediction Model (DPM). CPM is capable of simulating the effects of historical maintenance activities and DPM does not consider the effects of historical maintenance activities when predicting the future condition rating probabilities. Both CPMs and DPMs were quantitatively and qualitatively validated.