Structural health monitoring (SHM) enables bridge engineers to monitor the structural behavior of entire bridges or individual bridge components. At the request of the Iowa Department of Transportation, a fiber optic SHM system was developed and deployed by the Iowa State University (ISU) Bridge Engineering Center (BEC) to detect gradual or sudden damage in fracture-critical bridges (FCBs). With the equipment that was selected and the software that was developed in this research, the SHM system is deployable to any girder bridge that supports one-way traffic;Significant laboratory and field testing was conducted as part of this research to select hardware components for the SHM system. In the laboratory testing, several fiber bragg grating (FBG) fiber optic sensors (FOSs) were bonded to steel coupons with multiple adhesives, and the coupons were subjected to cyclic and sustained tensile loads. The FOS/adhesive combinations with the best performance were selected for use in the FCB SHM system. After FOSs were installed at critical locations in the US Highway 30 (US30) demonstration bridge, conventional strain sensors were installed next to the FOSs, and measurements between the technologies for bridge responses to ambient traffic loads were compared. Results revealed good agreement between the sensing technologies;Using the software developed in this research, the FCB SHM system was trained with measured US30 bridge performance data that were collected by the FOSs. During the training process, the SHM system filtered data and extracted event extrema from quasi-static strain records. The SHM system used the extrema to develop relationships among the FOSs, which are similar to those that are used with bivariate control charts in statistical process control (SPC). Since the relationships were developed from measured data, the SHM system was essentially trained to identify the typical bridge behavior for the structural condition that existed when the training data was collected. Relationships that were established during training are used to evaluate future strain data that are collected. Daily evaluation reports, which utilize histograms to summarize evaluations, are autonomously generated by the SHM system and delivered to the bridge engineer for interpretation and decision making. Changes in histogram distributions are predicted to be indicative of damage formation;Significant effort was given to address the areas of SHM that are considered to hinder its general acceptance for practical applications. Specifically, data mining and storage procedures, as well as methods of presenting SHM results to bridge engineers, were addressed. Improved data mining procedures were developed and the amount of saved data from the monitoring has been significantly reduced. In addition, evaluation reports are presented to bridge owners in a familiar format that allows for rapid visual assessment. With the SHM system developed in this research, FCBs are able to be continuously monitored for damage formation, and thus, bridge owners are able to better manage their bridge inventory.