Bridge management involves all efforts to build, preserve, and operate bridge networks cost-effectively with an objective to deliver the best value for the public tax dollars spent. The dissertation consists of three complementary studies that address both bridge management policies and condition data that contribute to bridge management practices. This dissertation begins with an overview of federal and state government bridge management efforts taken in conjunction with the federal bridge programs in the last 40 years. While the majority of the states have implemented a BMS, the level of implementation is varied, and the overall input from BMSs to network-level decisions remains minimal. Survey findings from 40 states indicate that federal funding eligibility is the major criterion that impacts state-level bridge management decisions. State transportation agencies need federal guidance on areas such as using decision support tools, implementing BMSs, and improving data quality. The findings from the study are useful to both practitioners and policy makers, and identify challenges and needs for bridge management at both federal and state level. Following the policy study, a statistical comparison of field NBI condition ratings and ratings generated by FHWA's NBI Translator (BMSNBI) algorithm for Iowa bridges is presented. Statistical analysis indicates that the ratings generated by the NBI Translator algorithm are not representative of actual NBI ratings. Results from the research raise questions about the effectiveness of the algorithm. Final study in this dissertation presents a new methodology to predict National Bridge Inventory (NBI) condition ratings from bridge management system (BMS) element condition data, based on Classification and Regression Trees (CART). The proposed methodology achieves significantly better accuracies than other methodologies reported in the literature for the data set used in this study. The CART prediction methodology uses simple and logical conditions of BMS element condition data to predict NBI condition ratings and has potential use for federal and state transportation agencies to summarize bridge condition data
