As the backbone of the US transportation system, bridges are also its most visible part. There are over 600,000 bridges across all US states ensuring network continuity. In order to optimize such activities and use the available monies most effectively, a solid understanding of the parameters that affect the performance of concrete bridge decks is critical. The National Bridge Inventory (NBI), perhaps the single-most comprehensive source of bridge information, gathers data on more than 600,000 bridges in all fifty states, the District of Columbia, and the Commonwealth of Puerto Rico. Recently there has been a growing interest in analyzing the NBI database. The NBI uses visual inspection, a commonly practiced damage detection method, to rate bridge decks. Focusing on concrete highway bridge deck performance, the present study developed a nationwide database based on NBI data and other critical parameters, such as bridge age, deck area, climatic regions, and distance from seawater. Additionally, two new performance parameters were computed from the available concrete bridge deck condition ratings (CR): Time-in-condition rating (TICR) and deterioration rate (DR). Following the aggregation of all these parameters to form a nationwide database, filtering and processing were performed. Approaches to dealing with inconsistencies and missing data are proposed as well. After developing the nationwide database this research presents network-level, one-way statistical relationships to get a better understanding of the parameters. ☐ Next, a data mining technique on the nationwide database was used to analyze the data. Data mining is a discovery procedure to explore and visualize useful but less-than-obvious information or patterns embedded in large collections of data. Given the amount and variety of parameter types in a large data set such as that of the nationwide database, using traditional clustering techniques for discovery is impractical. As a consequence, this research has applied a novel data discovery tool called two-step cluster analysis to visualize associations between concrete bridge deck design parameters and bridge deck condition ratings. Two-step cluster analysis is a powerful knowledge discovery tool that can handle categorical and interval data simultaneously and is capable of reducing dimensions for large data sets. The two-step cluster analysis is a useful tool for bridge owners and agencies to visualize general trends in their concrete bridge deck condition data and support them in their decision-making processes to effectively allocate constrained funds for maintenance, repair, and design of bridge decks. ☐ Understanding the attributes of bridge deck performance is central to asset management. This research attempts to characterize how various environmental and structural parameters affect bridge deck performance by employing a binary logistic regression. The logistic model shows the relationship between a dependent variable (lowest vs. highest bridge deck deterioration) and the relative importance of a number of independent variables selected for this study (predictor variables). Observations of extreme bridge deck deterioration taken from the nationwide database were used in the model. Bridge deck deterioration was computed as the decrease in CR over time. Maintenance responsibility fulfillment, functional classification of inventory route, design and construction type, average daily truck traffic, climatic regions, and distance to seawater, were all used as independent variables. Our application of a binary logistic regression model for bridge deck deterioration provides practical insight regarding how certain parameters influence bridge deck performance. ☐ A leading factor in structural decline of highway bridges is the deterioration of concrete decks. Thus, a method to forecast bridge deck performance is vital for transportation agencies to allocate future repair and rehabilitation funds. The objective of this study was the development of a nationwide CR deterioration model based on the nationwide database through the use of a Bayesian statistical approach that predicts probability of CR decrease. In addition to CR data, the impact of other governing factors on CR decrease are shown in the paper, such as average daily truck traffic (ADTT), maintenance responsibility fulfillment, deck structure type, and regional climate effect. One singular advantage of this method is that it can be continually updated as additional NBI information becomes available. Moreover, the results of this model can be used as prior data in future Bayesian studies. The results presented in this study, by providing a better idea of how US concrete bridge decks are performing based on the NBI data, are intended to furnish a progressive bridge management system. ☐ Results yielded by each of the analysis above will encourage future researchers to add other crucial parameters not contained in the nationwide database such as structural design characteristics (e.g., minimum deck thickness), construction practices (e.g., curing practices), specifications (e.g., water-to-cement ratio), and other notable factors (e.g., application of deicing salts). Furthermore, analyze the nationwide database in various statistical application areas leading to more accurate understating of the factors affecting bridge deck deterioration and enhanced deck deterioration prediction models.