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The C+ score for US bridges on the 2017 infrastructure report card underscores the need for improved data-driven methods to understand bridge performance. There is a lot of interest and prior work in using inspection records to determine bridge health scores. However, aggregating, cleaning, and analyzing bridge inspection records from all states and all past years is a challenging task, limiting the access and reproducibility of findings. This research introduces a new score computed using inspection records from the National Bridge Inventory (NBI) data set. Differences between the time series of condition ratings for a bridge and a time series of average national condition ratings by age are used to develop a health score for that bridge. This baseline difference score complements NBI condition ratings in further understanding a bridge’s performance over time. Moreover, the role of bridge attributes and environmental factors can be analyzed using the score. Such analysis shows that bridge material type has the highest association with the baseline difference score, followed by snowfall and maintenance. This research also makes a methodological contribution by outlining a data-driven approach to repeatable and scalable analysis of the NBI data set. 

The American Society of Civil Engineers (ASCE) Report Card for America’s Infrastructure gave bridges a C+ (mediocre) grade in 2017. Approximately, 1 in 5 rural bridges are in critical condition, which presents serious challenges to public safety and economic growth. Fortunately, during a series of workshops on this topic organized by the authors, it has become clear that Big Data could provide a timely solution to these critical problems. In this work in progress paper, we describe a conceptual framework for developing SMart big data pipelines for Aging Rural bridge Transportation Infrastructure (SMARTI). Our framework and associated research questions are organized around four ingredients: • Next-Generation Health Monitoring: Sensors; Unmanned Aerial Vehicle/System (UAV/UAS); wireless networks • Data Management: Data security and quality; intellectual property; standards and shared best practices; curation • Decision Support Systems: Analysis and modeling; data analytics; decision making; visualization, • Socio-Technological Impact: Policy; societal, economic and environmental impact; disaster and crisis management.