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US Bridges scored a C+ on the 2017 infrastructure report card. There is a need for substantial improvement in bridge conditions as many of them are structurally deficient and can become unsafe in the near future. The nation's most recent bridge rehabilitation estimate is $123 billion. Many state's department of transportation (DOT) have limited resources, leaving them with difficult decisions about where to invest and allocate limited resources. To make cost-effective decisions, these bridge stakeholders need clean data and studies to estimate the future bridge conditions. This will give them data-driven, accurate life-cycle models for bridges and improved inspections intervals. Previous researchers have identified factors that may cause bridge deterioration. Unfortunately, these researchers limit their data to specific regions and bridge types. This severely limits their result's general applicability. In this thesis, we approach bridge health-related decision making challenges using a novel data science perspective. This bridge health deterioration study provides new insights into making bridge rehabilitation and reconstruction decisions. In this research, we use all US inspection record data regulated by the Federal Highway Agency that is available in the National Bridge Inventory (NBI) database and precipitation data from the Center for Disease Control and Prevention (CDC). Our specific contributions are 1) providing a reference big data pipeline implementation for bridge health-related datasets; 2) demonstrating the feasibility of data science to study bridge deterioration; 3) developing repeatable methods for sharing large datasets with reproducible analysis driven by data science and making them available to other researchers. Further, our curated datasets and platforms are used to analyze the statistical significance of bridge deterioration factors as identified by the literature and subject matter experts at the Nebraska State DOT. From our results, we found that bridge material type has the highest association in comparison to other factors such as average daily traffic, average daily truck traffic, structure length, maintainer, region, and precipitation. This research used all NBI inspection records and precipitation rates from all US counties.
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