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Health Assessment and Risk Mitigation of Railroad Networks Exposed to Natural Hazards using Commercial Remote Sensing and Spatial Information Technologies

The overarching goal of this United States Department of Transportation (USDOT) project is to integrate data from three commercial remote sensing and spatial information (CRS&SI) technologies to create a novel data-driven decision making framework that empowers the railroad industry to monitor, assess, and manage the risks associated with their aging bridge inventories. First, automated wireless sensing technology provides a cost-effective means of remotely monitoring the capacity of operational rail bridges in a network. Second, wheel impact load detection (WILD) data provides a measure of load demand in the form of axle weights in the rail network. Third, global position system (GPS) data of train movement in combination with the WILD data provides a spatial mapping of the movement of live load within the rail network. A data-to-decision (D2D) framework will be established to automate the processing of the CRS&SI data to convert data to actionable information that empowers risk-based decision-making. Within the D2D framework is the use of reliability methods to rationally combine structural capacity and demand information to calculate reliability indices for critical bridge components monitored. Lower limit states are established on the reliability indices to provide a quantitative basis for quantitatively assessing the condition ratings of a bridge span. When adding the consequences associated with bridge performance during natural hazard events (e.g., flooding, earthquakes), the data-driven decision making framework provides bridge engineers a powerful risk assessment strategy. The team is partnering with Union Pacific (UP) who will support the installation of wireless monitoring systems to a select number of bridges exposed to natural hazards; UP will also provide access to WILD and GPS data of their trains.