Health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.
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Health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.

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      Final report : health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.
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      The overarching goal of this project was to integrate data from 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, especially those exposed to natural hazards. Among the CRS&SI technologies explored, wireless sensing was a primary focus. Wireless monitoring systems were designed and deployed to monitor railroad bridges exposed to train and environmental loads. Coupled with structural inspection data, wheel impact load detection (WILD) data, and train location data, the wireless monitoring data was interrogated to assess train loads on bridges, assess the health and reliability of bridges, and quantify the consequences of exceeding performance limit states. A data-to-decision (D2D) framework was established to automate the processing of the CRS&SI data to convert it into actionable information that empowers risk-based decision-making. The team partnered with Union Pacific (UP) Railroad who provided access to operational railroad bridges, data and expertise. The project instrumented the Harahan Bridge and the Parkin Bridge in the New Madrid fault zone for demonstration of the system. This project produced a large number of critical findings that advanced the use of CRS&SI technologies for risk management rail bridges: 1. Two permanent wireless monitoring systems were designed and deployed on the Harahan (Memphis, TN) and Parkin (Parkin, AR) Bridges. These bridges are critical elements of the Memphis sub-junction and were selected due to their exposure to natural (e.g., seismic, aging) and man-made (e.g., vehicular collisions) hazards. The Harahan and Parkin Bridges were instrumented with 36 and 30 wireless sensing channels, respectively, ranging from strain to acceleration measurements. As part of these monitoring systems, a real-time alert service was implemented to provide email alerts to UPRR engineers and inspectors regarding structural responses exceeding pre-defined response thresholds. 2. A relational database was developed to provide a comprehensive data repository to store inspection data, CRS&SI sensor data and analytical models of railroad bridges. Unifying these three forms of data/information empowers more extensive data analysis to assess the health of railroad bridges and aid railroads with risk management of their networks. 3. A reliability framework was created to assess the health of bridge components monitored. The reliability index calculated using the first-order reliability method was found to be an ideal scalar metric for assessing component health. Lower limit states were defined to trigger inspection and maintenance of bridge elements using monitoring data. The reliability index, when coupled with the consequences of exceeding defined limit states, allows owners to assess and manage the risks associated with their bridges and rail networks. The reliability framework was successfully validated on the Harahan Bridge. 4. The loads imposed on short-span rail bridges were quantified using long-term monitoring data. Specifically, the maximum static response and dynamic load factor as a function of train velocity were assessed for the Parkin Bridge. A novel approach to bridge load rating was developed using structural monitoring data; results indicated AREMA-specified load rating procedures are likely conservative for well-maintained rail and bridges. 5. An extensive return-on-investment (ROI) analysis was performed to assess the return rate for railroads that elect to invest in structural monitoring systems. Using the data-driven load rating methodology developed, the revelation of greater bridge capacity allows railroads to carry larger loads at higher train velocities. This ensures the return on investment is positive and reaped rapidly after technology adoption. 6. The research effort also resulted in scholarly publications including four conference proceeding and two journal papers. Members of the research team provided 16 presentations in which aspects of the research effort in advancing CRS&SI technologies were presented to broad set of audiences across the United States. A key objective of the project was the development of a commercialization plan for the CRS&SI technologies developed. The commercialization plan was produced based: market analysis, interviews with prospective technology adopters, and feedback provided by the project Technology Advisory Committee (TAC). The market analysis indicated a robust market both domestically and internationally for data-driven risk management methods based on CRS&SI data. Specifically, Class I and II railroads were identified as prospective customers domestically. However, interviews with technology adopter revealed a strong preference for short-term monitoring due to the perceived high costs associated with placing permanent monitoring systems in the field. Based on this feedback, the team devised a go-to-market strategy centered on short-term monitoring. A rapid-to-deploy commercial solution based on Civionics’ wireless monitoring system and the Prospect Solutions’ Decision Making Toolchest was developed. In partnership with the Transportation Technology Center, Inc. (TTCI) in Pueblo, Colorado, the team validated the commercial system using a short-span steel girder bridge loaded under a controlled loading regime. A wireless monitoring system measuring bridge strain and accelerations was deployed in minimal time and at minimal cost. Monitoring data was used to assess the load rating of the bridge using the data-driven load rating method developed by the team.
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