Mr. Kevin A. Endsley Profile

Kevin A. Endsley

at Michigan Tech Research Institute

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Proceedings Article | 6 April 2012 Paper

Decision support system for integrating remote sensing in bridge condition assessment and preservation

Arthur Endsley, Colin Brooks, Devin Harris, Tess Ahlborn, Khatereh Vaghefi

Proceedings Volume 8345, 834548 (2012) https://doi.org/10.1117/12.915640

KEYWORDS: Bridges, Remote sensing, Decision support systems, Inspection, Thermography, Photography, Structural health monitoring, Radar, Databases, Sensors

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Since the National Bridge Inventory (NBI) was first conducted, structural health monitoring (SHM) of U.S. bridge infrastructure has consisted largely of time and labor-intensive surveys with subjective results. In-situ and embedded sensors, while more reliable and accurate, can be costly and in many cases infeasible for SHM because they require installation in hard-to-reach places or during construction. Remote sensing (RS) technologies such as radar, electrooptical imaging and laser scanning may offer an innovative, cost-effective method of monitoring the dynamic conditions of U.S. bridges in real-time. While some RS techniques may be costly for state agencies to deploy on their own, RS imagery is available through government agencies or commercial vendors for moderate or no cost. How can disparate RS datasets be integrated with one another and with inventory data in a way that is meaningful to bridge asset management decision makers? This paper discusses the development and functionality of the Bridge Condition Decision Support System (DSS), a web-based asset management tool for bridge managers and inspectors. The DSS seamlessly merges bridge metrics from RS data with NBI inventory data allowing decision makers to compare up-to-date bridge condition metrics from multiple inputs as a time series. It enables analysis of RS and inventory data available through user-friendly web services which can also expose virtually unlimited server-side data processing. Using open-source software, the authors developed a scalable, spatially-aware bridge condition database with a fast and flexible server application programming interface (API) and a cross-browser compatible web mapping application written in Javascript.

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