United States Department of Transportation
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Smart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges
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2022-10-01
[PDF-941.92 KB]
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Edition:Final Report 01/20/2020 – 07/31/2022
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Abstract:Mobile sensing is an alternative paradigm that offers numerous advantages compared to the conventional stationary sensor networks. Mobile sensors have low setup costs, collect spatial information efficiently, and require no dedicated sensors to any particular structure. Most importantly, they can capture comprehensive spatial information using few sensors. The advantages of mobile sensing combined with the ubiquity of smartphones with the internet of things (IoT) connectivity have motivated researchers to think of cars+smart phones as large-scale sensor networks that can contribute to the health assessment of structures. Working with mobile sensors has several challenges. The signals collected within a vehicle’s cabin are contaminated by the vehicle suspension dynamics; therefore, the extraction of bridge vibration from signals collected within a vehicle is not an easy task. Additionally, mobile sensors simultaneously measure vibration data in time while scanning over a large set of points in space, which creates a different data structure compared with fixed sensors. Since collected data are mixed in time and space, they contain spatial discontinuities. When these challenges are addressed, mobile sensing is a promising data resource enabling crowdsourcing and an opportunity to extract information about infrastructure conditions at an unprecedented rate and resolution. In this regard, deep learning-based frameworks have been developed in this project to (a) resolve the dynamic behavior of a vehicle by estimating input forces to which it is subjected from responses acquired from within a vehicle and (b) learn underlying partial differential equations governing the underlying dynamics of a system from recorded data.
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