Initial Evaluation of Digital Twin Technology and Internet-of-Things Sensors for the Interstate-90 Homer Hadley Floating Bridge

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The emergence of digital twin technology is set to reshape the management of civil infrastructure by enabling real-time monitoring, predictive maintenance, and data-driven decision-making. Advances in internet-of-things (IoT) sensors, 5G connectivity, and cloud computing allow structural health monitoring systems to collect high-resolution data from diverse sensor types, transmit it in real time, and aggregate it within accessible, cloud-based platforms. These capabilities are particularly valuable for complex structures like floating bridges, which require constant visual inspections and are highly sensitive to dynamic forces and inputs from the environment. This thesis details the design, deployment, and initial evaluation of a “proof-of-technology” digital twin for the Homer M. Hadley (I 90) floating bridge in collaboration with the Washington State Department of Transportation, the University of Washington’s Mobility Innovation Center, and industry partners. The Homer M. Hadley bridge is the only floating bridge in the world that supports light-rail transit, requires many more maintenance and operations decisions than a typical bridge, and has the potential to be uniquely benefitted from the insights that digital twins can provide. A system of IoT sensors was installed to monitor key structural and environmental parameters, including anchor cable tension, pontoon movement, pontoon freeboard, and temperature. Data was transmitted over the 5G cellular network and integrated into a cloud-based digital twin platform. Additional data, for example, lake level, lake water quality, traffic, and weather data from outside sources were also federated into the system. The digital twin was then used to assess bridge behavior in real-world conditions, focusing on anomaly detection capabilities, usefulness of real-time monitoring, and the feasibility of integrating such a system into existing maintenance programs.

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Thesis (Master's)--University of Washington, 2025

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