Michigan to test 2,000 sensors on Mackinac Bridge

By Folake Dosu  |  March 15, 2019

iot-sensors-mackinac-bridge

Michigan’s iconic Mackinac Bridge is getting an IoT upgrade from researchers at Michigan State University and Washington University in St. Louis this summer, according to the state’s Department of Transportation, mlive.com reports.

These researchers will install about 2,000 small sensors that will not only explore “logistics of large-scale deployment,” but also offer “useful monitoring data” to the bridge authority, says the state agency.

“The successful large-scale deployment of this novel low-cost sensing technology will dramatically transform the economics of bridge preservation/management and ultimately improve the serviceability of bridges.”

“The successful large-scale deployment of this novel low-cost sensing technology will dramatically transform the economics of bridge preservation/management and ultimately improve the serviceability of bridges,” Nizar Lajnef, Michigan State University associate professor of civil and environmental engineering, said in an MDOT news release.

“We also will explore how the collected data could be used for improved cost-effective, condition-based maintenance of the Mackinac Bridge structural components. We are very excited that this will be the first fully instrumented bridge in the country using advanced wireless and self-powered monitoring technology.”

Assisting with the installation will be staff with the Mackinac Bridge Authority, whose organization will also grant access to the bridge and provide equipment. The bridge authority will own the data gathered by the sensors, although data will be made available to researchers for publication pending approval from the MBA, according to the outlet.

“In addition to being a statewide need, the development of effective methods for preserving our transportation infrastructure systems is a critical national need,” Lajnef explained in the release.

“Through this large-scale deployment, we would show that the system can autonomously monitor the loading experienced by the bridge components, and that the information from the sensors can be collected without significant human intervention and at significantly low cost.”

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