Real-time fatigue health monitoring has the potential to serve as a valuable complement to structural health monitoring (SHM) for bridge inspections. SHM is an objective supplement to visual bridge inspections with a minimum interval between bridge inspections at 24 months. SHM can provide quantitative and objective data on a bridge’s fatigue condition for fracture-critical components, of which fatigue is a criterion. Current methods of continuous structural health monitoring for condition assessment are performed by collecting measured bridge response subjected to operational traffic from an array of sensors installed on fracture-critical members of a bridge. The measured responses are used to determine the remaining fatigue life of the bridge—the minimum time before repair. The large amount of data involved in this process complicates the design of a system that will automate the data collection process at a bridge, analyze that data, and display information about bridge health to researchers and engineers. Variations in bridge designs and condition assessment algorithms also necessitate that such a system be modular and adaptable to allow for expansion to additional structures. A new system has been developed that separates bridge SHM from the data storage and communication system. This architecture creates a reliable interface for sending data from one or more bridges to a cloud server where it can be processed using modular algorithms that can be adapted for different use cases. The cloud-based web service and data repository makes bridge structural health data available to researchers at all steps of the process. This system provides significant advantages over previous platforms for structural health monitoring and condition assessment, most notably in the areas of modularity, extensibility, and reliability.
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Networkless Wireless Sensing for Bridge Structural Health Monitor
The recent report by American Society of Civil Engineers gave the nation's bridges an unimpressive C grade. Across the country, more than 617,000 highway bridges: 46,154 structurally deficient and 42% 50+ years old. Continuous bridge assessment is essential to protect public safety. Federal Highway Administration requires all highway bridges inspected once every 24 months. However, any drastic change on bridges within 24 months will be left undetected. Nonetheless, bridge inspection is time-consuming and labor-intensive. Civil engineers have been using bridge health monitoring (BHM) systems with wired and/or wireless sensors to measure structural response (e.g., displacement, strain, acceleration) of a bridge. The response measurements are then converted to the information related to structural health for assessment.
State-of-the-art BHM technology deploys sensor networks to facilitate data connection. Installing cables is expensive and subject to extreme weather. Wireless solutions face challenges such as energy consumption. Sensors are battery-powered. Another not well-publicized problem is security threats inherited in wireless networks.
Our approach to wireless BHM is to utilize sensors networkless by collecting data with a drone. Similar to a mail carrier who goes around and picks up the mail, a drone collects data from sensors throughout the bridge. A drone eliminates restrictions for civil engineers on node placement since the drone replaces sink nodes. Networkless makes BHM less prone to attacks such as Jamming and DoS. To secure access, we deploy a Needham-Schroeder authentication protocol for the drone to collect data from sensor nodes securely.
Networkless sensing for BHM benefits energy efficiency. It saves battery life as the sensor nodes remain asleep until scheduled transmission or woken up by a drone. It reduces design complexity and operation energy. The system also assures security since there is no vulnerable network to be attacked.
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- Award ID(s):
- 2105718
- NSF-PAR ID:
- 10426279
- Date Published:
- Journal Name:
- IEEE/MIT Undergraduate Research Technology Conference (URTC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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