Version 1
: Received: 10 August 2024 / Approved: 12 August 2024 / Online: 13 August 2024 (17:08:19 CEST)
How to cite:
Lawal, O.; V.Shajihan, S. A.; Mechitov, K.; Spencer Jr., B. F. Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection. Preprints2024, 2024080862. https://doi.org/10.20944/preprints202408.0862.v1
Lawal, O.; V.Shajihan, S. A.; Mechitov, K.; Spencer Jr., B. F. Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection. Preprints 2024, 2024080862. https://doi.org/10.20944/preprints202408.0862.v1
Lawal, O.; V.Shajihan, S. A.; Mechitov, K.; Spencer Jr., B. F. Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection. Preprints2024, 2024080862. https://doi.org/10.20944/preprints202408.0862.v1
APA Style
Lawal, O., V.Shajihan, S. A., Mechitov, K., & Spencer Jr., B. F. (2024). Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection. Preprints. https://doi.org/10.20944/preprints202408.0862.v1
Chicago/Turabian Style
Lawal, O., Kirill Mechitov and Billie F. Spencer Jr.. 2024 "Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection" Preprints. https://doi.org/10.20944/preprints202408.0862.v1
Abstract
Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
