Next Article in Journal
Construction of the Guide Star Catalog for Double Fine Guidance Sensors Based on SSBK Clustering
Previous Article in Journal
Beam Wander Restrained by Nonlinearity of Femtosecond Laser Filament in Air
 
 
Article

Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers

1
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Ashby Building, Stranmillis Road, Belfast BT9 5AG, UK
2
School of Natural and Built Environment, Queen’s University Belfast, David Keir Building, Stranmillis Road, Belfast BT9 5AG, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Filippo Ubertini
Sensors 2022, 22(13), 4994; https://doi.org/10.3390/s22134994
Received: 30 May 2022 / Revised: 24 June 2022 / Accepted: 28 June 2022 / Published: 2 July 2022
(This article belongs to the Section Fault Diagnosis & Sensors)
Structural Health Monitoring (SHM) is critical in the observation and analysis of our national infrastructure of bridges. Due to the ease of measuring bridge rotation, bridge SHM using rotation measurements is becoming more popular, as even a single DC accelerometer placed at each end of span can accurately capture bridge deformations. Event detection methods for SHM typically entail additional instrumentation, such as strain gauges or continuously recording video cameras, and thus the additional cost limits their utility in resource-constrained environments and for wider deployment. Herein, we present a more cost-effective event detection method which exploits the existing bridge rotation instrumentation (tri-axial MEMS accelerometers) to also act as a trigger for subsequent stages of the SHM system and thus obviates the need for additional vehicle detection equipment. We show how the generalised variance over a short sliding window can be used to robustly discriminate individual vehicle loading events, both in time and magnitude, from raw acceleration data. Numerical simulation results examine the operation of the event detector under varying operating conditions, including vehicle types and sensor locations. The method’s application is demonstrated for two case studies involving in-service bridges experiencing live free-flow traffic. An initial implementation on a Raspberry Pi Zero 2 shows that the proposed functionality can be realised in less than 400 ARM A32 instructions with a latency of 47 microseconds. View Full-Text
Keywords: event detection; bridge structural health monitoring; rotation measurement; generalised variance event detection; bridge structural health monitoring; rotation measurement; generalised variance
Show Figures

Figure 1

MDPI and ACS Style

Ferguson, A.J.; Woods, R.; Hester, D. Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers. Sensors 2022, 22, 4994. https://doi.org/10.3390/s22134994

AMA Style

Ferguson AJ, Woods R, Hester D. Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers. Sensors. 2022; 22(13):4994. https://doi.org/10.3390/s22134994

Chicago/Turabian Style

Ferguson, Alan J., Roger Woods, and David Hester. 2022. "Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers" Sensors 22, no. 13: 4994. https://doi.org/10.3390/s22134994

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop