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

Abstract

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.

The Great Wall Vibration Monitoring of Traffic and Natural Hazards Using MEMS Accelerometers

“…The analysis of wind-induced vibrations on HVTL conductors by Zanelli et al using a MEMS accelerometer showed that different wind-induced phenomena can be detected by the MEMS accelerometer [ 14 ]. Ferguson et al proposed a vehicle detection method based on the generalised variance computed over a sliding window using a triaxial MEMS accelerometer [ 24 ]. However, it suffers from a deficiency in the high-frequency noise component, which was subsequently found to be insufficient for our purposes.…”