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

Area of Science:

• Civil Engineering
• Structural Health Monitoring
• Sensor Technology

Background:

• Structural Health Monitoring (SHM) is crucial for bridge infrastructure management.
• Bridge rotation measurements using accelerometers offer a cost-effective SHM approach.
• Existing event detection methods often require expensive additional instrumentation.

Purpose of the Study:

• To develop a cost-effective event detection method for bridge SHM.
• To leverage existing tri-axial MEMS accelerometers for vehicle event triggering.
• To reduce the need for specialized vehicle detection equipment in SHM systems.

Main Methods:

• Utilized generalized variance over a short sliding window for event detection.
• Employed raw acceleration data from tri-axial MEMS accelerometers.
• Validated the method through numerical simulations and case studies on in-service bridges.

Main Results:

• Successfully discriminated individual vehicle loading events from acceleration data.
• Demonstrated robust performance under varying vehicle types and sensor locations.
• Achieved low computational requirements ( < 400 ARM A32 instructions) and latency (47 microseconds) on a Raspberry Pi Zero 2.

Conclusions:

• The proposed method offers a cost-effective solution for event detection in bridge SHM.
• Exploiting existing rotation instrumentation simplifies SHM systems and reduces costs.
• The technique is suitable for real-time implementation in resource-constrained environments.