Hybrid Time-Frequency Analysis for Micromobility-Based Indirect Bridge Health Monitoring

Area of Science:

• Civil Engineering
• Structural Health Monitoring
• Transportation Infrastructure

Background:

• Bridges are critical infrastructure requiring continuous condition monitoring.
• Traditional structural health monitoring (SHM) faces challenges like traffic disruption and sensor limitations.
• Indirect bridge health monitoring (iBHM) offers a non-disruptive alternative using traffic data.

Purpose of the Study:

• To investigate the feasibility of using bicycles as mobile sensing platforms for iBHM.
• To develop and evaluate a novel signal processing method for analyzing bike-based iBHM data.
• To assess the effectiveness of this approach compared to existing methods.

Main Methods:

• Utilized system identification methods to analyze acceleration data from a bicycle-mounted sensor.
• Employed a hybrid Wavelet Packet Transform (WPT) and Synchro-extracting Transform (SET) approach.
• Measured bridge responses under various traversing conditions and rider-bicycle-bridge interactions.

Main Results:

• The WPT-SET hybrid method demonstrated superior robustness and accuracy in identifying dominant non-stationary frequencies.
• The proposed bike-based iBHM approach effectively captured bridge dynamic characteristics.
• Performance was validated against the Time-Varying Filtering Empirical Mode Decomposition (TVF-EMD) algorithm.

Conclusions:

• Bicycles serve as cost-effective, mobile sensing platforms for iBHM.
• The WPT-SET method is a reliable tool for analyzing iBHM data.
• This approach enables structural health monitoring in areas inaccessible to motorized vehicles.