Experimental analysis compares robot and portable SHM systems for identifying bridge modal frequencies, suggesting practical implications for network monitoring.
Bridge infrastructures lack affordable network-wide monitoring, as fixed S HM is typically limited to landmark spans. Drive-by monitoring with mobile robots offers scalable coverage using vehicles as moving sensor platforms. This paper experimentally evaluates a frequency domain subtraction algorithm on vibration data collected by an unmanned ground vehicle (UGV), validated against a portable SHM system on the UCF footbridge and the Alafaya Trail Bridge. The paired on-bridge and adjacent-road vibrations are transformed by FFT, scaled by energy and subtracted to isolate the dynamic response of the bridge and reveal modal peaks. On the footbridge, the UGV consistently recovered four of eight modal frequencies while on the stiffer Alafaya bridge, it identified the fundamental mode within 0.35% of the reference. Though portable SHM yields higher fidelity, it lacks network f easibility. UGV-based monitoring achieves a practical balance of accuracy and consistency. Future work will address uncertainty quantification, advanced modal frequency extraction algorithm and mode-shape extraction.
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Algadi et al. (2025) studied this question.
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