Smartphone and Smartwatch Crowdsensing for Bridge Modal Identification with Convergence Behavior and Bootstrap Uncertainty Analysis

This study investigates the feasibility, accuracy, and data-sufficiency requirements of smartphone- and smartwatch-based crowdsensing for pedestrian bridge modal identification under real-world conditions. Full-scale experiments were conducted on a bridge across two crowdsensing scenarios with varying dynamic excitation intensities by six pedestrians performing walking, running, and bicycling activities while carrying smartphones and wearing smartwatches. Triaxial acceleration data were collected over 300 s and processed using a framework comprising preprocessing, modal estimation, growing-window convergence analysis, and block-bootstrap uncertainty quantification. Using the full dataset, both devices reliably identified the four consistently detectable bridge modes with average errors of approximately 3% across the scenarios relative to the benchmark. In the convergence analysis, smartwatches consistently produced narrower confidence intervals and more stable early-window estimates, which may be related to their more constrained wearing condition and reduced incidental motion compared to pocket-carried smartphones. Higher pedestrian excitation with additional pedestrians running accelerated the convergence, reducing the required data duration and number of pedestrian passes, albeit with increased uncertainty. The study established data-sufficiency thresholds, showing that reliable modal estimates require in the range of 5–17 walking or running passes, while bicycling passes range from 14 to 28, depending on bridge excitation level and device type. Results demonstrate that commodity smartphones and smartwatches are viable, scalable, and cost-effective platforms for crowdsensed bridge modal identification, provided that uncertainty ranges are properly accounted for and sufficient passes across different pedestrian activities are collected to achieve the desired accuracy.