Vibration serviceability under crowd-induced loading has become a key design criterion for footbridges. Although increased research efforts are put into the characterization of crowd-induced loading, including related interaction phenomena, and first-generation design guides are available, a major challenge lies in the further development and validation of prediction models for crowd-induced vibrations. Full-scale benchmark datasets that simultaneously register structural and crowd motion make an invaluable contribution to meeting this need by providing detailed information on representative operational loading and response data. Currently available datasets either (1) involve a (too) small number of pedestrians or (2) do not involve the simultaneous registration of pedestrian and bridge motion, or else they involve a footbridge (3) where only a single mode or a very limited number of modes are sensitive to walking excitation, (4) for which no suitable digital twin is available, or (5) that is not open access. This paper therefore presents a new and publicly available full-scale dataset collected specifically for the further development and validation of models for crowd-induced loading. The dataset is collected for a real footbridge, with a number of modes that are sensitive to pedestrian-induced vibrations, and with a digital twin available. The pedestrian and bridge motions are registered simultaneously using wireless triaxial accelerometers and video cameras. In addition to two data blocks involving purely ambient excitation, four data blocks are collected for two pedestrian densities, 0.25 and 0.50 persons/m 2 , representing a total of more than 1 h of data for each pedestrian density. Analysis of the structural response shows that the different data blocks can be considered representative for the involved load case. The identified distribution of step frequencies in the crowd indicates a significant contribution of (near-)resonant loading for a number of modes of the footbridge. Furthermore, the dataset displays clear signs of human-structure interaction, suggesting a significant increase in effective modal damping ratios due to the presence of the crowd.

Obtaining pedestrian trajectories by a vision-based methodology is receiving increasing attention in the literature over recent decades. Within the field of study of human-induced vibrations on footbridges, practical challenges arise when collecting the trajectories of high-density crowds during measurement campaigns. A cheap and robust methodology tackling these issues is presented and applied on a case study consisting of a real-life footbridge occupied with many pedestrians. A static camera setup consisting of low-cost action cameras with limited installation height is used. In addition, a drone camera was employed to collect a limited amount of footage. Pedestrians are equipped with colored hats and detected using a straightforward color-segmenting approach. The measurements are subjected to both systematic and random measurement errors. The influence of the former is theoretically investigated and is found to be of limited importance. The effect of the latter is minimized using a Kalman filter and smoother. A thorough assessment of the accuracy results reveals that the remaining uncertainty is in the order of magnitude of 2 to 3 cm, which is largely sufficient for the envisaged purpose. Although the methodology is applied on a specific case study in the present work, the conclusions regarding the obtained accuracy and employability are generic since the measurement setup can be extended to a footbridge with virtually any length. Moreover, the empirically obtained results of the presented case study should find use in the calibration of pedestrian dynamic models that describe the flow of high-density crowds on footbridges and the further development of load models describing crowd-induced loading.

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