Prediction of human-induced structural vibration using multi-view markerless 3D gait reconstruction and an enhanced bipedal human-structure interaction model

Liang, Huiqi; Lu, Yijing; Xie, Wenbo; He, Yuhang; Wei, Peizi; Zhang, Zhiqiang; Wang, Yuxiao

Abstract

In the context of advancing material engineering and construction technology, structures are evolving to be lightweight, giving rise to a heightened focus on human-induced vibration serviceability. Despite the availability of various Human-Structure Interaction (HSI) models, integrating outdoor tests with these models remains challenging due to the lack of a comprehensive testing framework. Existing methods heavily rely on invasive wearable sensors, lacking non-invasive alternatives. To bridge this gap, this paper proposed an outdoor testing framework for evaluating human-induced structural vibrations. Using a 2D body keypoints detection network, human gaits were captured from multiple viewpoints, representing it with the Skinned Multi-Person Linear Model (SMPL) model through triangulation and optimization. Gait and walking force data from 30 participants were analyzed using a Long Short-Term Memory (LSTM) network to classify landing states, which indicate whether both feet are in contact with the structure. Extending a bipedal HSI model from 1D to a 2D structure, walking tests were conducted on a 19.8 m × 2.35 m outdoor footbridge to update dynamic properties. Results showed over 90 % accuracy in predicting human landing states and within 10 % relative Root Mean Square Error (RMSE) in predicting pedestrian vertical walking force. Comparing models with and without HSI, disparities of 20 % to 60 % in frequency changes and 50 % to 180 % in damping ratio values were observed. The proposed non-invasive method predicted vertical structural vibration response with <10 % error, outperforming cases that used walking loads from force-measuring insoles without accounting for time-varying dynamics. These findings affirmed the feasibility and accuracy of our multi-view, non-invasive human gait acquisition method coupled with the improved bipedal HSI model in human-induced vibration prediction.

Citation

Liang, H., Lu, Y., Xie, W., He, Y., Wei, P., Zhang, Z., & Wang, Y. (2025). Prediction of human-induced structural vibration using multi-view markerless 3D gait reconstruction and an enhanced bipedal human-structure interaction model. Journal of Sound and Vibration, 602, Article 118931. https://doi.org/10.1016/j.jsv.2025.118931

• Journal Article Type: Article
• Acceptance Date: Jan 3, 2025
• Online Publication Date: Jan 22, 2025
• Publication Date: 2025-04
• Deposit Date: Feb 3, 2025
• Journal: Journal of Sound and Vibration
• Print ISSN: 0022-460X
• Electronic ISSN: 1095-8568
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 602
• Article Number: 118931
• DOI: https://doi.org/10.1016/j.jsv.2025.118931
• Public URL: https://durham-repository.worktribe.com/output/3362729