Footbridge damage detection using smartphone-recorded responses of micromobility and convolutional neural networks
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Journal Title
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Author
Date
2024-07-02
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Language
en
Pages
16
Series
Automation in Construction, Volume 166, pp. 1-16
Abstract
This paper presents a footbridge damage detection and classification framework using smartphone-recorded responses of micromobility and deep learning techniques. Time–frequency representations (TFRs) of scooter vibrations are employed to detect and classify footbridge damage severities using a Two-Dimensional Convolutional Neural Network (2D CNN). A One-Dimensional (1D) CNN using scooter frequency spectra was also investigated for comparison. The effectiveness of the method was verified using a numerical model of scooter-footbridge interactions and field tests on a real footbridge. The results indicated that both CNNs were sensitive to footbridge frequency alterations caused by damage in the numerical simulations. Nevertheless, the performance of the 1D CNN experienced a substantial decline in field tests involving stochastic influencing factors, whereas the accuracy of damage classification using the 2D CNN remained high. Finally, reasonable interpretations for the superior performance of the 2D CNN are provided using Shapley Additive Explanations (SHAP) values.Description
Keywords
Convolutional neural networks, Scooters, Smartphones, Structural health monitoring, Vehicle–bridge interaction
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Citation
Li, Z, Lan, Y & Lin, W 2024, ' Footbridge damage detection using smartphone-recorded responses of micromobility and convolutional neural networks ', Automation in Construction, vol. 166, 105587, pp. 1-16 . https://doi.org/10.1016/j.autcon.2024.105587
