Crowdsensing-based automatic bridge health condition assessment using drive-by measurements and deep learning
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A4 Artikkeli konferenssijulkaisussa
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2024-07
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Language
en
Pages
8
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The e-Journal of Nondestructive Testing & Ultrasonics, Volume 2024, issue 07
Abstract
In recent decades, assessing the structural health conditions of aging bridges has emerged as a significant concern. A recent drive-by measurement method has attracted substantial attention, in which only several sensors are installed on crowdsensing vehicles rather than bridges, providing a more economical and convenient solution. This paper proposes an automatic bridge condition assessment framework incorporating drive-by measurements and deep learning techniques. The methodology involves collecting and segmenting accelerations from a vehicle passing a healthy bridge into short-time overlapped frames. Over multiple vehicular passes, all frames are then transformed into frequency-domain responses, forming the input for training an unsupervised deep learning model. The model is then trained to reconstruct the input using these frequency-domain responses. In assessing the bridge with an unknown health state, the trained model is employed to reconstruct the passing vehicle's new short-time frames, and the response construction error automatically determines the bridge's health condition. Experimental validation utilizing a laboratory bridge and scaled truck demonstrated that the trained model could consistently identify a healthy bridge during passages, with larger reconstruction errors indicating that the bridge was damaged. The innovative framework showcased promise for efficient and reliable bridge health condition assessment.Description
Keywords
Automation, Crowdsensing, Deep learning, Drive-by method, Structural health monitoring
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Citation
Li, Z, Lan, Y & Lin, W 2024, ' Crowdsensing-based automatic bridge health condition assessment using drive-by measurements and deep learning ', The e-Journal of Nondestructive Testing & Ultrasonics, vol. 2024, no. 07 . https://doi.org/10.58286/29775