Crowdsensing-based automatic bridge health condition assessment using drive-by measurements and deep learning

Zhenkun Li*, Yifu Lan, Weiwei Lin

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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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.
Original languageEnglish
Number of pages8
JournalThe e-Journal of Nondestructive Testing & Ultrasonics
Volume2024
Issue number07
DOIs
Publication statusPublished - Jul 2024
MoE publication typeA4 Conference publication
EventEuropean Workshop on Structural Health Monitoring - Potsdam, Germany
Duration: 10 Jun 202413 Jun 2024
Conference number: 11

Keywords

  • Automation
  • Crowdsensing
  • Deep learning
  • Drive-by method
  • Structural health monitoring

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