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 language | English |
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Number of pages | 8 |
Journal | The e-Journal of Nondestructive Testing & Ultrasonics |
Volume | 2024 |
Issue number | 07 |
DOIs | |
Publication status | Published - Jul 2024 |
MoE publication type | A4 Conference publication |
Event | European Workshop on Structural Health Monitoring - Potsdam, Germany Duration: 10 Jun 2024 → 13 Jun 2024 Conference number: 11 |
Keywords
- Automation
- Crowdsensing
- Deep learning
- Drive-by method
- Structural health monitoring
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i3 – Industry Innovation Infrastructure
Sainio, P. (Manager)
School of EngineeringFacility/equipment: Facility
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Solid Mechanics Laboratory (i3)
Lehto, P. (Manager)
Department of Mechanical EngineeringFacility/equipment: Facility