An evolutionary vehicle scanning method for bridges based on time series segmentation and change point detection

• Published in: Mechanical systems and signal processing Vol. 210; p. 111173
• Main Author: Alamdari, M. Makki
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-03-2024
• Subjects: Damage; Distance; Drive-by bridge inspection; Indirect structural health monitoring; Matrix profile; Time series data; Drive-by bridge inspection; Matrix profile; Damage; Indirect structural health monitoring; Distance; Time series data
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2024.111173

In this paper, the problem of bridge health monitoring is formulated as an unsupervised semantic segmentation where the aim is to identify the onset of structural damage by detecting unexpected irregularities in the time series data. In particular, the main objective of this study is to approach this problem through an indirect sensing framework based on the concept of vehicle-mounted scanning. Thus, the sole source of information is the acceleration responses of the moving vehicle passing over the bridge. This is achieved through automatic feature learning by adopting the concept of matrix profile to conduct an all-pair-similarity search for a given time series, where the location of the contextual change is determined by the corresponding Corrected Arc Crossings (CAC) profile. The performance of the method is first evaluated through numerical investigations for different damage cases where the effect of operational uncertainties such as road roughness, and variability in vehicle properties are considered. Further, the method is experimentally evaluated using a lab-scale bridge model with different damage cases. Through these investigations, it is consistently observed that the location of the change point always coincides with the minimum point of the CAC profile. The proposed framework has three major advantages making it more appealing compared to the competing techniques. First, it has been fully formulated in the context of unsupervised learning where no access to labeled data is required. Second, it is capable of working with a limited amount of data yet providing promising results. Finally, it can be easily applied to online settings where data is streaming in real-time. These successful results demonstrate the potential of the proposed method as a cost-effective and rigorous bridge inspection tool and open up opportunities in the field of indirect bridge health monitoring.