Paper
27 March 2015 Mitigating the effects of variable speed on drive-by infrastructure monitoring
Andrew Thorsen, George Lederman, Yoshinobu Oshima, Jacobo Bielak, Hae Young Noh
Author Affiliations +
Abstract
Vehicle-based monitoring has the potential to become an accurate and cost-efficient way to monitor infrastructure assets, but a number of challenges must be addressed for such a technique to be implemented widely. The majority of vehicle-based infrastructure sensing has assumed that the vehicle’s speed profile is identical every time it passes over the asset of interest. Ultimately, however this technology will be most practical if damage detection schemes can be applied regardless of the speed of the vehicle. Thus methods must be designed to handle speed variability to make this method more practical. In this paper we investigate the effects of variable speed when monitoring infrastructure from the dynamic response of a passing vehicle, which we measure by placing accelerometers on the vehicle of interest. We have conducted a series of laboratory tests to study this phenomenon, in which a vehicle crosses over a scaled model bridge structure with a varying speed profile. We quantify the ability of several features to detect changes in the infrastructure, independent of the variable speed. We show that aligning signals to normalize for speed variability improves the classification results. This work brings us closer to the ultimate goal of using vehicle-based monitoring to ensure more efficient and more reliable infrastructure in the future.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew Thorsen, George Lederman, Yoshinobu Oshima, Jacobo Bielak, and Hae Young Noh "Mitigating the effects of variable speed on drive-by infrastructure monitoring", Proc. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, 943509 (27 March 2015); https://doi.org/10.1117/12.2084435
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KEYWORDS
Sensors

Bridges

Mass spectrometry

Structural health monitoring

Civil engineering

Data modeling

Environmental monitoring

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