Traffic load monitoring and structural health monitoring (SHM) have been gaining increasing attention over the last decade. However, most of the current installations treat the two monitoring types as separated problems, thereby using dedicated installed sensors, such as smart cameras for traffic load or accelerometers for Structural Health Monitoring (SHM). This paper presents a new framework aimed at leveraging the data collected by a SHM system for a second use, namely, monitoring vehicles passing on the structure being monitored (a viaduct). Our framework first processes the raw three-axial acceleration signals through a series of transformations and extracts its energy. Then, an anomaly detection algorithm is used to detect peaks from 90 installed sensors, and a linear regression together with a simple threshold filters out false detection by estimating the speed of the vehicles. Initial results in conditions of moderate traffic load are promising, demonstrating the detection of vehicles and realistic characterization of their speed. Moreover, a k-means clustering analysis distinguishes two groups of peaks with statistically different features such as amplitude and damping duration that could be likely associated with heavy vehicles and cars, respectively.

Enhancing structural health monitoring with vehicle identification and tracking

Burrello A.;Benini L.
2020

Abstract

Traffic load monitoring and structural health monitoring (SHM) have been gaining increasing attention over the last decade. However, most of the current installations treat the two monitoring types as separated problems, thereby using dedicated installed sensors, such as smart cameras for traffic load or accelerometers for Structural Health Monitoring (SHM). This paper presents a new framework aimed at leveraging the data collected by a SHM system for a second use, namely, monitoring vehicles passing on the structure being monitored (a viaduct). Our framework first processes the raw three-axial acceleration signals through a series of transformations and extracts its energy. Then, an anomaly detection algorithm is used to detect peaks from 90 installed sensors, and a linear regression together with a simple threshold filters out false detection by estimating the speed of the vehicles. Initial results in conditions of moderate traffic load are promising, demonstrating the detection of vehicles and realistic characterization of their speed. Moreover, a k-means clustering analysis distinguishes two groups of peaks with statistically different features such as amplitude and damping duration that could be likely associated with heavy vehicles and cars, respectively.
2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
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Burrello A.; Brunelli D.; Malavisi M.; Benini L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/795295
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