Abstract
Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors.
Highlights
All civil structures degrade over time, and many experience harsh environmental and/or excessive operational stress
We evaluated the performance of our method using the data collected from one of our Structural Health Monitoring (SHM) deployments on a cable-stayed bridge in operation in Western Sydney, and from one of our laboratory-based experiments on a replica of an Sydney Harbour Bridge (SHB)
When new data from multiple sensors arrive at time tn+1, the incremental tensor update step transforms them into an equivalent tensor-based time component, which is presented to the one class support vector machine (OCSVM) model for damage detection
Summary
All civil structures degrade over time, and many experience harsh environmental and/or excessive operational stress. For most structures such as bridges, the current monitoring practice relies on visual engineering inspections. They use simple tests, which are are expensive, time-consuming, qualitative, often subjective, and only capable of assessing suspicious problems. Structural Health Monitoring (SHM) systems provide a quantitative, objective, and less expensive alternative to continuously monitor these ageing infrastructures. SHM systems tightly integrate sensor-based data collection, complex data analysis algorithms, and intuitive information presentation software to allow managers and engineers to make informed decisions on a structure’s maintenance and damage mitigation. SHM may provide early damage detection, ongoing condition assessment, Sensors 2018, 18, 111; doi:10.3390/s18010111 www.mdpi.com/journal/sensors
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