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Sensors 2018, 18(1), 111;

A Tensor-Based Structural Damage Identification and Severity Assessment

Faculty of Engineering and IT, The University of Sydney, Sydney, NSW 2006, Australia
School of Civil and Environmental Engineering, Universiry of New South Wales, Sydney, NSW 2052, Australia
Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), 13 Garden Street, Eveleigh, NSW 2015, Australia
Author to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 16 December 2017 / Accepted: 19 December 2017 / Published: 2 January 2018
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
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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. View Full-Text
Keywords: tensor analysis; damage identification; damage severity assessment; structural health monitoring (SHM) tensor analysis; damage identification; damage severity assessment; structural health monitoring (SHM)

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Anaissi, A.; Makki Alamdari, M.; Rakotoarivelo, T.; Khoa, N.L.D. A Tensor-Based Structural Damage Identification and Severity Assessment. Sensors 2018, 18, 111.

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