Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Abstract

Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion. Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state. In this paper, the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features. These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine, an unsupervised classifier generating a decision function using only patterns belonging to this baseline state. Structural damage, once detected by the trained machine, a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage. The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated. Subsequently, vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.

Graphical abstract

Keywords

structural health monitoring / damage localization / auto-regressive with exogenous input models / one-class support vector machine / reinforced concrete frame

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Funding note

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK).

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Competing interests

The authors declare that they have no competing interests.

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2024 The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn