Application of couple sparse coding in smart damage detection of truss bridges

Abstract

Damage detection of bridge structures plays a crucial role in in-time maintenance of such structures, which subsequently prevents further propagation of the damage, and likely collapse of the structure. Currently, the application of machine learning algorithms are growing in smart damage detection of structures. This work focuses on application of a new machine learning algorithm to identify the location and severity of damage in truss bridges. Frequency Response Functions (FRFs) are used as damage features, and are compressed using Principal Component Analysis (PCA). Couple Sparse Coding (CSC) is adopted as a classification method to learn the relationship between the bridge damage features and its damage states. Two truss bridges are used to test the proposed method and determine its accuracy in damage detection of truss bridges. It is found that the proposed method provides a reliable detection of damage location and severity in truss bridges.

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ORCID for Mohammad Kashani: orcid.org/0000-0003-0008-0007

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