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

Damage detection in bridge structures plays a crucial role in the 'in-time' maintenance of such structures, which subsequently prevents further propagation of the damage, and likely collapse of the structure. Currently, the applications of machine learning algorithms are growing in the 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 are used as damage features, and are compressed using principal component analysis. Couple sparse coding 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 the detection of damage to truss bridges. It is found that the proposed method provides a reliable detection of damage location and severity in truss bridges.