Damage detection of in-service steel railway bridges using a fine k-nearest neighbor machine learning classifier

Abstract

Minor areas of surface corrosion in steel railway bridges can grow progressively andlead to localized section losses and structural failure over time. This paper proposes a novel combined damage detection approach for the classification of various extents and degrees of cross section losses due to damages like corrosion using a k-Nearest Neighbor (kNN) machine learning classifier. A Finite Element (FE) model of an in-service railway bridge is developed and validated using vibration data from field testing and these combined FE-field data are trained and tested to classify various corrosion cases following the Australian Standard AS7636. The results show that the proposed technique is practical and highly accurate in classifying damages in steel railway bridges, even if a minor level of steel corrosion is intended to be classified. Furthermore, a comparison between the accuracies of kNN classifier and the Radial Basis Function (RBF) Gaussian kernel Support Vector Machine (SVM) is presented.