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Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm

Within machine learning, several structural damage detection and localization methods based on clustering and novelty detection methods have been proposed in the recent years in order to monitor mechanical and civil structures. In order to train a machine learning model, an unsupervised mode is preferred because it only requires sufficient normal data from the intact states of a structure for training, and the testing abnormal data from various damage states are generally quite rare. With an unsupervised training mode, the capability of detecting structural damage mainly depends on the identification of abnormal data from the testing data. This identification process is termed unsupervised novelty detection. The premise of unsupervised novelty detection is that a large volume of a normal data set is available first to train a normal model that is established by machine learning algorithms. Then, the trained normal model can be used to identify abnormal data from future testing data. In this article, a new structural damage detection and localization method is proposed using a density peaks-based fast clustering algorithm. In order to realize damage detection, the original density peaks-based fast clustering algorithm is modified to an unsupervised machine learning method by adding training and testing processes. Furthermore, to improve the performance of the proposed method, the Gaussian kernel function of radius is introduced to calculate the local density of data points, and a new damage-sensitive feature using a continuous wavelet transform is also proposed. Damage-sensitive features are extracted from the measured data through sensors installed on a laboratory-scale steel structure. Extensive experimental studies are carried out under various structural damage scenarios in order to validate the performance of the proposed method. The proposed density peaks-based fast clustering method shows satisfactory performance with regard to damage localization under various damage scenarios as compared to a traditional approach.