Early damage detection by an innovative unsupervised learning method based on kernel null space and peak-over-threshold

This article proposes an innovative unsupervised learning method for early damage detection and long-term structural health monitoring of civil structures under environmental variability. This method consists of three main parts including a novelty detector based on kernel null Foley-Sammon transform (KNFST), a practical approach to choosing an optimal Gaussian kernel parameter, and a probabilistic method for the threshold estimation. The crux of KNFST is to map all original samples to a kernel feature space and project the kernelized features into a single point in a null space. The proposed threshold estimation method exploits the extreme value theory, the generalized Pareto distribution, and the peak-over-threshold. The major contribution of this article is to propose an innovative novelty detection method by a one-class kernel null space algorithm and a probabilistic threshold estimation approach. Dealing with the problem of environmental variations and estimating a reliable alarming threshold are the main advantages of the proposed method. The effectiveness and reliability of the proposed method are validated by the Wooden Bridge in a laboratory environment and the full-scale Z24 Bridge. Results demonstrate that the proposed unsupervised learning method highly succeeds in detecting damage even under strong environmental variations.