## 1 Introduction
[PERSON] and [PERSON] [13] used an automated OC-SVM to locate damage locations in a three-story, two-bay steel structure in the laboratory. [PERSON] and [PERSON] [14] integrated OC-SVM with an auto-encoder to detect structural damage in laboratory steel bridges. Only the data from the entire structure is used in their research. The auto-encoders loss error is used as a damage sensitivity factor in training. The proposed method can detect slight damage and has a high detection rate. In addition, cluster-based unsupervised algorithms for structural damage detection are commonly used. [PERSON] et al. [15] suggested an unsupervised structural damage assessment technique based on the K-nearest neighbor algorithm to detect bridge damage. We must manually pre-define the number of clusters K for data clustering in the K-nearest neighbor approach. However, when dealing with high-dimensional and massive training data from various unknown conditions, determining an acceptable value for K can be difficult, and the clustering results are prone to local optimality. [PERSON] et al. [16] suggested an unsupervised learning method that uses a fast-clustering strategy based on density peaks for civil infrastructure damage detection to address the issues raised by conventional clustering-based methods. In structural damage detection, it is crucial to choose appropriate metrics. Throughout the years, researchers have used many different damage parameters as indicators for judging structural damage status, such as natural frequencies, damping, frequency response function, modal strain energy, etc. Different damage parameters have different application scenarios. The success of structural damage detection algorithms largely depends on the selection of damage parameters, because the estimation of the threshold often has a great relationship with the damage indicators. [PERSON] and [PERSON] [17] reviewed extensive literature and recommended that modal curvature-based algorithms are more reliable in detecting damage than frequency and modal-based algorithms. [PERSON] et al. [18] concluded that damping is a very effective indicator of damage, especially when combined with frequency or mode shape, provided that appropriate damping measurement techniques can be used. [PERSON] et al. [19] used modal strain energy (DIMSE), frequency response function strain energy dissipation ratio (FRFSEDR), flexibility strain energy damage ratio (FSEDR) and residual force-based damage index (RFBDI) as damage indexes; then, they applied Bayesian data fusion approach to these four damage indexes to find out the accurate damage location. In addition to using damage parameters as metrics, other factors are often used as metrics in unsupervised structural damage methods. [PERSON] et al. [20] used the learned feature representations in the hidden layers of the deep auto-encoders as damage sensitive features to indicate the presence of damage in the monitored structures. [PERSON] and [PERSON] [14] used three indexes quantifying the reconstruction losses between the original acceleration response inputs and the reconstructed acceleration response outputs as damage-sensitive features. According to our findings, the data employed in many unsupervised structural damage detection algorithms tends to be undamaged. On the other hand, building constructions are frequently exposed to the natural environment, resulting in differing degrees of damage. It is not easy to ensure that the data obtained is entirely undamaged. We also noticed that the selection of suitable damage indicators has the some limitations: (1) When selecting damage parameters as damage indicators, researchers are required to have relevant background knowledge; (2) damage indicators for most unsupervised structural damage detection methods always solely rely on pre-defined distance metrics or reconstruction errors, and thereby cannot be flexibly embedded into other structural damage detection models.