## 1 Introduction
For electrical equipment, the commonly employed porcelain and composite insulators are susceptive to earthquakes, so their material properties are closely related to the structural safety. It was reported that the material strength of brittle insulation materials generally obeys the normal or Weibull distribution [42] that shows the research necessity. Although some studies investigated the influence of material uncertainty on the seismic fragility of electrical equipment [29], only the material strength was considered, and other material properties such as elastic modulus, Poisson's ratio, and density were still not taken account into. A more comprehensive seismic vulnerability study for electrical equipment is urgently needed. On the other hand, the seismic vulnerability analysis considering a series of parameter uncertainties requires a large amount of computation time, and the analysis results are so complex that they are difficult to be applied in the subsequent engineering evaluations. For example, Monte Carlo simulations can also be used to derive the structural fragility curves; however, the computational cost would significantly increase once the parameter uncertainties are considered. Bayesian updating is effective in incorporating prior knowledge and observational data to refine fragility estimates. However, it relies on well-defined prior distributions and requires iterative updating as new data becomes available. In many practical cases, defining appropriate priors can be challenging, particularly when data is limited. Fortunately, artificial intelligence techniques including machine learning and deep learning provide the solution by establishing the mathematical model that can replace the map between the different physical quantities in engineering [43; 44; 45]. For example, [PERSON] et al. used machine learning techniques to predict the seismic collapse behavior of deep steel columns [46]. [PERSON] and [PERSON] estimated seismically induced slope displacements through machine learning [47]. Furthermore, many scholars conducted computer vision for structural damage detection using deep learning [48]. The use of artificial intelligence solves these engineering problems and simplifies the data results, and also provides a way to obtain the results out of the calculation range. To address the above-mentioned issues, this paper studies the seismic performance of a typical \(\pm\)800 kV suspended filter capacitor by developing a complex and detailed simulation model aimed at accurately predicting the seismic response. Then, a seismic vulnerability analysis approach based on the adaptive boosting (AdaBoost) regressor was proposed and applied to the filter capacitor. In the analysis method, various material property parameters and earthquake uncertainty were considered at the same time. At last, the accuracy and efficiency of the proposed vulnerability analysis approach was discussed. In addition, the impact of the pretension force of the bottommost suspension insulators was also investigated. Therefore, the main contributions can be summarized as follows:  
(1) This paper fills the gap of seismic performance and the vulnerability evaluations of electrical equipment in substations on the topic of suspended filter capacitors. (2) This work first investigates the influence of the pre-tension force of the suspension insulators on seismic responses and vulnerability. (3) This paper proposes a novel AdaBoost regressor-based approach for assessing the seismic vulnerability of substation equipment, which could consider the uncertainties of multiple material parameters. Single-variable-input and multi-variable-input regressors are proposed and compared, respectively. (4) This work validates the effectiveness of the proposed approach from the views of computational efficiency and prediction accuracy.