## 1 Introduction  
Due to the continuous development of automotive technology, structural systems are gradually being integrated. Uncertainty issues have also become increasingly complex and diverse, involving multiple covariates and implicit responses. A common approach is to establish a surrogate model through the design of experiments (DOE) for reliability analysis and optimal design. To improve efficiency, on the one hand, it is necessary to establish an automated analysis platform to achieve tasks such as model parameterization, solving and post-processing, thereby addressing the problem of inefficiency associated with traditional manual operation. Currently, a variety of software is available for parametric design. For the study of some microscopic stochastic structures, [PERSON] et al. [1] reconstructed the catalyst layer of a proton exchange membrane fuel cell (including non-precious metal catalyst or platinum) with a high-resolution porous structure. [PERSON] et al. [2] parameterized random reconstruction of the fuel cell gas diffusion layer by an ANSYS-SpaceClaim script, improving the efficiency of the repetitive operations. Additionally, for larger structureswith more fixed shape characteristics, scholars have conducted research on optimization techniques for structural parameters. [PERSON] et al. [3] employed the HyperStudy (version 2017.2.0) tool and the ANSYS (version 18.1) solver to optimize the weight of the leaf disc. [PERSON] et al. [4] proposed a parametric modeling method based on the secondary development of Abaqus and designed a plug-in tool for cell structure simulation analysis through a graphical user interface. [PERSON] et al. [5] used SolidWorks and ANSYS Workbench software for secondary development and compiled parametric design software using C# to integrate 3D model design, engineering drawing generation and finite element safety analysis. In addition, LS-OPT (version 6.0) [6] and Isight [7] are also often used to build optimization platforms for structural performance analysis and lightweight design. It is evident that the current commonly used software has parametric design and optimization capabilities. In practical application, suitable methods and software should be selected according to the design requirements. On the other hand, metamodeling methods are used for reliability analysis. For complex engineering problems, there may be complex nonlinear relationships between the design variables and the responses, which are not explicitly expressed and are black-box problems, or the performance (limit state) functions involve expensive computational problems. Therefore, metamodeling methods are used in practical engineering, and the basic idea is to construct approximate (surrogate) models, i.e., to establish approximate mapping relationships between input variables and output responses by DOE, which play a role in computationally efficient and predictable responses at unknown points. Commonly used surrogate models include the response surface model [8], polynomial chaos expansions (PCEs) [9], kriging [10; 11], polynomial chaos-kriging (PCK) [12], machine learning [13; 14], support vector machines [15; 16] and radial-basis functions [17]. Once the surrogate model has been constructed, reliability methods can be used to calculate the failure probability. In order to reduce the training sample size, active-learning metamodeling methods have been proposed, such as the active-learning reliability method, which combines kriging and Monte Carlo simulation (AK-MC) [18]; the candidate sample set is a large number of samples generated by the MC method, and the evaluation of the learning function occurs in each loop, which affects the computational efficiency. Therefore, some scholars have replaced MC methods with importance sampling (IS) [19], subset simulation (SS) [20] and directional sampling (DS) [21] to form more efficient kriging-based active-learning methods.