An Efficient Reliability Analysis Method for Steel Structures Based on Support Vector Machines and Hyperparameter Optimization

To address the challenge of exorbitant computational costs in the reliability analysis of complex steel structures, which stems from the impact of multiple sources of uncertainty throughout their entire lifecycle, this paper presents a comparative evaluation of the explicit reconstruction of the Limit State Function (LSF) using SVM combined with Hyperparameter Optimization (HPO) for structural reliability analysis under constrained computational budgets. Although traditional Monte Carlo simulation (MCS) exhibits high accuracy, it requires a substantial number of finite element calculations, rendering it difficult to satisfy the efficiency requirements of engineering projects. Conversely, the first-order and second-order reliability methods (FORM/SORM) offer high computational efficiency but rely on explicit limit state functions, posing challenges for their direct application to complex structural systems. Thus, this study initially acquires response samples of the structure under various combinations of random variables through a limited number of finite element analyses (FEA). Subsequently, it employs an SVM to develop a highly accurate equivalent explicit limit state function, which serves as a substitute for the original implicit limit state function. Finally, it integrates Monte Carlo simulation to efficiently evaluate the structure’s failure probability and reliability index. Meanwhile, to tackle the problem of SVM model performance being highly susceptible to hyperparameters, this study presents a comparative analysis of four strategies: Bayesian Optimization (BO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Random Search (RS), aiming to identify the optimal parameter combination and improve the model’s generalization capability. Through verification with four progressive examples, including linear, nonlinear, truss, and multistory frame structures, the results demonstrate that the proposed method can accurately characterize the nonlinearity of structural responses. The obtained failure probabilities and reliability indices are in close agreement with those obtained from the direct Monte Carlo simulation (MCS) and existing research. Moreover, while maintaining computational accuracy, the method significantly reduces computational costs, thereby providing an efficient and practical solution for structural reliability analysis in engineering practice.