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In the field of reliability engineering, the Active learning reliabilitymethod combining Kriging and Monte Carlo Simulation (AK-MCS) has been developedand demonstrated to be effective in reliability analysis. However, theperformance of AK-MCS is sensitive to the size of Candidate Sample Pool (CSP),particularly for systems with small failure probabilities. To address thelimitations of conventional AK-MCS that relies on CSP, this paper proposes aCSP-free AK-MCS. The proposed methodology consists of two stages: surrogatemodel construction and Monte Carlo simulation for estimating the failureprobability. In the stage of surrogate model construction, the surrogate modelis iteratively refined based on the representative samples selected by solvingthe optimization problem facilitated by Particle Swarm Optimization (PSO)algorithm. To achieve an optimal balance between solution accuracy andefficiency, the penalty intensity control and the density control for theexperimental design points are introduced to modify the objective function inoptimization. The performance of the proposed methodology is evaluated usingnumerical examples, and results indicate that by leveraging an optimizationalgorithm to select representative samples, the proposed CSP-free AK-MCSovercomes the limitations of conventional CSP-based AK-MCS and exhibitsexceptional performance in addressing small failure probabilities.

CSP-free adaptive Kriging surrogate model method for reliability analysis with small failure probability