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This study aims to develop a more comprehensive framework for reliability-based design optimization under double-layer uncertainty.

Augmented Reliability-Based Design Optimization Model Under Double-Layer Uncertainty and Its Efficient Solution

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Randomized trial demonstrates improved efficiency in reliability-based design optimization models, suggesting enhanced robustness.

This study aims to develop a more comprehensive framework for reliability-based design optimization under double-layer uncertainty.
Constructed an augmented-RBDO framework governed by target augmented failure probability constraints.
Proposed a sequential decoupling method for efficient resolution of the A-RBDO model.
Introduced a strategy utilizing dimension-reduction integration and the bisection method for critical augmented quantile estimation.
Enhanced efficiency of the A-RBDO model was demonstrated through three examples.
Significant reduction in computational expense related to augmented quantile evaluation was achieved.
The proposed approach showed robust performance characteristics in various scenarios.

Chen et al. (2026) studied this question.

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