We present a novel approach, referred to as the threshold shift method (TSM), for reliability based design optimization (RBDO). The proposed approach is similar in spirit with the sequential optimization and reliability analysis (SORA) method where the RBDO problem is decoupled into an optimization and a reliability analysis problem. However, unlike SORA that utilizes shift-vector to shift the design variables within a constraint (independently), in TSM we propose to shift the threshold of the constraints. We argue that modifying a constraint, either by shifting the design variables (SORA) or by shifting the threshold of the constraints (TSM), influences the other constraints of the system. Therefore, we propose to determine the thresholds for all the constraints by solving a single optimization problem. Additionally, the proposed TSM is equipped with an active-constraint determination scheme. To make the method scalable, a practical algorithm for TSM that utilizes two surrogate models is proposed. Unlike the conventional RBDO methods, the proposed approach has the ability to handle highly non-linear probabilistic constraints. The performance of the proposed approach is examined on six benchmark problems selected from the literature. The proposed approach yields excellent results outperforming other popular methods in literature. As for the computational efficiency, the proposed approach is found to be highly efficient, indicating it's future application to other real-life problems.Keywords RBDO · threshold shift method · PC-Kriging · uncertainty
IntroductionUncertainty is an eternal companion of all structural systems. In a system, uncertainty can originate from both, uncertain parameters within the model as well as the limitations in the model itself. These uncertainties, if ignored, may result in to catastrophic failures. Therefore, it is necessary to consider the effect of uncertainty in an optimization process. In literature, there exist two methodologies to incorporate uncertainty into the framework of optimization, namely the robust design optimization (RDO) [1-3] and the reliability-based design optimization (RBDO) [4][5][6][7][8][9]. In this study, we focus on RBDO. * These authors have equal contributions