Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy

Luo, Changqi; Zhu, Shun-Peng; Keshtegar, Behrooz; Macek, Wojciech; Branco, Ricardo; Meng, Debiao

doi:10.1016/j.cma.2024.116863

Cited by 14 publications

(3 citation statements)

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“…As mentioned in the literature, MCS is one of the most well-known quantification techniques [34,35]. Although it requires a higher random sampling number than 10 4 , it can handle large random variables, various distribution types, and highly nonlinear systems [36][37][38]. That is why MCS was chosen to validate and confirm some of the results (Case 2 and Case 4).…”

Section: Validation Results With Mcsmentioning

confidence: 99%

“…The probabilistic method requires statistical calculation; otherwise, the second one operates without statistical analysis. One of the most famous probabilistic techniques, called Monte Carlo simulation (MCS) [34,35], is computationally intensive and widely applied to handle various kinds of uncertainty, including large random variables, different distribution types, and highly nonlinear systems [36][37][38]. Subsequently, the first-order reliability method (FORM) and the second-order reliability method (SORM) were developed to reduce computational time by approximating the performance function to the first order and the second moment, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the first-order reliability method (FORM) and the second-order reliability method (SORM) were developed to reduce computational time by approximating the performance function to the first order and the second moment, respectively. Very recently, an improvement of the technique called the active kriging algorithm with conjugate first-order reliability method (AK-CFORM) has been developed to enhance the robustness of FORM [36]. The former represents progress, but it needs more computation time (requires higher precision) in the analysis of the statistical distribution of uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Kumkam,

Sleesongsom

2024

Aerospace

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This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner.

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