A combined radial basis function and adaptive sequential sampling method for structural reliability analysis

Hong, Linxiong; Li, Huacong; Peng, Kai

doi:10.1016/j.apm.2020.08.042

Cited by 45 publications

(3 citation statements)

References 41 publications

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“…In recent years, Kriging metamodel methods have obtained remarkable attention in the state-of-the-art literature. From the design of experiment (DOE) aspect of view, the strategy for constructing a Kriging model can broadly be classified into two sorts, the one is "one-shot" and the other is adaptive sampling or sequential sampling methods [26]. Literally, "one-shot" means producing enough design points before generating a Kriging model without supplementing new sample points in succeeding courses.…”

Section: Akmentioning

confidence: 99%

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Qin¹,

Cao²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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In this work, an improved active kriging method based on the AK-IS and truncated importance sampling (TIS) method is proposed to efficiently evaluate structural reliability. The novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the literature. The innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly, and the whale optimization algorithm (WOA) is employed to acquire the optimal Kriging model and the most probable point (MPP). To verify the performance of the AWK-TIS method for structural reliability, four numerical cases which are utilized as benchmarks in literature and one real engineering problem about a jet van manipulate mechanism are tested. The results indicate the accuracy and efficiency of the proposed method.

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Section: Akmentioning

confidence: 99%

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Qin¹,

Cao²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…They further constructed an objective function response surface surrogate model through Latin hypercubic sampling and optimized the design parameters for horizontal vibration reduction using a multi-objective genetic algorithm. Response surface model (RSM), [14][15][16][17] radial basis function model (RBF), [18][19][20] and Kriging model [21][22][23][24][25] are frequently used surrogate models. Owing to its capacity to substantially enhance the efficiency of engineering optimization design problems, the surrogate model approach has garnered significant recognition in the aerospace and other domains.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-model-based dynamic-sensing optimization method for high-speed elevator car horizontal vibration reduction

Cheng,

Zhang,

Wang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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The horizontal vibration of high-speed elevators has a significant impact on ride comfort and safety stability. Plenty of intelligent methods have been implemented in horizontal vibration reduction. The conventional optimization process for vibration reduction design involves conducting numerous complex dynamics simulations to establish the correlation between design parameters and vibration response. However, this method is inefficient and costly due to the demanding computational power requirements and time-consuming nature of the simulations. This study proposes a surrogate-model-based dynamic-sensing optimization method. A Kriging model is developed based on dynamics simulations, providing an efficient approach for obtaining the vibration response with specified design parameters. The DSPSO algorithm was proposed by incorporating perceptual particles and environmental adaptation mechanisms into the PSO algorithm. The DSPSO algorithm can be nested and interact with the Kriging model to optimize design parameters. During evolution, feasible and uncertain solutions are extracted into the sample space to enhance the accuracy of the Kriging model. The effectiveness of the proposed method is validated using both the ZDT series test functions and elevators from Canny Elevator Co., Ltd.

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“…However, it is noted that most of the aforementioned active learning methods are developed based on the Kriging model, where the error at unknown points can be empirically measured by the Kriging variance. Thus, the use of these learning algorithms to other surrogate models is not directly applicable unless additional effort such as bootstrap resampling strategy [86] or fold cross-validation [87] is employed to get the prediction variance, which is a cumbersome process. To address this issue, several studies have been devoted to getting the model variance in a more effective way, among which the recently proposed Bayesian inference framework has shown promising potential [88,89].…”

Section: Introductionmentioning

confidence: 99%

Efficient structural reliability analysis based on adaptive Bayesian support vector regression

Wang

et al. 2021

Computer Methods in Applied Mechanics and Engineering

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A combined radial basis function and adaptive sequential sampling method for structural reliability analysis

Cited by 45 publications

References 41 publications

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Surrogate-model-based dynamic-sensing optimization method for high-speed elevator car horizontal vibration reduction

Efficient structural reliability analysis based on adaptive Bayesian support vector regression

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