A novel learning function based on Kriging for reliability analysis

Yan, Shi; Lü, Zhenzhou; He, Ruyang; Chen, Siyu

doi:10.1016/j.ress.2020.106857

Cited by 80 publications

(17 citation statements)

References 35 publications

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“…More details can be found in [16]. Recent applications of Machine learning models in reliability engineering include methodology development, system diagnostic, remaining useful life estimation and prognostic health management [17][18][19][20][21]. Unsupervised learning consists in examining datasets with only input variables or features, and no labels or response variable.…”

Section: B Machine Learning For Anomaly Detectionmentioning

confidence: 99%

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability

Guo

Saleh

2021

IEEE Access

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Section: B Machine Learning For Anomaly Detectionmentioning

confidence: 99%

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability

Guo

Saleh

2021

IEEE Access

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“…A series system with three design points is used as the second example, 28,45 whose limit state function is given by:…”

Section: Numerical Examplesmentioning

confidence: 99%

“…The example of non-linear oscillator is shown in Figure 8. The input six-dimensional variables are normal distribution and uncorrelated, 19,20,28,41,50 which are shown in Table 4. The limit state function is defined as follows:…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Therefore, in order to improve the computational efficiency and to avoid a large of simulation cost in engineering reliability analysis, more recent attention has focused on surrogate model-based method, which aims to replace original performance function with approximate numerical model. Up to now, various surrogate models are widely used to balance accuracy and efficiency, such as response surface method (RSM), 17,18 neural network (NN), 19 radial basis function (RBF), 20,21 support vector machine (SVM), 22,23 Kriging model, 24–28 polynomial chaos expansion (PCE), 29 polynomial chaos kriging (PCK), 30 and deep neural network (DNN), 31 etc. Generally, to construct a surrogate model, one-shot sampling and sequential sampling are two typical methods.…”

Section: Introductionmentioning

confidence: 99%

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An active learning method combining adaptive kriging and weighted penalty for structural reliability analysis

You

Zhang

Tang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Reducing the surrogate model-based method computation without loss of prediction accuracy remains a significant challenge in structural reliability analysis. The unbalanced probability density, important information in critical region and information redundancy of added sample points are ignored in most of traditional surrogate-based methods, resulting in heavy computational burden. In this work, an active learning combining adaptive Kriging method and weighted penalty (AK-WP) is proposed to analyze the reliability of engineering structures. Firstly, an active learning and weighted penalty function (WPLF) is the result of integrating active learning method, weighted function and penalty function, which is proposed to find the most probable point (MPP). Meanwhile, to avoid redundant information, the best suitable MPP is determined by a proposed distance law established between the found MPP and the existing design of experiment (DoE). Secondly, the Kriging model is refined according to best suitable MPP in each iteration. Thirdly, the failure probability is estimated by the Monte Carlo sample points from the n-ball domain until the convergence condition is satisfied. The accuracy and efficiency of the proposed method are demonstrated by some numerical examples including the highly nonlinear, the small probability problems and implicit function, as well as a real engineering application.

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“…The active learning method selects a small amount of experimental design points to build the initial surrogate model and identify the best next point by active learning equation until the required failure probability is obtained. Some possible active learning functions include but are not limited to, H learning function [50], least improvement function [51], reliability based expected improvement function [52], and folded normal based expected improvement function [53]. Bayesian experimental design selecting the next best point considers not only the potentially "dangerous" points to be close to the limit state, but also the updating process of the next best point [54][55][56].…”

mentioning

confidence: 99%

Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads

Liu

Shang

Liu

et al. 2021

JMSE

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Offshore steel trestles (OSTs) are exposed to severe marine environments with stochastic wave and current loads, making structural safety assessment challenging and difficult. Reliability analysis is a suitable way to consider both wave and current loading intensity uncertainties, but the implicit and complex limit state functions of the reliability analysis usually imply huge computational costs. This paper proposes an efficient reliability analysis framework for OST using the kriging model of optimal linear unbiased estimation. The surrogate model is built with stochastic waves, current parameters, and the corresponding load factors. The framework is then used to evaluate the reliability of an example OST subjected to wave and current loads at three limit states of OST, including first yield (FY), full plastic (FP), and collapse initiation (CI). Three different distributions are used for comparison of the results of failure probability and reliability index. The results and the computational cost by the proposed framework are compared with that from the Monte Carlo sampling (MCS) and Latin hypercube sampling (LHS) method. The influences of sample number on the prediction accuracy and reliability index are investigated. The influence of marine growth on the reliability analysis of the OST is discussed using MCS and the kriging model. The results show that the reliability analysis based on the kriging model can obtain the reliability index for the OST efficiently with less calculation time but similar results compared with MCS and LHS. With the increase of the number of samples, the prediction accuracy of the kriging model increases, and the corresponding failure probability fluctuates greatly at first and then tends to be stable. The reliability of the example OST is reduced with the increase of marine growth, regardless of the limit state.

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A novel learning function based on Kriging for reliability analysis

Cited by 80 publications

References 35 publications

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability

An active learning method combining adaptive kriging and weighted penalty for structural reliability analysis

Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads

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