Global sensitivity analysis of the reliability of the slope stability based on the moment-independent combine with the Latin hypercube sampling technique

Xu, Zhaoxia; Wang, Xiuzhen

doi:10.1007/s00477-023-02385-5

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Step 3 Determine the input variables and output responses, and obtain the samples by Latin hypercube sampling (LHS) approach and conduct deterministic analysis, and divide the acquired into training and testing samples [29].…”

Section: Structural Reliability Estimation Procedures With Mnnfmentioning

confidence: 99%

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Liang,

Sun,

Sun

et al. 2024

Aerospace

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As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.

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Section: Structural Reliability Estimation Procedures With Mnnfmentioning

confidence: 99%