Structural reliability analysis based on ensemble learning of surrogate models

Cheng, Kai; Lü, Zhenzhou

doi:10.1016/j.strusafe.2019.101905

Cited by 98 publications

(35 citation statements)

References 55 publications

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“…Surrogate model-based methods attempt to replace the original performance function (PF) with some computational cheaper models, which are recently heated along with the soaring of machine learnings [25]. Nevertheless, due to the lack of solid mechanical bases, most of the surrogate models, including conventional response surface methods [21], statistical learnings [26], Kriging interpolations [27], support vector machines [28], artificial neuron networks [29] and their hybrids or variations [30][31][32][33], would appear to be too crude when high accuracy of reliability estimation is required, which would be even deteriorated when sharp changes on LSS exist. Compared to methods following the three afore-mentioned three clues, methods emphasize on searching algorithms seem more efficient and accurate, which are realized by regulating the sampling process strategically through some algorithms.…”

Section: Introductionmentioning

confidence: 99%

Breadth-First Search Multi-Dimensional Binary Search Tree based Algorithms for Structural

Xu¹

2022

Preprint

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This research proposes a set of novel algorithms for structural reliability estimation based on muti-dimensional binary search tree and breadth-first search, namely the reliability accuracy supervised searching algorithm, the limit-state surface resolution supervised searching algorithm and the reliability index precision supervised fast searching algorithm. The proposed algorithms have the following strengths: 1, all the proposed algorithms have satisfactory computational efficiency by reducing redundant samplings; 2, their computational costs are stable and computable; 3, performance functions of high non-linearity can be will handled; 4, the reliability accuracy supervised searching algorithm can adapt its computational cost according to a prescribed accuracy; 5, the limit-state surface resolution supervised searching algorithm is able to probe sharp changes on limit-state surfaces; 6, the reliability index precision supervised fast searching algorithm computes the reliability index with sufficient precision in a fast way.

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Section: Introductionmentioning

confidence: 99%

Breadth-First Search Multi-Dimensional Binary Search Tree based Algorithms for Structural

Xu¹

2022

Preprint

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“…On the other hand, the FORM and SORM methods, which are part of the moment methods, are useful in structural engineering problems [10][11][12][13][14][15][16]. A reliability index, which defined as the shortest geometric distance between the origin of standard normal space and the LSF, is presented.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Operator for Reliability Assessment of Frame Structures

Kaveh

Vaez

Hosseini³

et al. 2021

Period. Polytech. Civil Eng.

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In structural design of steel frames, in order to achieve proper safety, the effect of uncertainties in the design and loading parameters of the structure must be considered. This approach is obtained by defining a reliability index. In this study, the Modified Dolphin Monitoring (MDM) operator was used to evaluate the reliability index of three well-known steel frame structures based on the Hasofer-Lind method. The reliability index was evaluated using the EVPS and VPS algorithms and with considering the MDM operator on them. The constraint of the last story drift is considered as limit state function. The random variables consist of external loads, modulus of elasticity, moment of inertia and cross-sectional areas. According to the number of evaluations of the limit state function, the results show the efficiency of this method in comparison to the Monte Carlo simulation method. Also, the values of the most probable point (MPP) are examined.

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“…While it is easy to combine FEA with the MCS method to conduct reliability analysis, this method is computationally expensive. In recent years, metamodeling techniques have been developed to overcome this issue, such as the model tree (MT), evolutionary polynomial regression (EPR), multivariate adaptive regression spline (MARS), gene expression programming (GEP) [15], response surface method (RSM) [16][17][18], support vector machine [19,20], kriging surrogate model [21][22][23][24], and ANN [25][26][27][28][29][30][31][32]. Metamodeling techniques are adopted to establish the approximate models, which can replace the original implicit LSF.…”

Section: Introductionmentioning

confidence: 99%

Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

2021

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This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

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Structural reliability analysis based on ensemble learning of surrogate models

Cited by 98 publications

References 55 publications

Breadth-First Search Multi-Dimensional Binary Search Tree based Algorithms for Structural

Breadth-First Search Multi-Dimensional Binary Search Tree based Algorithms for Structural

Heuristic Operator for Reliability Assessment of Frame Structures

Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

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