An improved AK-MCS for reliability analysis by an efficient and simple reduction strategy of candidate sample pool

Liu, Ziyi; Lü, Zhenzhou; Ling, Chunyan; Feng, Kaixuan; Hu, Yingshi

doi:10.1016/j.istruc.2021.10.090

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…When 𝐺(𝑥) < 0, the structure failure occurs, otherwise, the ten-bar truss structure can normally work. 29 And the vertical displacement 𝐷(𝑥) is obtained from the MATLAB and ANSYS co-simulation. The finite element model of ten-bar truss structure is presented in Figure 14.…”

Section: Example 5: Ten-bar Truss Structurementioning

confidence: 99%

“…The example is a series system with four failure regions. 6,10,29 Suppose 𝑥 1 , 𝑥 2 are standard normal distributed random variables. The performance function is plotted in Figure 6 and is defined as…”

Section: Example 2: Series System With Four Branchesmentioning

confidence: 99%

“…The example is a series system with four failure regions 6,10,29 . Suppose

{x_1},{x_2}

are standard normal distributed random variables.…”

Section: Example Analysismentioning

confidence: 99%

“…The performance function can be defined as

G(x) = 0{\rm{.0036 - }}D(x{\rm)}

. When

G(x) &lt; 0

, the structure failure occurs, otherwise, the ten‐bar truss structure can normally work 29 . And the vertical displacement

D(x{\rm)}

is obtained from the MATLAB and ANSYS co‐simulation.…”

Section: Example Analysismentioning

confidence: 99%

See 3 more Smart Citations

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

Sun

Liu

Zhi

et al. 2023

Quality & Reliability Eng

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Due to the limited sample sizes and highly complicated performance functions, how to improve the reliability calculation accuracy and efficiency is an important issue for complex equipment working in harsh environments. This paper proposes an active learning reliability algorithm based on the double‐mutation slap swarm algorithm‐optimized (DMSSA) Kriging surrogate model, parallel infilling strategy and subset simulation (SS). In the method, the uniform design (UD) is employed to select the initial sampling points and their real responses of performance functions are estimated. The DMSSA integrates Cauchy and Gauss mutation to explore the optimal hyperparameter of Kriging model with high accuracy. The parallel infilling strategy combines the expected improvement (EI), minimizing prediction (MP) and mean squared error (MSE) criteria to find new sampling points, which are adaptively added into the database in each iteration to update the design of experiment (DoE). Ultimately, the SS method is employed to deal with the small failure probability problems. Five various examples are analyzed to verify the calculation accuracy and efficiency of the proposed method.

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Section: Example 5: Ten-bar Truss Structurementioning

confidence: 99%

Section: Example 2: Series System With Four Branchesmentioning

confidence: 99%

“…The example is a series system with four failure regions 6,10,29 . Suppose

{x_1},{x_2}

are standard normal distributed random variables.…”

Section: Example Analysismentioning

confidence: 99%

“…The performance function can be defined as

G(x) = 0{\rm{.0036 - }}D(x{\rm)}

. When

G(x) &lt; 0

, the structure failure occurs, otherwise, the ten‐bar truss structure can normally work 29 . And the vertical displacement

D(x{\rm)}

is obtained from the MATLAB and ANSYS co‐simulation.…”

Section: Example Analysismentioning

confidence: 99%

See 2 more Smart Citations

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

Sun

Liu

Zhi

et al. 2023

Quality & Reliability Eng

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show abstract

“…Then, the adaptive Kriging model is implemented in the low dimensional space identified by the activity scores [36], which reduces the time cost of the adaptive analysis process with a high input space dimension. An Enhanced Adaptive Kriging Monte Carlo Simulation (EAK-MCSI) [37] is proposed based on a candidate sample pool reduction strategy that reduces the time cost required to traverse the candidate sample pool in small failure probability problems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Kriging-Based Fourth-Moment Reliability Analysis Method for Engineering Structures

Wang,

Xie

et al. 2024

Applied Sciences

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The fourth-moment method can accurately perform a reliability analysis when it is challenging to determine the distribution of the random variable due to limited available samples. This method only utilizes the first four moments of the random variable and constructs the fourth-moment reliability index. However, it cannot be applied in engineering cases where the state function cannot be expressed explicitly, as it becomes difficult to establish a correlation between the first four moments of the random variable and the state function. Simplifying the state function forcefully may result in significant reliability prediction errors. To address this limitation, this study proposes an adaptive Kriging-based fourth-moment method for reliability analysis under complex state equations. The proposed method demonstrates better applicability and efficiency compared to existing methods. Several numerical examples are provided to validate the effectiveness and accuracy of the proposed method.

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An efficient adaptive kriging refinement method for reliability analysis with small failure probability

Shi,

Xiang,

Pan

et al. 2023

Struct Multidisc Optim

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An improved AK-MCS for reliability analysis by an efficient and simple reduction strategy of candidate sample pool

Cited by 19 publications

References 34 publications

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

An active learning reliability algorithm using DMSSA‐optimized Kriging model and parallel infilling strategy

An Adaptive Kriging-Based Fourth-Moment Reliability Analysis Method for Engineering Structures

An efficient adaptive kriging refinement method for reliability analysis with small failure probability

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