An efficient method by nesting adaptive Kriging into Importance Sampling for failure-probability-based global sensitivity analysis

2021 ◽  
Author(s):  
Jingyu Lei ◽  
Zhenzhou Lu ◽  
Lu Wang
Keyword(s):  
Sensitivity Analysis ◽  
Importance Sampling ◽  
Efficient Method ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity ◽  
Adaptive Kriging

scholarly journals Global Sensitivity Analysis of Fuzzy Distribution Parameter on Failure Probability and Its Single-Loop Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Cheng ◽  
Zhenzhou Lu ◽  
Luyi Li
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Single Loop ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Distribution Parameters ◽  
Input Variables ◽  
Sampling Probability

An extending Borgonovo’s global sensitivity analysis is proposed to measure the influence of fuzzy distribution parameters on fuzzy failure probability by averaging the shift between the membership functions (MFs) of unconditional and conditional failure probability. The presented global sensitivity indices can reasonably reflect the influence of fuzzy-valued distribution parameters on the character of the failure probability, whereas solving the MFs of unconditional and conditional failure probability is time-consuming due to the involved multiple-loop sampling and optimization operators. To overcome the large computational cost, a single-loop simulation (SLS) is introduced to estimate the global sensitivity indices. By establishing a sampling probability density, only a set of samples of input variables are essential to evaluate the MFs of unconditional and conditional failure probability in the presented SLS method. Significance of the global sensitivity indices can be verified and demonstrated through several numerical and engineering examples.

scholarly journals Resonance risk and global sensitivity analysis of a straight–curved combination pipe based on active learning Kriging model

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983835
Author(s):  
Yuzhen Zhao ◽  
Yongshou Liu ◽  
Qing Guo ◽  
Tao Han ◽  
Baohui Li
Keyword(s):  
Sensitivity Analysis ◽  
Active Learning ◽  
Coordinate System ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Dynamic Stiffness ◽  
Kriging Model ◽  
Global Sensitivity ◽  
Pipes Conveying Fluid ◽  
Conveying Fluid

The resonance failure of straight–curved combination pipes conveying fluid which are widely used in engineering is becoming a serious issue. But there are only few studies available on the resonance failure of combination pipes. The resonance failure probability and global sensitivity analysis of straight–curved combination pipes conveying fluid are studied by the active learning Kriging method proposed in this article. Based on the Euler–Bernoulli beam theory, the dynamic stiffness matrices of straight and curved pipes are derived in the local coordinate system, respectively. Then the dynamic stiffness matrix and characteristic equation of a straight–curved combination pipe conveying fluid are assembled under a global coordinate system. The natural frequency is calculated based on the characteristic equation. A resonance failure performance function is established based on the resonance failure mechanism and relative criterions. The active learning Kriging model based on expected risk function is introduced for calculating the resonance failure probability and moment-independent global sensitivity analysis index. The importance rankings of input variables are obtained with different velocities. According to the results, it is shown that the method proposed in this article provides a lot of guidance for resonance reliability analysis and anti-resonance design in combination pipes conveying fluid.

scholarly journals Global sensitivity analysis of failure probability caused by fatigue crack propagation

2019 ◽  
Author(s):  
Zdeněk Kala
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Structural Reliability ◽  
Global Sensitivity Analysis ◽  
Limit State ◽  
Random Variables ◽  
Probability Of Failure ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Stress Changes

The probability of failure of a load bearing steel member is investigated using a new type of global sensitivity analysis subordinated to contrasts. The main objective of the probability-oriented sensitivity analysis is structural reliability. The structural reliability methodology uses random variables as inputs. The subject of interest is the identification of those random variables that are most important when the limit state of a steel bridge member is reached. The limit state is defined by the occurrence of brittle fracture, which results from stress changes caused by multiple repeated loads. The propagation of a single-edge crack from initial to critical size is analysed using linear fracture mechanics. The failure probability and sensitivity indices are calculated using sampling-based methods. The sensitivity indices are estimated using double-nested-loop simulation of the Latin Hypercube Sampling method. New findings indicate that interaction effects among input variables strongly influence the probability of failure especially at the beginning of the operating period.

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