2021
DOI: 10.32604/cmes.2021.010482
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Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

Abstract: In structural reliability analysis, simulation methods are widely used. The statistical characteristics of failure probability estimate of these methods have been well investigated. In this study, the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample, called 'contribution indexes' , are proposed to measure the contribution of sample. The contribution indexes in four widely simulation methods, i.e., Monte Carlo simulation (MCS), importance sampling (IS),… Show more

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Cited by 4 publications
(3 citation statements)
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References 31 publications
(53 reference statements)
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“…To assess the effect of x i on the failure probability in its entire distribution ranges, a global sensitivity index is established [74,75] as (17) where P f | x i indicates the conditional failure probability, which can be rewritten as the condition expectation of the indicator function…”
Section: Candidate Sampling Pool Reductionmentioning
confidence: 99%
“…To assess the effect of x i on the failure probability in its entire distribution ranges, a global sensitivity index is established [74,75] as (17) where P f | x i indicates the conditional failure probability, which can be rewritten as the condition expectation of the indicator function…”
Section: Candidate Sampling Pool Reductionmentioning
confidence: 99%
“…9). This step can check whether the stability of the structural model is sufficient to eliminate misjudgments caused by factors such as data contingency and determine the universal significance of the relationships [29]. In Fig.…”
Section: Test Of Structural Model Based On Full Datamentioning
confidence: 99%
“…Specifically, topics across reliability evaluation index of complex mechanical structures, innovation and improvement of intelligent algorithms, fault analysis of complex mechanical structures, reliability evaluation based on intelligent algorithms and reliability optimization design in intelligent algorithms are included. Specifically, "Robust Remaining Useful Life Estimation Based on an Improved Unscented Kalman Filtering Method" by Zhao et al [1], proposes a robust RUL estimation approach to reduce the errors and randomness of estimation results for degradation problems of the linear relationship between the measured values and their degradation state; "A Local Sparse Screening Identification Algorithm with Applications" by Li et al [2], proposes a local sparse screening identification algorithm (LSSI) to identify nonlinear systems; "Subinterval Decomposition-Based Interval Importance Analysis Method" by Wang et al [3], proposes an interval important analytical method; "Research on Trajectory Tracking Method of Redundant Manipulator Based on PSO Algorithm Optimization" by Xu et al [4], studies the trajectory tracking method of redundant manipulator based on Particle Swarm Optimization algorithm; "Robust Design Optimization and Improvement by Metamodel" by Song et al [5], proposes two criteria to judge the optimal solution whether satisfies robustness requirement and suggested a robustness measure based on maximum entropy; "Reliability Analysis Based on Optimization Random Forest Model and MCMC" by Yang et al [6], proposes a novel method of reliability analysis combining Monte Carlo Markov Chain with random forest algorithm; "A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data" by He et al [7], proposes a Bayesian method based on multi-ellipsoid convex model and performance test data to evaluate the nonprobabilistic reliability of structures logically; "Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames" by Sun et al [8], establishes a machine learning (ML)-based seismic fragility analysis framework to effectively assess the risk to structures under seismic loading conditions; "Sensitivity of Sample for Simulation-Based Reliability Analysis Methods" by Yuan et al [9], regards the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample as 'contribution indexes' to measure the contribution of sample; "A Fast Product of Conditional Reduction Method for System Failure Probability Sensitivity Evaluation" by Yang et al [10], applies the idea of failure mode relevancy to failure probability sensitivity analysis; "Stress Relaxation and Sensitivity Weight for Bi-directional Evolutionary Structural Optimization to Improve the Computational Efficiency and Stabilization on Stress-based Topology Optimization" by Ma et al [11], proposes an improved topology optimization method for the continuum structures considering stress minimization in the framework of the conventional BESO method.…”
mentioning
confidence: 99%