Sample Size Reduction Based on Historical Design Information and Bayesian Statistics

Wei, Zhigang

doi:10.4271/2013-01-2440

Cited by 7 publications

(2 citation statements)

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“…Several works have shown that inferring the S-N curve parameters in a Bayesian environment maximizes accuracy and reduces the number of required specimens to infer the S-N curve parameters. 16,17,[59][60][61] A normal distributed prior for the bilinear S-N curve model is constructed based on the random forest with jackknife estimator proposed by Wager. 27 The approach has already been applied to the probabilistic prediction of the S-N curve parameters by Kolyshkin et al 24 To investigate the influence of the prior distribution and to detect a conflicting prior distribution, the simulated outcomes are also evaluated with a uniform prior.…”

Section: Workflow For Fatigue Test Planningmentioning

confidence: 99%

“…The process starts by defining a prior distribution for each S‐N curve parameter. Several works have shown that inferring the S‐N curve parameters in a Bayesian environment maximizes accuracy and reduces the number of required specimens to infer the S‐N curve parameters 16,17,59–61 . A normal distributed prior for the bilinear S‐N curve model is constructed based on the random forest with jackknife estimator proposed by Wager 27 .…”

Section: Workflow For Fatigue Test Planningmentioning

confidence: 99%

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Reduction of testing effort for fatigue tests: Application of Bayesian optimal experimental design

Frie,

Kolyshkin,

Mordeja

et al. 2023

Fatigue Fract Eng Mat Struct

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S‐N curve parameter determination is a time‐ and cost‐intensive procedure. A standardized method for simultaneously determining all S‐N curve parameters with minimum testing effort is still missing. The Bayesian optimal experimental design (BOED) approach can reduce testing effort and accelerates uncertainty reduction during fatigue testing for S‐N curve parameters. The concept is applicable to all S‐N curve models and is exemplary illustrated for a bilinear S‐N curve model. We demonstrate the fatigue testing workflow for the bilinear S‐N curve in detail while discussing steps and challenges when generalizing to other S‐N curve models. Applying the BOED to the bilinear S‐N curve models, minor errors and uncertainties for all S‐N curve parameters are obtained after only 10 experiments for data scatter values below 1.1. For such, the relative error in fatigue limit estimation was less than 1% after five tests. When S‐N data scatter higher than 1.2 is concerned, 17 tests were required for robust analysis. The BOED methodology should be applied to other S‐N curve models in the future. The high computational effort and the approximation of the posterior distribution with a normal distribution are the limitations of the presented BOED approach.

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