کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
259583 | 503638 | 2012 | 9 صفحه PDF | دانلود رایگان |
Bayesian statistics can be used in order to determine the characteristic value of an unknown distribution based on a limited number of test samples. In cases where no previous test results are available, most often a Bayesian method based on vague prior information is used. The assumption of a vague or uniform prior results in a conservative approach in cases where only a limited number of test results are available. However, in case of concrete, prior information on concrete strength can be found in literature or country-specific prior information can be determined. Therefore, the use of a combined vague–informative prior is of particular interest, more specifically in the form of scaled inverse-χ2 distributions that can be used for updating the standard deviation of the strength distribution of concrete. The differences between the use of a vague and a combined vague–informative prior are investigated through Monte Carlo simulations. Because prior information is taken into account, the uncertainty regarding the standard deviation of the predictive strength distribution is significantly reduced.
► Prior information on concrete strength can be gathered.
► Priors can be taken into account when assessing the in situ characteristic concrete strength.
► The influence of different priors can be compared using Monte Carlo simulations.
► Using combined vague–informative priors yields a less conservative Bayesian estimation method.
► Informative priors significantly reduce the bias and variability of the Bayesian estimate.
Journal: Construction and Building Materials - Volume 28, Issue 1, March 2012, Pages 342–350