Title:
Statistical Extrapolation of Shrinkage Data--Part II: Bayesian Updating
Author(s):
Z. P. Bazant, J. K. Kim, F. H. Wittmann, and F. Alou
Publication:
Materials Journal
Volume:
84
Issue:
2
Appears on pages(s):
83-91
Keywords:
Bayes theorem; concretes; deformation; diffusion; drying; errors; extrapolation; regression analysis; shrinkage; statistical analysis; volume change; Materials Research
DOI:
10.14359/1798
Date:
3/1/1987
Abstract:
The statistical information previously available in the literature on concrete shrinkage is exploited to improve the extrapolation of short-time shrinkage measurements into long-time measurements. With this approach the long-time predictions can be significantly improved, compared to the predictions obtained by statistical regression from the measured data alone, as described in Part I of this study. The method of analysis consists of latin hypercube sampling of random parameters of the shrinkage formula for the prior, and in adjustments of the weights of the samples on the basis of the Bayes theorem. The shrinkage formula of the BP model, which is justified by diffusion theory, is used. The formula gives prior mean predictions that are rather close to the measured short-time data. Using this formula, good long-time predictions can be obtained even with measurements of only three-day duration. The formulas from the ACI and the CEB-FIP models are found unsuitable for Bayesian extrapolation. An improvement of the formula for predicting the final shrinkage strain from concrete composition is also presented.