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Page Predicting the Compressive Prepared by: October, 1995 EXECUTIVE SUMMARY This report describes the evolution of
a model to predict the compressive strength of high
performance silica fume concrete using Bayesian
Regression. This document fulfils the requirements of the
agreement between this Department and CSHRP concerning
the Joint CSHRP /Agency Bayesian Applications Project. Highperformance silica fume concrete
is a product that is being used in Newfoundland to
achieve high early strength in concrete structures.
Contractors have the option of using this product to
speed up construction activities such as the removal of
false work and form work. As the product is new to the
Department, it was suggested that a model should be
created to predict its compressive strength based on the
properties of the concrete mix. As Department personnel had little
experience with the product, it was decided to initially
obtain Prior information only from laboratory tests and
old mix designs of normal Portland cement and silica fume
concretes. Sample data was selected from the project
files of two structures constructed with silica fume
concrete in 1994. Several variables were identified and a
Bayesian regression model was created by the computer
program XLBAYES using the Data Prior method.
Sensitivity analysis of the model suggested that the
laboratory data should only be concerned with silica fume
cement. Additional laboratory testing was carried out on
silica fume concrete and the data on portland cement
concrete was removed. Examination of Model 2 suggested an
improvement in the predictive equation. . However, the
coefficient of the variable for the fine to total
aggregate ratio did not have the correct sign. Laboratory
data indicated that the compressive strength of the
concrete would increase if the amount of fine aggregate
in the mix increases. The observations from the
construction of the two structures and conventional
practice predicted otherwise. A third model was developed using an
Expert Judgement Prior where three experts were asked to
predict the compressive strengths. Posterior equations
for two of the experts agreed that the compressive
strength would decrease when the fine aggregate content
increases. The third expert based his estimates on the
laboratory data and predicted otherwise. It was concluded that Bayesian analysis can be a vital tool when developing predictions. Sometimes soliciting expert judgement is more valuable than collecting data. Acknowledgements I would like to thank the following
people who contributed to the preparation and completion
of this report: Mr. Keith Foster, P. Eng., Manager of
Materials Engineering, who suggested the topic for the
report and provided concrete expertise and direction. Mr. Colin Crane, Senior Concrete
Technician, who provided most of the laboratory and field
data on the silica fume concrete mix designs and acted as
one of the experts. Mr. Dennis Coffin, Manager of
Laboratory and Field Services with Newfoundland
Geosciences Ltd.,who contributed expert analysis and the
best predictive model. Mr. Chris Walsh, Engineering Student,
who acted as data manager for the first model iteration
and wrote the working paper on factors influencing the
compressive strength of concrete. Mr. Mark Nickeson and Mr. Lyle Kajner,
associates with VEMAX Management Inc., who provided
valuable feedback and assistance with interpretations of
the model. Mr. Luc Frechette and Mr. Greg
Williams, Project Managers with CSHRP who organized the
training sessions and workshops on Bayesian analysis. Mr. Cory Williams, Engineering Student,
who proof read most of the report and made several
constructive suggestions.
References
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