The Bayesian updating rule is used to assess the American Concrete Ins
titute (ACI) model relating the elastic modulus of concrete to its com
pressive strength. Uncertainties inherent to the modeling process are
identified. A likelihood function for the assessment of the model is d
erived assuming statistical independence between observations. This fu
nction is subsequently modified to account for model-induced correlati
on. It is shown that the correlation effectively reduces the amount of
information contained in the data. The likelihood model is used with
data available from literature and new data acquired at the University
of California, Berkeley, for a specific concrete mix to compute the p
osterior statistics of the model parameters and to derive a predictive
model for the elastic modulus of concrete. The presented approach is
unique as it accounts for all sources of model uncertainty, deals with
the important issue of model-induced correlation, and shows how Bayes
ian updating can be used to derive an improved predictive model for a
specific concrete mix. Use of the proposed approach in performance-bas
ed codified design is discussed.