Although the majority of creep models are comprehensive and up-to-date, there is a lack of consensus in their utilisation due to substantial scatter in their predictions, even when comparisons are made under well-controlled conditions. On one hand, creep entails complex phenomena that depend on several factors and, on the other hand, these models are typically utilised on a deterministic basis without fully incorporating information related to random input variability.

In this paper, a methodology is proposed, based on Bayesian updating methods, for creep deformation prediction by combining prior model distributions obtained through Monte Carlo simulation with in-situ measurements obtained from concrete specimens. Both single point-in-time and sequential updating approaches are formulated and contrasted in the context of site data collected over a period of about six years. For the specific structure examined, the sequential updating method offers advantages in terms of the estimated variability of future predictions. The proposed methodology is suitable for quantifying the value of monitoring information, as demonstrated by considering the change in prediction variability against the length of observation period.