A Bayesian framework for modeling and testing the temperature-dependent residual compressive strength of concrete (REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World, 2026)
This study presents a novel Bayesian model for the temperature-dependent residual compressive strength of a concrete mix. The model consists of a lognormal distribution for the concrete strength retention factor kfc, i.e., the ratio of the compressive strength after exposure to elevated temperature to the initial compressive strength at room temperature. The Bayesian conceptualization of the model enables straightforward updating of the kfc model as new experimental data becomes available. Furthermore, a framework for pre-posterior analysis is presented to guide decision-making before actual data collection. Specifically, the concept of expected information gain (EIG) is adopted to optimize experimental design. This approach allows the identification of optimal measurement temperatures to reduce uncertainty in estimating kfc. The analysis reveals that measuring at 600–900 °C yields a higher information gain than measuring at any other temperature. Comparative analyses illustrate the effectiveness of the EIG concept in guiding experimental planning.