Data-Based Statistical Analysis of Laboratory Experiments on Concrete Frost Damage and Its Implications on Service Life Prediction

To meet the requirements of durability design for concrete suffering frost damage, several test standards have been launched. Among the various damage indexes such as deteriorated compressive strength, relative dynamic elastic modulus (RDEM), residual deformation, etc., the concept of a Durability Factor (DF) is proposed by many standards to define the frost resistivity of concrete against frost action based on the experimental results from standard tests. Through a review of the literature, a clear tendency of strength/RDEM decay and residual deformation increase is captured with increasing cycles of freezing and thawing. However, tests following different standards finally derive huge scattering quantitative responses of frost resistance. Based on the large database of available laboratory experiments, this study presents a statistical analysis to propose a predictable model to calculate the DF with respect to other material factors. The statistical model is believed to be more convenient for engineering applications since the time-consuming experiment is no longer needed, and it is more precise compared with that developed according to only single experimental results to cover the uncertainties and unavoidable errors in specific tests. Moreover, the formula to calculate the DF is revised into a more general form so as to be applicable for all the laboratory experiments even for those cases without fully following the standards to derive a DF value.

Automated summary

To meet durability design requirements for concrete under frost damage, various test standards have been proposed. However, there are significant differences in the quantitative responses of frost resistance derived from tests following different standards. This study presents a statistical analysis to propose a predictable model for calculating a Durability Factor (DF) that is more convenient for engineering applications and takes into account uncertainties and errors in specific tests.