Multi-factor model to predict surface chloride concentration of concrete based on fuzzy logic system

Abstract

Surface chloride concentration (Cs) is one of the most critical parameter to determine the chloride concentration in concrete structure by Fick’s second law. The prevalent prediction models don’t consider the coupling effect of multiple factors and their comprehensive influence on Cs, which limits their calculation precision. Therefore, this paper uses the test data from published literatures to formulate fuzzy rules, and employs the membership function to transform literal descriptions of the service environments of concrete structures into mathematical language. The quantitative prediction model takes water-to-binder ratio, mineral admixtures type, temperature, exposed marine environment (or solution concentration of simulated seawater in the experiment) and duration of exposure as input variables, and takes Cs as output variable. Simultaneously, quantitative effects of these input variables on Cs are also discussed. It is shown that the goodness-of-fit of the prediction results obtained by the proposed model was greater than 0.9, which indicates the prediction accuracy of the proposed model is much higher than that of other prevalent models. In addition, the calculated results show that the duration of exposure is the most significant factor affecting Cs. Under different exposed environmental conditions, the time when the chloride concentration on surface of concrete tends to stable is different. This critical time in submerged zone or splash zone is longer than it in tidal zone.