Durability and service life prediction of reinforced concrete structures
Journal article, 2015
This paper presents some durability and service life models for reinforced concrete structures with regard to chloride ingress, carbonation and frost attack. In the past years a number of models for durability design of concrete structures have been suggested by relevant organisations or international committees. It is necessary to validate these models against long-term field data for their applicability with respect to exposure climate in order to satisfactorily use the models in the durability design and redesign of concrete structures. In this study, various potential models for concrete resistance to chloride ingress, carbonation and frost attack were briefly reviewed. Three models including the simple ERFC, the DuraCrete and the ClinConc, for prediction of chloride ingress were evaluated using the infield data collected from both the field exposure site after over 20 years exposure and the real road bridges of about 30 years old. A physicochemical model for prediction of carbonation depth was evaluated using the infield data collected from the field exposure site after 11 years exposure and the limited data from the real structures with the age of 7-13 years. For the modelling of frost attack, some problems in measurement of critical saturation degree and actual degree of saturation are discussed. According to the comparison results, the simple ERFC overestimates whilst the DuraCrete model underestimate the chloride ingress in most cases. The ClinConc model on the other hand gives reasonable good prediction for both the short-term (one year) and the long-term (21 years) exposure. The Papadakis model for carbonation also gives fairly good prediction of carbonation depth when compared with the Norwegian infield data classified as exposure class XC3, but underestimates the carbonation depths when compared with the infield data from Norwegian structures in exposure class XC4. For the frost attack, it is premature to apply the models to the service life prediction so far.