Neuronet-Based Approach To Modeling the Durability of Aggregate in Concrete Pavement Construction

The durability of aggregate used in concrete pavements construction is commonly assessed by subjecting small concrete beams containing the aggregate to cyclic freezing and thawing. The durability of aggregate and concrete specimens is quantified by measuring the durability factor (DF) and percent expansion (EXP). A typical durability test may last 3 to 5 months and involve high costs. It was assumed that the durability of aggregate used as a constituent in concrete elements may be related to some easily measured physical properties of the aggregate. A data base obtained from records of the Kansas Department of Transportation contained a total of 750 durability tests. The observed wide scatter in the experimental data when DF or EXP is related to one physical parameter suggested the use of artificial neural networks to model durability. Neural network models were developed to predict durability of aggregate from five basic physical properties of the aggregate. The models were found to classify the aggregates with regard to their durability with a relatively high accuracy. In addition, the models were used to assess the reliability of prediction. To illustrate the use of the models, numerical examples are presented.