In the Netherlands, there is a need for a procedure that allows accurate estimation of the stiffness of cement bound base courses using deflection measurements and avoiding the need to take a large amount of cores. Such aprocedure is needed to ensure clients that the pavement is built by the contractor as agreed upon in the contract. This paper describes the development of such a procedure. The procedure is developed using Computational Intelligence (CI) Techniques, and in particular Artificial Neural Networks (ANN) and Support Vector Machines (SVM), on a data set consisting of over 2000 deflection profiles calculated for a large number of three layer pavement structures using the BISAR PC software. The ANN and SVM models use falling weight deflectometer (FWD) deflection bowl parameters and the total pavement thickness as input. The total pavement thickness can be determined with radar measurements. The model showed to be capable of predicting the cement treated base course modulus with a high degree of accuracy and isa quick and powerful tool for scanning the stiffness of cement bound basecourses.
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