A PAVEMENT DETERIORATION MODEL USING RADIAL BASIS FUNCTION NEURAL NETWORKS

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  • Pavement Deterioration Model Using Radial Basis Function Neural Networks

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A pavement deterioration model (PDM) applying Radial Basis Function Neural Networks (RBFNN) is presented in this paper. The RBFNN architectures are designed to be used to develop PDM based on the database that has at least two point history condition data, and are also designed as sequential PDM where the future pavement condition can be predicted using only information about present MCI value and age of pavements. The pavement condition prediction results are compared with actual measured MCI value and other existing methods. The results indicate that proposed RBFNN architectures have good capability to be used to predict future performance of pavements, and its application is very flexible and less time consuming.

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