Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance


  • Gaetano Bosurgi Dept of Engineering, University of Messina, Messina, Italy
  • Orazio Pellegrino Dept of Engineering, University of Messina, Messina, Italy
  • Giuseppe Sollazzo Dept of Engineering, University of Messina, Messina, Italy




Artificial Neural Network (ANN), asphalt pavement, Long Term Pavement Performance (LTPP), neural network optimisation, Pavement Management System (PMS), structural performance


Artificial Neural Networks represent useful tools for several engineering issues. Although they were adopted in several pavement-engineering problems for performance evaluation, their application on pavement structural performance evaluation appears to be remarkable. It is conceivable that defining a proper Artificial Neural Network for estimating structural performance in asphalt pavements from measurements performed through quick and economic surveys produces significant savings for road agencies and improves maintenance planning. However, the architecture of such an Artificial Neural Network must be optimised, to improve the final accuracy and provide a reliable technique for enriching decision-making tools. In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. In particular, input factor quality (structural, traffic, climatic), “homogeneity” of training data records and the actual net topology have been investigated. Finally, these results further prove the approach efficiency, for improving Pavement Management Systems and reducing deflection survey frequency, with remarkable savings for road agencies.


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How to Cite

Bosurgi, G., Pellegrino, O., & Sollazzo, G. (2019). Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance. The Baltic Journal of Road and Bridge Engineering, 14(1), 58-79. https://doi.org/10.7250/bjrbe.2019-14.433