a machine learning algorithm for the prediction of the viscoelastic properties of asphalt mixtures

The dynamic modulus |e*| and phase angle f are the most important properties for the viscoelastic characterization of asphalt mixtures. their experimental determination requires time-consuming procedures and expensive laboratory equipment. hence, different prediction procedures have been developed for the estimation of these rheological properties, with witczak and hirsch models being the most widely accepted. nowadays machine learning (ml) techniques are applied to various engineering problems because of their abilities in data processing, optimization and estimation.  this paper proposes the k-nearest neighbors algorithm as an ml method for the prediction of the viscoelastic properties of asphalt mixtures. the training and validation of the algorithm was based on a database containing the bitumen characteristics, volumetric properties and dynamic modulus and phase angle values at different frequencies and temperatures with more than 5500 data points. the obtained results indicate that the ml algorithm developed in this study is accurate and it could be an effective approach to predict the viscoelastic properties of asphalt mixtures.