Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction

In this study a novel procedure is presented for an efficient development of predictive models of road pavement asphalt concretes mechanical characteristics and volumetric properties, using shallow artificial neural networks. The problems of properly assessing the actual generalization feature of a model and avoiding the effects induced by a fixed training-test data split are addressed. Since machine learning models require a careful definition of the network hyperparameters, a Bayesian approach is presented to set the optimal model configuration. The case study covered a set of 92 asphalt concrete specimens for thin wearing layers.

Automated summary

This study introduces a novel approach for developing predictive models of road pavement asphalt concretes using shallow artificial neural networks. The study addresses the challenges of assessing model generalization and avoiding the impact of fixed data splits. A Bayesian approach is presented to optimize the model configuration. The case study includes 92 asphalt concrete specimens for thin wearing layers.