Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction
Abstract
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.
Keywords:
thin surface layer, mix design, stiffness modulus, machine learning, Bayesian OptimizationPublished Online
2022-09-28
How to Cite
Baldo, N., Miani, M., Rondinella, F., Manthos, E., Valentin, J. “Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction”, Periodica Polytechnica Civil Engineering, 66(4), pp. 1087–1097, 2022. https://doi.org/10.3311/PPci.19996
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Section
Research Article