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

Authors

  • Nicola Baldo
    Affiliation
    Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, Italy
  • Matteo Miani
    Affiliation
    Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, Italy
  • Fabio Rondinella
    Affiliation
    Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via del Cotonificio 114, 33100 Udine, Italy
  • Evangelos Manthos
    Affiliation
    Department of Civil Engineering, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece
  • Jan Valentin
    Affiliation
    Faculty of Civil Engineering, Czech Technical University, Thákurova 7, 166 29 Prague, Czech Republic
https://doi.org/10.3311/PPci.19996

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 Optimization

Citation data from Crossref and Scopus

Published 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

Issue

Section

Research Article