Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework

• Published in: CivilEng 2023-12, Vol.4 (4), p.1083-1097
• Main Authors: Baldo, Nicola, Rondinella, Fabio, Daneluz, Fabiola, Vacková, Pavla, Valentin, Jan, Gajewski, Marcin D., Król, Jan B.
• Format: Article
• Language: English
• Subjects: Algorithms; artificial neural network; Artificial neural networks; Asphalt mixes; asphalt mixture; Asphalt pavements; Business metrics; categorical boosting; Decision trees; Design; Flexible pavements; Goodness of fit; Limestone; Machine learning; Mathematical models; Mechanical properties; Neural networks; Parameters; Soft computing; Stiffness; stiffness modulus; Temperature
• ISSN: 2673-4109; 2673-4109
• DOI: 10.3390/civileng4040059

Stiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R2 = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented.