Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K

Summary:

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). 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.

The dataset includes:

Characteristics of bituminous binder, CSV raw data

bituminous binder.csv

Grading curves of tested asphalt mixtures

AML16 Grading curves.csv

AMP22 Grading curves.csv

Volumetric characterizations of AML16 and AMP22 mixtures

AML16 Volumetric characterizations.csv

AMP22 Volumetric characterizations.csv

Outcomes of the 4PBT experimental trial carried out on AML16 and AMP22 mixtures

AML16 Stiffness Modulus 4PB.csv

AMP22 Stiffness Modulus 4PB.csv