Dataset for asphalt mixtures' stiffness modulus prediction using a machine-learning approach based on temperature and frequency conditions within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K

Summary:

One selected Asphalt Concrete AC22 mixture was investigated in the four-point bending test (4PBT) method for stiffness modulus. The mixture was prepared using aggregate, a conventional grade bitumen, and filler. Their stiffness moduli (SM) were determined while samples were exposed to loading frequencies from 0.1 to 50 Hz, and testing temperatures ranged from 0 to 30 °C. The laboratory results were used to train a neural model that had temperature and frequency as inputs and stiffness as output.

The dataset includes:

Outcomes of the 4PBT experimental carried out on AC22 mixture

Stiffness Modulus AC22 0°C.csv

Stiffness Modulus AC22 10°C.csv

Stiffness Modulus AC22 15°C.csv

Stiffness Modulus AC22 20°C.csv

Stiffness Modulus AC22 30°C.csv