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

This research was conceptualized and developed as part of activities related to project GA22-04047K, funded by The Czech Scientific Foundation (GACR), and project No. 2021/03/Y/ST8/00079, funded by the Polish National Science Centre (NCN) under the Weave-UNISONO 2021. The dataset was used for analyses for the journal paper titled "Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework", which is available on https://doi.org/10.3390/civileng4040059