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

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 conference paper titled "Asphalt Mixtures’ Stiffness Modulus Prediction Using a Machine-Learning Approach Based on Temperature and Frequency Conditions" which is available at https://doi.org/10.1201/9781003402541-107