Published July 22, 2024
| Version v1
Dataset
Open
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
Authors/Creators
-
1.
University of Udine
-
2.
Czech Technical University in Prague
-
3.
Warsaw University of Technology
- 4. Warsaw University of Tachnology
Description
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
Notes (English)
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
Files
Stiffness Modulus AC22 0°C.csv
Files
(820 Bytes)
| Name | Size | Download all |
|---|---|---|
|
md5:e5596172d966a82d0a4e94a052c507be
|
168 Bytes | Preview Download |
|
md5:0d6e7e197586ca8269bdc6913a100210
|
168 Bytes | Preview Download |
|
md5:abc856d420943665dba1090ed48f3a4d
|
166 Bytes | Preview Download |
|
md5:ea173735699f3271aaefcb7eb2c0bd53
|
160 Bytes | Preview Download |
|
md5:bc6f76122192d7f79fb89b08692266f6
|
158 Bytes | Preview Download |
Additional details
Identifiers
Funding
- Czech Science Foundation
- GACR GA22-04047K
- National Science Centre
- (NCN) Weave-UNISONO 2021 2021/03/Y/ST8/00079