Published October 16, 2023
| Version v1
Dataset
Open
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
Creators
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1.
University of Udine
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2.
Czech Technical University in Prague
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3.
Warsaw University of Technology
Description
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
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 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
Files
AML16 Grading curves.csv
Files
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Additional details
Identifiers
Funding
- Czech Science Foundation
- GACR GA22-04047K
- National Science Center
- (NCN) Weave-UNISONO 2021 2021/03/Y/ST8/00079