Published May 26, 2025
| Version v1
Dataset
Open
Dataset for Deep Learning - ANFIS configuration-study to analyze fracture resistance parameters of asphalt mixtures containing reclaimed asphalt within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K
Authors/Creators
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1.
Adana Science and Technology University
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2.
Cukurova University
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3.
Czech Technical University in Prague
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4.
Warsaw University of Technology
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5.
Ege University
Description
Summary:
Three types of asphalt concrete (AC) mixtures were investigated. They involved four grading types (AC 8, AC 11, AC 16, AC 22), three binder types (70/100, PMB 25/55–65, PMB 45/80–65), and three reclaimed asphalt contents (30%, 40%, 50%).
The set of data for properties of the asphalt mixtures includes nominal maximum aggregate size (NMAS), RA content (RA%), bulk density, maximum density, air void content, bitumen content, and Stiffness Modulus (E), fracture toughness (KIC), fracture energy (GF) and flexibility index (FI) were utilized.
The dataset includes:
Outcomes of the experimental carried out on AC 8, AC 11, AC 16, AC 22 mixtures:
01 HMA composition.csv
02 HMA volumetric properties.csv
03 HMA mechanical properties.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 "A comprehensive Deep Learning - ANFIS configuration-study to analyze fracture resistance parameters of asphalt mixtures containing reclaimed asphalt", which is available on https://doi.org/10.1016/j.conbuildmat.2025.141893
Files
01 HMA composition.csv
Additional details
Identifiers
Funding
- Czech Science Foundation
- GACR GA22-04047K
- National Science Centre
- (NCN) Weave-UNISONO 2021 2021/03/Y/ST8/00079