Published March 20, 2025 | Version v1
Dataset Open

Dataset for the mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with Neural Network modelling within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K

Authors/Creators

  • 1. ROR icon University of Udine
  • 2. ROR icon Czech Technical University in Prague
  • 3. Warsaw University of Tachnology
  • 4. ROR icon 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 aggregate, a conventional 50/70 penetration grade bitumen, and limestone 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 main scope of this research was to compare analysis between different modelling approaches: conventional regressions, both linear and non-linear, and artificial neural networks.
 
 
The dataset includes:
 
Outcomes of the 4PBT experimental carried out on two types of asphalt concrete: NMAS16 and NMAS22 mixtures
  • Stiffness Modulus NMAS16.csv
  • Stiffness Modulus NMAS22.csv

Notes (English)

This research was conceptualised 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 "Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling", which is available at https://doi.org/10.7409/rabdim.025.001

Files

Stiffness Modulus NMAS16.csv

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Additional details

Identifiers

DOI
10.7409/rabdim.025.001

Funding

Czech Science Foundation
GACR GA22-04047K
National Science Centre
(NCN) Weave-UNISONO 2021 2021/03/Y/ST8/00079