Dataset used to develop soft computing models that predict the stiffness modulus of bituminous mixtures

Lee P Leon*, Hector Martin, Upaka Rathnayake, Portia Felix

*Corresponding author for this work

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Abstract

This data article presents information on the measurement of Indirect Tensile Stiffness Modulus of laboratory and field asphalt mixtures. The asphalt mixes are composed of three distinct binders that were categorised by their penetration grade (40/55-TLA, 60/75-TLA, and 60/70-MB) and aggregates (limestone, sharp sand, and filler). The asphalt mixtures are called dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA). The variables in the dataset were selected in accordance with the specifications of the dynamic modulus models that are currently in use as well as the needs for the quality control and assurance (QC & QA) assessment of asphalt concrete mixes. The data parameters included are temperature, asphalt content, and binder viscosity, air void content, cumulative percent retained on 19, 12.5, and 4.75 mm sieves, maximum theoretical specific gravity, aggregate passing #200 sieve, effective asphalt content, density, flow, marshal stability, coarse-to-fine particle ratio and the Indirect Tensile Stiffness Modulus (ITSM). Utilising soft computing techniques, models were developed utilising the data thus eliminating the requirement for complex and time-consuming laboratory testing. 
Original languageEnglish
Article number110382
Number of pages8
JournalData in Brief
Volume54
Early online date09 Apr 2024
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Indirect tensile stiffness modulus
  • Gene expression programming
  • Asphalt concrete
  • Pavement design
  • Pavement materials
  • Multi expression programming

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