TY - JOUR
T1 - Dataset used to develop soft computing models that predict the stiffness modulus of bituminous mixtures
AU - Leon, Lee P.
AU - Martin, Hector
AU - Rathnayake, Upaka
AU - Felix, Portia
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/6
Y1 - 2024/6
N2 - 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.
AB - 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.
KW - Asphalt concrete
KW - Gene expression programming
KW - Indirect tensile stiffness modulus
KW - Multi expression programming
KW - Pavement design
KW - Pavement materials
UR - http://www.scopus.com/inward/record.url?scp=85189934534&partnerID=8YFLogxK
U2 - 10.1016/j.dib.2024.110382
DO - 10.1016/j.dib.2024.110382
M3 - Article
AN - SCOPUS:85189934534
SN - 2352-3409
VL - 54
JO - Data in Brief
JF - Data in Brief
M1 - 110382
ER -