@misc{10481/106837,
year = {2025},
month = {8},
url = {https://hdl.handle.net/10481/106837},
abstract = {Accurately modelling and simulating the stiffness modulus of asphalt mixtures is essential
for reliable pavement design and performance prediction under varying environmental and
loading conditions. The preceding is commonly achieved through master curves, which
relate stiffness to loading frequency at a reference temperature. However, conventional
master curves face two primary limitations. Firstly, temperature is not treated as a state
variable; instead, its effect is indirectly considered through shift factors, which can introduce
inaccuracies due to their lack of thermodynamic consistency across the entire range of
possible temperatures. Secondly, conventional master curves often encounter convergence
difficulties when calibrated with experimental data constrained to a narrow frequency
spectrum. In order to address these shortcomings, this investigation proposes a novel
formulation known as the Thermo-Stiffness Integration (TSI) model, which explicitly
incorporates both temperature and frequency as state variables to predict the stiffness
modulus directly, without relying on supplementary expressions such as shift factors. The
TSI model is built on thermodynamics-based principles (such as Eyring’s rate theory and
activation free energy) and leverages the time–temperature superposition principle to create
a physically consistent representation of the mechanical behaviour of asphalt mixtures. This
manuscript presents the development of the TSI model along with its application in a case
study involving eight asphalt mixtures, including four hot-mix asphalts and four warmmix asphalts. Each type of mixture contains recycled concrete aggregates at replacement
levels of 0%, 15%, 30%, and 45% as partial substitutes for coarse natural aggregates. This
diverse set of materials enables a robust evaluation of the model’s performance, even under
non-traditional mixture designs. For this case study, the TSI model enhances computational
stability by approximately 4 to 45 times compared to conventional master curves. Thus, the
main contribution of this research lies in establishing a valuable mathematical tool for both
scientists and practitioners aiming to improve the design and performance assessment of
asphalt mixtures in a more physically realistic and computationally stable approach.},
organization = {Department of Science, Technology, and Innovation (COLCIENCIAS) - (“Research Project 745/2016, Contract 037-2017,
No. 1215-745-59105”)},
publisher = {MDPI},
keywords = {asphalt mixtures},
keywords = {master curves},
keywords = {mathematical modelling},
title = {A Novel Master Curve Formulation with Explicitly Incorporated Temperature Dependence for Asphalt Mixtures: A Model Proposal with a Case Study},
doi = {10.3390/infrastructures10090227},
author = {Martinez-Arguelles, Gilberto and Casas, Diego and Peñabaena-Niebles, Rita and Guerrero-Bustamante, Oswaldo and Polo-Mendoza, Rodrigo},
}