Inverse-designed neural networks for rheological prediction of asphalt binders using SARA properties
Mahmoud Khadijeh (TU Delft - Civil Engineering & Geosciences)
Sophie Stüwe (Technische Universität Wien)
Johannes Mirwald (Technische Universität Wien)
Bernhard Hofko (Technische Universität Wien)
Lukas Eberhardsteiner (Technische Universität Wien)
Aikaterini Varveri (TU Delft - Civil Engineering & Geosciences)
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Abstract
Asphalt binders, critical for flexible pavement performance, owe their
viscoelastic properties to their complex chemical composition,
characterised by Saturates, Aromatics, Resins and Asphaltenes (SARA)
fractions. This study introduces a bidirectional neural network (BidiNN)
framework to predict and optimise the rheological behaviour of asphalt
binders based on their SARA composition. The forward model maps chemical
inputs, including SARA fractions and environmental conditions, to
rheological outputs such as complex modulus (𝐺∗) and phase angle (𝛿), achieving high accuracy (𝑅2>0.965).
The inverse model infers optimal SARA fractions to achieve target
viscoelastic properties, addressing the non-linear interplay between
chemical constituents and mechanical performance. Cycle-consistency
losses ensure robust bidirectional predictions, while an ensemble
approach captures the non-uniqueness of the inverse problem, generating
multiple chemically viable SARA compositions. Chemical characterization
separates the binder into SARA fractions. Asphaltenes are first
precipitated and removed by filtration, after which the resulting
maltene fraction is separated by Solid Phase Extraction (SPE) into
saturates, aromatics, and resins.. Aging processes, monitored by
Fourier‑Transform Infrared (FTIR) spectroscopy, shift SARA distributions
toward higher asphaltene content, impacting binder durability. The
BidiNN framework enables precise tailoring of chemical compositions for
enhanced pavement performance and sustainability.
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