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Chemical composition-driven performance evaluation model for sbs-modified asphalt using multivariate statistical.

Dahai Liu1,2, Jintao Li3, Huaicun Guo1,2

  • 1Gansu Province Transportation Planning , Survey & Design Institute Co , Ltd, Lanzhou, Gansu Province, 730030, China.

Scientific Reports
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

This study establishes a performance optimization model for modified asphalt by correlating its chemical composition with key performance indicators. The model effectively predicts modified asphalt performance, aiding in quality control.

Keywords:
Chemical compositionFactor theoryGrey correlation analysisPrincipal component analysisSBS modified asphalt

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Civil Engineering

Background:

  • Modified asphalt performance is intrinsically linked to its chemical makeup.
  • Effective quality control of modified asphalt relies on understanding the correlation between chemical composition and performance.

Purpose of the Study:

  • To develop a performance optimization model for modified asphalt using grey correlation theory and factor analysis.
  • To establish the correlation between the chemical components (asphaltene, resin, aromatic, saturated fractions) and performance indicators of SBS modified asphalt.

Main Methods:

  • Utilized rod-shaped thin-layer chromatography and Fourier transform infrared spectroscopy to determine chemical composition.
  • Conducted dynamic shear rheological tests to assess performance indicators like phase angle and complex shear modulus.
  • Applied grey correlation theory to establish correlations and factor analysis to develop the optimization model.

Main Results:

  • Identified strong correlations between chemical components and most performance indicators, with softening point correlating highest with asphaltene and resin, and phase angle with aromatic and saturated fractions.
  • Reduced complex performance indicators to two principal factors (F1 and F2) through factor analysis.
  • Validated the developed model using modified asphalt with varying SBS modifier dosages, confirming its feasibility.

Conclusions:

  • The established performance optimization model accurately assesses modified asphalt properties.
  • The model provides a feasible approach for quality control and performance prediction of modified asphalt based on its chemical composition.