A multi-index evaluation system for tuff-asphalt mixtures exposed to long-term water damage based on the fractional grey prediction model

Yanxia Cai, Jiaming Zhao, Chenchen Li, Jiachen Shi and Baoxin Zhang

PLOS ONE, 2025, vol. 20, issue 7, 1-23

Abstract: Given the high susceptibility of tuff-asphalt mixtures to water damage, it is crucial to conduct a comprehensive evaluation of their post-damage performance across multiple performance metrics and integrate these indicators to assess the impact of different water-stability improvement methods on the overall performance of the mixture. This study enhances the long-term water stability of tuff asphalt mixtures by incorporating cement and anti-stripping agents, evaluating their performance under prolonged water exposure. A fractional grey prediction model (FGM) was employed to predict long-term behavior, while a comprehensive evaluation framework was developed using weighted averaging and relative scoring methods. Results demonstrate that both additives significantly improve water stability. Water damage leads to an increase in the viscous components and a decrease in the elastic recovery capacity of asphalt mixtures, which directly results in a decline in their high-temperature rutting resistance. The FGM exhibits high accuracy, with prediction errors below 3% compared to experimental data. The proposed evaluation system provides a practical reference for holistic performance assessment and alternative selection in engineering applications.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0327853

DOI: 10.1371/journal.pone.0327853

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