Research on factors influencing high temperature deformation of asphalt mixture and prediction modeling

This study addresses the root causes of rutting defects by examining the influence of environmental temperature, load, water-heat coupling, and the maximum nominal particle size of aggregates on the permanent deformation of asphalt mixtures. Through testing rutting specimens under varying experimental conditions, the patterns of permanent deformation were analyzed. The results indicate that temperature, water-heat coupling, load, and aggregate nominal particle size are significant factors affecting rutting depth and dynamic stability, with load exerting the greatest impact, followed by water-heat coupling. Using machine learning algorithms, a predictive model was developed to estimate rutting depth and dynamic stability, enabling accurate evaluation of the high-temperature deformation capacity of asphalt mixtures.

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Publication Details

Journal
Frontiers in Materials
Published
2026-02-09
DOI
https://doi.org/10.3389/fmats.2026.1751897
Primary Topic
Asphalt Pavement Performance Evaluation
Type
article
Field-Weighted Citation Impact
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article

Research on factors influencing high temperature deformation of asphalt mixture and prediction modeling

Xinyi He, Richeng Huang, Defu Liao, Hongliu Rong et al.
Frontiers in Materials
Asphalt Pavement Performance Evaluation
article

Research on factors influencing high temperature deformation of asphalt mixture and prediction modeling

Xinyi He, Richeng Huang, Defu Liao, Hongliu Rong, Zhaonan Zuo, Kai Ye, Zhichen Hu
article en

Abstract

This study addresses the root causes of rutting defects by examining the influence of environmental temperature, load, water-heat coupling, and the maximum nominal particle size of aggregates on the permanent deformation of asphalt mixtures. Through testing rutting specimens under varying experimental conditions, the patterns of permanent deformation were analyzed. The results indicate that temperature, water-heat coupling, load, and aggregate nominal particle size are significant factors affecting rutting depth and dynamic stability, with load exerting the greatest impact, followed by water-heat coupling. Using machine learning algorithms, a predictive model was developed to estimate rutting depth and dynamic stability, enabling accurate evaluation of the high-temperature deformation capacity of asphalt mixtures.

Frontiers in MaterialsVol. 13
Guangxi University (CN), Zhejiang Energy Group (China) (CN)
Openalex Percentile: Top 63%
Asphalt Pavement Performance Evaluation
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Research on factors influencing high temperature deformation of asphalt mixture and prediction modeling — Xinyi He, Richeng Huang, et al. · Frontiers in Materials (2026) | TGRS Research Map | TGRS