基于灰色模型的沥青混合料低温指标预测
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内蒙古大学

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U414???????

基金项目:

内蒙古自然科学基金资助项目(2020MS01013);内蒙古交通科技资助项目(222000-5206034);国家自然科学基金资助项目(11562013)


Prediction of low temperature index of asphalt mixture based on grey model
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Inner Mongolia University

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Inner Mongolia Natural Science Foundation Support Project (2020MS01013); Inner Mongolia Transportation Science and Technology Support Project (222000-5206034); The National Natural Science Foundation of China Support Project (11562013)

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    摘要:

    为综合考虑沥青低温性能对沥青混合料低温性能的影响,并依据沥青及混合料低温性能实现对混合料低温性能的预测,采用弯曲梁流变试验、常规低温试验和TSRST测定5种沥青及混合料的低温评价指标,并进行灰色关联分析,建立分数阶灰色预测模型。结果表明:沥青低温连续分级温度、玻璃态转变温度、脆点、4种温度下的蠕变速率和耗散能比对混合料低温性能影响较大;冻断温度和转折点温度FGM(1,4)模型的误差分别为1.83%和3.71%,精度为优,预测结果合理。

    Abstract:

    In order to comprehensively consider the influence of low-temperature performance of asphalt on low-temperature performance of asphalt mixture, and predict low-temperature performance of asphalt mixture based on low-temperature performance of asphalt and mixture. The low temperature evaluation indexes of five kinds of asphalt and mixture were determined by bending beam rheological test, conventional low temperature test and TSRST. They were carried out grey correlation analysis, and were established fractional grey prediction model. The results show that the low temperature continuous classification temperature, glass transition temperature, brittle point, creep rate and dissipation energy ratio at four temperatures have great influence on the low temperature performance of asphalt mixture. The error of FGM (1,4) model for freezing temperature and turning point temperature is 1.83% and 3.71% respectively, indicating that the accuracy is excellent and the prediction result is reasonable.

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历史
  • 收稿日期:2022-11-25
  • 最后修改日期:2023-02-14
  • 录用日期:2023-03-07
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