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季冻区玄武岩纤维复合高粘改性沥青性能评价 被引量:4

Performance evaluation of basalt fiber high-viscosity composite modified asphalt in seasonal frozen region
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摘要 针对玄武岩纤维和高粘改性剂对沥青性能产生影响的问题,基于中国东北季冻区气候条件,对两种改性材料设计16组复合改性沥青掺配方案,进行不同掺配比下的多指标试验,根据复合材料理论对复合改性沥青粘度指标建立增粘预测模型.通过熵值组合权评价法对沥青多指标性能进行综合评判,选出两种改性材料的最佳掺配比.结果表明,两种改性材料的加入对沥青性能具有提升作用,增粘预测模型能够预测复合改性沥青在任意掺配比下的粘度.评价法计算出方案15可得到最高综合评分,推荐的最佳掺配比与试验结果一致. Aiming at the influence of basalt fiber and high viscosity modifier on asphalt performances,16 groups of blending schemes of composite modified asphalt were designed for these two materials according to the climatic conditions of seasonal frozen region in northeast China.The multi-index experiments were carried out under different blending ratios.Prediction model for the viscosification of composite modified asphalt was established according to composite material theory.The entropy-combination weight evaluation method was used to comprehensively evaluate the multi-index performances of asphalt,and the best mixing ratio of two modifying materials was selected.The results show that the addition of two modifying materials can improve the performances of asphalt,and the viscositying prediction model can predict the viscosity of composite modified asphalt at arbitrary blending ratios.Scheme 15 is identified by the evaluation method to get the highest comprehensive score,and the recommended optimum mixing ratio is consistent with the experimental results.
作者 于保阳 刘美鸥 孙宗光 YU Bao-yang;LIU Mei-ou;SUN Zong-guang(School of Transportation Engineering,Dalian Maritime University,Dalian 116026,China;School of Traffic Engineering,Shenyang Jianzhu University,Shenyang 110168,China;First Construction Co.Ltd.,China Railway No.9 Group Co.Ltd.,Suzhou 215299,China)
出处 《沈阳工业大学学报》 CAS 北大核心 2022年第4期473-480,共8页 Journal of Shenyang University of Technology
基金 辽宁省教育厅一般项目(lnjc202014).
关键词 季冻区 玄武岩纤维 高粘改性剂 复合改性沥青 沥青性能 复合材料理论 增粘预测 熵值组合权 seasonal frozen region basalt fiber high viscosity modifier composite modified asphalt asphalt performance composite material theory viscositying prediction entropy-combination weight
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