融雪盐对沥青混合料的腐蚀作用研究-《郑州大学学报(工学版)》

文章信息/Info

Author(s):: Zhang Wengang; Wang Fang; Ding Longting;; School of Construction Engineering, Shandong University of Technology; School of Physics and Optoelectronics Engineering, Shandong University of Technology; Highway College of Chang’an University;

Keywords:: Corrosion; melted snow salt; asphalt mixture; dry and humid cycle; estimates

摘要:: 中国北方冬季广泛使用的氯化钠融雪盐在有效融雪的同时也对沥青路面产生了较为严重的腐蚀作用.本文配制了不同浓度的融雪盐溶液，分别对SMA-13、AC-13和GFC-13等不同级配的沥青混合料进行腐蚀试验，探究在不同融雪盐浓度及不同干湿循环腐蚀作用下，沥青混合料的动稳定度、破坏应变、残留稳定度、冻融劈裂残留强度百分比等指标腐蚀规律，并依据试验结果对沥青混合料遭受融雪盐腐蚀作用后残留性能的百分比进行了预估研究.试验结果表明：随着融盐溶液浓度和干湿循环腐蚀次数的增加，沥青混合料的DS、εB 、MS’’、TSR均呈现出较大程度的减小，融雪盐对沥青混合料腐蚀作用严重.同等条件下，OGFC比SMA及AC更易遭受融雪盐的腐蚀.融雪盐浓度、干湿循环腐蚀次数与沥青混合料路用性能符合模型P=Te(a*C3+b*C2+c*C+d).

Abstract:: Snowfall in northern of China was often, melting snow salt, which was mainly composed of sodium chloride, was widely applied to remove snow. And at the same time, it had a serious corrosion effect on the asphalt pavement. In this paper, melting snow salt was used to prepare different concentrations of solution, and different types of asphalt mixtures, such as Stone Matrix Asphalt (SMA-13), Asphalt Concrete (AC-13) and Open-graded Friction Courses (OGFC-13) were tested under different corrosion conditions - different concentration of melting salt solution and the drying-watering cycle times. The Dynamic stability (DS), Residual marshall stability (MS’’), Freeze-Thaw splitting strength ratio (TSR), and Maximum flexural-tensile strain (εB) were tested after corrosion, the change rules were analysed also. The residual pavement performance percentage of asphalt mixtures were predicted at last. The results indicated that as the concentration of melting salt solution and the drying-watering cycle times increased, the performances of asphalt mixtures were all became smaller and smaller, the corrosion effect of melting snow salt on asphalt mixture was serious, especially to OGFC. And the residual pavement performances percentage of asphalt mixtures could be well predicted by the prediction formula P=Te*(a*C3+b*C2+c*C+d).