摘要
控制苯乙烯-丁二烯嵌段共聚物(SBS)掺量是改善SBS改性沥青性能的重要手段。文章采用3大指标试验和红外试验,分析了SBS掺量对改性沥青的常规指标、红外特征峰的影响,研究了A 966/1377、A(966+699)/1377和A 699/1377变量因子与取样方式对标准曲线估算精度的影响。结果表明:不同掺量区间的SBS对改性沥青路用性能的影响程度是不同的;699 cm-1处特征峰是拟合标准曲线的最佳SBS特征峰,以A 699/1377为变量因子进行线性回归的标准曲线的估算精度最高,且SBS掺量试验结果的估算误差<2.5%;对于实际工程中改性沥青的SBS掺量检测,溶剂法取样是SBS掺量估算精度最高的取样方式,估算误差<5%。实际应用中推荐采用溶剂法取样。
Controlling the content of styrene-butadiene block copolymer(SBS)is an important means to improve the performance of SBS modified asphalt.Choosing the appropriate method of standard curve fitting and sample sampling is the key to estimate the SBS content of modified asphalt with high accuracy.Three major index tests and infrared tests were used to analyze the influence of SBS content on the conventional indexes and infrared characteristic peaks of modified asphalt.The influence of the variable factors of A-966/1377 and A-(966+699)/1377 and A-699/1377 and sampling methods on the estimation accuracy of the standard curve was studied.The results show that the influence of SBS in different dosage ranges on the road performance of modified asphalt is different.The characteristic peak at 699 cm-1 is the best SBS characteristic peak fitting the standard curve.The estimation accuracy of the standard curve for linear regression with A-699/1377 as the variable factor is the highest,and the estimated error of SBS blending test results is controlled to be less than 2.5%.For the SBS content detection of modified asphalt in solid engineering,solvent method sampling is the sampling method with the highest accuracy of SBS content estimation,and the estimation error can be controlled to less than 5%.In practical applications,the solvent method is recommended for sampling.
作者
田迎军
任瑞波
汲平
高宾
TIAN Yingjun;REN Ruibo;JI Ping;GAO Bin(Construction Management Company of Shandong High-speed Group Co.,Ltd.,Jinan 250000,China;School of Traffic Engineering,Shandong Jianzhu University,Jinan 250101,China;Shandong High-speed Engineering Inspection Co.,Ltd.,Jinan 250002,China)
出处
《山东建筑大学学报》
2020年第2期39-45,共7页
Journal of Shandong Jianzhu University
基金
山东省自然科学基金项目(ZR2018BEE042)。