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沥青混合料表面构造水平及分布特性预测模型

Prediction model of level and distribution of HMA surface texture
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摘要 为了在设计阶段评价和预测沥青混合料表面构造水平及其分布特性,运用提出的沥青混合料表面构造水平及分布特性二维图像测试方法,测试了具有不同设计参数的沥青混合料不同波长处的表面构造水平.研究了设计参数对沥青混合料表面构造水平的影响规律,结果表明,随着空隙率、集料粒径的增大,沥青混合料表面构造特征波水平值增大;而随着沥青饱和度、集料级配分形维数,及截面上单位面积内集料方向角正弦值、集料规则度、集料间接触长度的增加,沥青混合料表面构造特征波水平值降低.在此基础上,建立了基于设计参数的沥青混合料表面构造水平及分布特性预测模型,并通过试验检验了该预测模型的准确性,结果表明该模型可准确预测沥青混合料表面构造水平及其分布特性. To evaluate and predict the level and distribution of hot mixed asphalt(HMA)surface texture at design stage,the level of the surface texture at different wavelengths of HMA with different design parameters is tested by using the proposed2D image texture analysis method(ITAM).The influence law of design parameters to the HMA surface texture is studied.The results show that the level of characteristic wavelength of surface texture is improved as the air voids,aggregate size increase.However,it decreases with the increased voids filled with asphalt,fractal dimension of aggregate gradation,and sine of aggregate direction angle,regularity of aggregate,contacting length of aggregate in unit area of mixture section.On this basis,a prediction model of the level and distribution of HMA surface texture based on the design parameters is established.Experiments are designed to test the accuracy of the prediction model,and the results indicate that the model is a powerful and robust tool in predicting the level and distribution of HMA surface texture.
作者 陈德 韩森 苏谦 漆祥 Chen De;Han Sen;Su Qian;Qi Xiang(School of Civil Engineering , Southwest Jiaotong University , Chengdu 710064 , China;Key Laboratory of High-Speed Railway Engineering of Ministry of Education, Southwest Jiaotong University , Chengdu 710064 , China;Highway School,Chang’an University, Xi’an 710064,China;Key Laboratory for Special Area Highway Engineering of Ministry of Education,Chang’an University,Xi’an 710064,China)
出处 《东南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2017年第3期599-606,共8页 Journal of Southeast University:Natural Science Edition
基金 国家自然科学基金面上资助项目(51578076 5178467) 中央高校基本科研业务费科技创新资助项目(2682016CX009) 中央高校基本科研业务专项费"特殊地区公路工程教育部重点实验室"开放基金资助项目(310821171103)
关键词 道路工程 沥青混合料 表面构造 图像处理 预测模型 highway engineering hot mixed asphalt(HMA) mixture surface texture image analysis prediction model
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