沥青路面表面纹理重构与构造深度预测模型  

Surface Texture Reconstruction and Mean Texture Depth Prediction Model of Asphalt Pavement

作　　者：阳恩慧[1,2] 陈强[1,2] 李杰[1,2] 狄海波 黄兵 邱延峻[1,2] YANG En-hui;CHEN Qiang;LI Jie;DI Hai-bo;HUANG Bing;QIU Yan-jun(School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,Sichuan,China;Highway Engineering Key Laboratory of Sichuan Province,Southwest Jiaotong University,Chengdu 610031,Sichuan,China;Sichuan Tibet Expressway Co.Ltd.,Chengdu 610041,Sichuan,China)

机构地区：[1]西南交通大学土木工程学院,四川成都610031 [2]西南交通大学道路工程四川省重点实验室,四川成都610031 [3]四川藏区高速公路有限责任公司,四川成都610041

出　　处：《中国公路学报》2023年第6期14-23,共10页China Journal of Highway and Transport

基　　金：国家自然科学基金项目(52278460,52178438);四川省交通科技项目(2019-ZL-12);四川省科技计划项目(21YYJC3270)。

摘　　要：为了实现通过调整混合料的级配设计来获得期望的路面平均构造深度的目标,采用高精度三维激光扫描技术,采集了AC、SMA、OGFC三种典型级配的沥青混合料试件表面纹理特征信息。通过邻域插值法对采样数据的异常值和离群值进行替换,并通过均值滤波对采样数据进行降噪处理后,三维重构了试样表面;在采用傅里叶变换得到重构表面频域信息的基础上,根据宏观纹理的波长对应的频率设计带通滤波器,从重构表面中分离并提取出了路面宏观纹理。应用蒙特卡罗算法计算了路面的平均构造深度,通过采用筛上质量比-粒径积同时考虑了混合料的粒径和筛孔通过率对平均构造深度的影响。采用多元线性回归、随机森林和人工神经网络的方法,建立筛上质量比-粒径积与平均构造深度的预测模型,研究了混合料级配对沥青路面平均构造深度的影响。研究结果表明:均值滤波在去除噪声信号的同时也比较完整地保留了高程轮廓特征,三维重构的试样表面特征与原始表面特征一致;平均构造深度会受到级配曲线中除最大公称粒径外的其他粒径及筛孔通过率的影响;通过多元线性回归、随机森林和人工神经网络3种模型建立了以各筛孔尺寸的筛上质量比-粒径积为自变量,平均构造深度为因变量的回归模型,得到的预测值与实测值的决定系数R2在0.95以上。To achieve the desired pavement mean texture depth by adjusting the gradation design of an asphalt mixture,high-precision three-dimensional laser scanning technology was used to collect the surface texture feature information of three typical gradation asphalt mixture specimens:Asphalt Concrete,Stone Matrix Asphalt,and Open Graded Friction Course.After the exception values and outliers were processed by neighborhood interpolation and the sampled data was denoised by mean filtering,the sample surface was reconstructed in three dimensions.A band-pass filter was designed according to the frequency corresponding to the wavelength of the macro texture based on the frequency domain information of the reconstructed surface obtained by the Fourier transform;the macro texture of the pavement was separated and extracted from the reconstructed surface.A Monte Carlo algorithm was used to calculate the mean texture depth of the pavement.The influence of the mixture particle size and passing rate of the sieve on the mean texture depth was considered by using the product of the mass ratio on the sieve and particle size.The prediction models of the product of the mass ratio on sieve and particle size,and the mean texture depth were established using multiple linear regression analysis,random forest,and artificial neural networks;the influence of the mixture gradation on the mean texture depth of the asphalt pavement was studied.The results show that mean filtering not only removes the noise signal but also retains the elevation profile features.The surface features of the three-dimensional reconstructed specimen are consistent with the original surface features.The mean texture depth is affected by other particle sizes in the grading curve,except for the maximum nominal particle size and passing rate of the sieve.The regression model was established using multiple linear regression,random forest,and an artificial neural network,which takes the product of the mass ratio and particle size on the sieve of each mesh size as the inde

关 键 词：道路工程 平均构造深度 蒙特卡罗算法 沥青路面 宏观纹理 级配曲线 

分 类 号：U416.22[交通运输工程—道路与铁道工程]