基于统计回归法的沥青路面温度场模型

Asphalt Pavement Temperature Field Model Based on Statistic Regression Method

在实测31 820组路面温度数据和5 554组气象数据的基础上,研究路面温度场预估模型,重点解决以往模型在较深处预估精度不足的问题。通过采用分段函数形式、考虑不同深度处路面温度受环境因素影响延后、引入深度衰减因子等手段得到了新的路面温度场模型。提出了不同深度处气温和太阳辐射对路面温度影响的延后时间,二者分别为0～4 h和2～4 h,层位越深则延后越长;推荐温度沿深度的衰减因子采用H-i(i=1,2,3)。新模型预估温度与实测结果相关系数达到0.92,在较深处(10 cm以下)仍然具有较好的精度。

An asphalt pavement temperature prediction model was studied based on 31 820 pavement temperature and 5 554 meteorological data to enhance the precision of temperature prediction in deep layer of pavement.A new pavement temperature prediction model was established by using segmented function and introducing depth attenuation factor,considering phase difference of pavement temperature in different depth influenced by environment factors.The delay time of air temperature and solar radiation influencing on pavement temperature was recommended,which are 0-4 h and 2-4 h respectively.The deeper the layer,the longer the delay.H-i was recommended as the depth attenuation factor of pavement temperature.The correlation coefficient between predicted temperature and measured temperature is 0.92,which has good precision below 10 cm depth.