ABSTRACT

Results of Falling Weight Deflectometer (FWD) test are dependent upon pavement temperature and its changes daily or seasonally. It is crucial to have the temperature of pavement when FWD data are collected, and also to compare the FWD data for different temperatures. Conventional models for pavement temperature rely on parameters such as air temperature, solar radiation, wind speed, and humidity. However, the accuracy of these data might not be high when the pavement is far from meteorological stations. Thus, this study is investigating the applicability of using remote sensing technology for the estimation of pavement temperature. Satellite input data are used to develop three models as linear, non-linear and Artificial Neural Network (ANN) for depths of 20 and 25 cm and in different layers of asphalt pavement. The predicted temperatures are very close to the measured temperature(correlation coefficients 0.79–0.99). Moreover, the results of this model are compared with the BELLS3 model for Chosen sites in Raton Pass, Aguilar, and Schurz-Nevada, based on the data in IOWA State University in the United States. Nonetheless, the results of this model can be extended for similar conditions with hot weather, subtropical area and low vegetation.

摘要

落偏挠度计（FWD）测试的结果取决于路面温度及其每日或季节性变化。收集FWD数据时必须具有路面温度，并且比较不同温度下的FWD数据也至关重要。路面温度的常规模型取决于诸如空气温度，太阳辐射，风速和湿度的参数。但是，当人行道远离气象站时，这些数据的准确性可能不高。因此，本研究正在研究使用遥感技术估算路面温度的适用性。卫星输入数据用于开发三种模型，分别是线性，非线性和人工神经网络（ANN），用于20厘米和25厘米深度以及沥青路面的不同层。预测温度非常接近实测温度（相关系数0.79–0.99）。此外，根据美国IOWA州立大学的数据，将该模型的结果与Raton Pass，Aguilar和Schurz-Nevada的选择站点的BELLS3模型进行了比较。但是，该模型的结果可以扩展到炎热天气，亚热带地区和低植被的类似条件。