Prediction model of temperature in different layers of asphalt pavement(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2017年06期
- Page:
- 24-30
- Research Field:
- 道路工程
- Publishing date:
Info
- Title:
- Prediction model of temperature in different layers of asphalt pavement
- Author(s):
- WANG kun; HAO Pei-wen
- 1. College of Civil Engineering, Shandong Jiaotong University, Jinan 250031, Shandong, China; 2. School of Highway, Chang’an University, Xi’an 710064, Shaanxi, China
- Keywords:
- road engineering; pavement temperature; prediction model; layer
- PACS:
- U416.2
- DOI:
- -
- Abstract:
- In order to define the internal temperature distribution law of asphalt pavement in Shandong district, this paper carried out the study by surface temperature of asphalt pavement and climate condition to predict the internal temperature of asphalt pavement, so as to further guide the asphalt pavement design. Taken Binzhou to Dezhou Freeway in Shandong as research object, the pavement temperature at different depths of asphalt pavement and the hourly climate data were gathered during different weather conditions. Temperature data of different weather conditions were analyzed by statistics method, and two kinds of temperature prediction models suitable for asphalt pavement and flexible base asphalt pavement were established. Prediction results of this model and BELLS3 temperature model were compared with the measured temperature to verify the accuracy of the proposed model. The results show that the asphalt temperature has a close relationship with air temperature, and it is more sensitive when the depth is close to pavement surface. The temperature distribution of different depths in asphalt pavement structure present significant differences, due to LSPM (large stone porous asphalt mixes) flexible base which mainly received heat from the surface of asphalt pavement. The temperature distribution of asphalt pavement appears obvious periodicity and approximate sine curve. The temperature distribution of LSPM flexible base is slightly flat with the change of time. Within the error range of ±1 ℃, the temperature prediction accuracy of the proposed model is 64.1%, while the accuracy of BELLS3 temperature model is only 54.8%, which indicates that the proposed temperature prediction model has higher prediction accuracy and practicability.
Last Update: 2017-12-18