Abstract
Asphalt pavement temperatures greatly influence on the bearing capacity and performance, especially in high-temperature season. The variation rules of pavement temperatures under the high-temperature range affect the design and maintenance management of the asphalt pavement, as well as the accurate prediction for pavement temperatures. However, asphalt pavement temperature is greatly affected by various strongly correlated environmental factors and cannot be measured directly or predicted effectively. In this project, temperature sensors were embedded in the pavement of in-service road to collect temperature data by continuous record measurement, and regression model was conducted by the partial least squares method through comprehensive analysis on the pavement temperature data and synchronously environmental data from local weather station measured in July 2013, July 2014, and July 2015. The quantitative relationships in high-temperature season between environmental factors and pavement temperature were determined, and a model was established to predict the temperature of asphalt pavement based on environmental data. The model was verified by the recorded data from July 1, 2016, to July 31, 2016, and the results indicated that the pavement temperature can be predicted accurately and reliably by the proposed model.
Highlights
Data AcquisitionThe sensors were embedded at the bottom of the top layer, the bottom of the bottom layer, and 5 cm below the base, respectively
Introduction of the Partial Least SquaresRegression Method
Tair is the air temperature 2 m above the ground, °C; N is the total cloud cover, %; U is the air relative humidity 2 m above the ground, %; Ff is the average wind speed 10–12 m above the ground 10 min before the observation, m/s; and RRR is the precipitation, mm
Summary
The sensors were embedded at the bottom of the top layer, the bottom of the bottom layer, and 5 cm below the base, respectively. This study, two sets of temperature sensors were embedded along the traveling lane with 12 m apart to improve the accuracy of the monitoring data, and the temperature of. All data of meteorological factors (including temperature, T; cloud cover, N; humidity, U; wind speed, Ff; and precipitation, RRR) in this study were from the China National Weather Service 54,511 (Beijing) Observation Station. Tair is the air temperature 2 m above the ground, °C; N is the total cloud cover, %; U is the air relative humidity 2 m above the ground, %; Ff is the average wind speed 10–12 m above the ground 10 min before the observation, m/s; and RRR is the precipitation, mm
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