Solar heating reflective coating layer (SHRCL) to cool the asphalt pavement surface

2017 ◽  
Vol 139 ◽  
pp. 355-364 ◽  
Author(s):  
Aimin Sha ◽  
Zhuangzhuang Liu ◽  
Kun Tang ◽  
Pinyi Li
Coatings ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1065
Author(s):  
Nanxiang Zheng ◽  
Junan Lei ◽  
Shoubin Wang ◽  
Zhifeng Li ◽  
Xiaobao Chen

To reduce the temperature of asphalt pavement in summer, and alleviate the urban heat island effect, a comprehensive method of combining a heat reflective coating and large void asphalt pavement was proposed. Using the developed coating cooling test equipment, the cooling effect of the coating on a large void asphalt mixture was studied in six different proportions, four different colors, and four different dosages, and the durability of the coating was verified by abrasion tests. Finally, the best dosage of the coating was recommended through an adhesion test of the coating, and a water permeability and anti-skid performance test of the pavement. The results show that the reflectivity of the coating can be improved by adding functional fillers, of titanium dioxide and floating beads, into the coating. The order by reflectivity and cooling effect of the four color coatings was green > red > gray > blue, and the maximum cooling value of the green coating reached 9.7 ℃. The cooling performance of the coating decreased with the increase of wear time, and the rate of decrease was fast, then slow, and finally tended to be stable after 20,000 times wear. The coating reduced the anti-skid performance and the water permeability coefficient of large void asphalt pavement, but still maintained a high level. The green coating with 15% titanium dioxide and 10% floating beads is recommended as the cooling coating for large void asphalt pavement, and its dosage should be controlled at about 0.4–0.8 kg/m2.


Author(s):  
Zhaoyun Sun ◽  
Xueli Hao ◽  
Wei Li ◽  
Ju Huyan ◽  
Hongchao Sun

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a Genetic-Algorithm-Improved Neural Network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover missing data and perform denoising. In particular, these data were filled using cubic-spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed via modification of the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.


2021 ◽  
Vol 2044 (1) ◽  
pp. 012022
Author(s):  
Nanxiang Zheng ◽  
Zhifeng Li ◽  
Zebin Li ◽  
Qunbao Fan ◽  
Jianpeng Deng

Author(s):  
Sajad Ranjbar ◽  
Fereidoon Moghadas Nejad ◽  
Hamzeh Zakeri ◽  
Amir H. Gandomi

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