|Table of Contents|

[1] Kong Lingyun, Zeng Qilan, Zhang Zhengqi, et al. Antiskid decay prediction of asphalt mixtures based on aggregate mechanical properties and gradation fractals [J]. Journal of Southeast University (English Edition), 2024, 40 (1): 58-67. [doi:10.3969/j.issn.1003-7985.2024.01.007]

Antiskid decay prediction of asphalt mixtures based on aggregate mechanical properties and gradation fractals()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2024 1
Research Field:
Traffic and Transportation Engineering
Publishing date:


Antiskid decay prediction of asphalt mixtures based on aggregate mechanical properties and gradation fractals
Kong Lingyun1 2 Zeng Qilan1 2 Zhang Zhengqi1 2 Peng Yi3 Wang Dawei4 Yu Miao1 2 Zhan You5
1National and Local Joint Laboratory of Traffic Civil Engineering Materials, Chongqing Jiaotong University, Chongqing 400074, China
2School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
3College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
4School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
5 School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
accelerated loading antiskid performance exponential model backpropagation neural networks(BPNN) support vector machine(SVM)
Through comprehensive data collection, along with the coarse aggregate mechanical index, fractal dimension, and British pendulum number(BPN), a pavement friction prediction model was proposed on the basis of backpropagation neural networks(BPNNs)and support vector machine(SVM). An accelerated attenuation test was conducted to examine the antiskid performance of the asphalt mixture and aggregates at different wearing cycles. Subsequently, BPN was fitted using an exponential model. Gray relational and correlation analyses were performed to evaluate the factors influencing pavement skid resistance. According to the principal component analysis results, six schemes were prepared for the training, validation, and testing of BPNN and SVM algorithms. Test results indicate that different aggregates exhibit different antiskid properties. Quartz sandstone is the most suitable, followed by basalt and limestone. The polished stone value has the highest correlation with the attenuation model of asphalt antiskid performance. BPNN is more stable, with an R2 value of approximately 0.8.


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Biographies: Kong Lingyun(1976—), female, doctor, professor; Peng Yi(corresponding author), male, doctor, dawsonyp@cqjtu.edu.cn.
Foundation items: National Natural Science Foundation of China(No. 52208425), Chongqing Postdoctoral Science Foundation(No. cstc2019jcyj-msxmX0744), China Postdoctoral Science Foundation(No. 2021M693918).
Citation: Kong Lingyun, Zeng Qilan, Zhang Zhengqi, et al.Antiskid decay prediction of asphalt mixtures based on aggregate mechanical properties and gradation fractals[J].Journal of Southeast University(English Edition), 2024, 40(1):58-67.DOI:10.3969/j.issn.1003-7985.2024.01.007.
Last Update: 2024-03-20