The rutting model of semi-rigid asphalt pavement based on RIOHTRACK full-scale track

Bo Kou; Jinde Cao; Wei Huang; Tao Ma; Bo Kou; Jinde Cao; Wei Huang; Tao Ma

doi:10.3934/mbe.2023353

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 8124-8145. doi: 10.3934/mbe.2023353

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The rutting model of semi-rigid asphalt pavement based on RIOHTRACK full-scale track

Bo Kou ¹,
Jinde Cao ^{1,2
,
,},
Wei Huang ³,
Tao Ma ⁴

1.
School of Mathematics, Southeast University, Nanjing 210096, China
2.
Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea
3.
Intelligent Transportation System Research Center, Southeast University, Nanjing 210096, China
4.
School of Transportation, Southeast University, Nanjing 210096, China

Received: 11 January 2023 Revised: 07 February 2023 Accepted: 13 February 2023 Published: 27 February 2023

Semi-rigid asphalt pavement has a wide range of application cases and data bases, and rutting is a typical failure mode of semi-rigid asphalt pavement. The establishment of an accurate rutting depth prediction model is of great significance to pavement design and maintenance. However, due to the lack of perfect theoretical system and systematic research data, the existing rutting prediction model of semi-rigid asphalt pavement is not accurate. In this paper, machine learning and mechanical-empirical model are combined to study the feature selection affecting the rutting evolution and rutting depth model of semi-rigid asphalt pavement. First, the particle swarm optimization random forest model is used to select the important features that affect the evolution of rutting depth. Second, the R-F model based on important features is proposed for the first time, which is compared with modification of rutting model in the Chinese Specifications for Design of Highway Asphalt Pavement (JTG D50-2017) and R-B model based on the improved Burgers model. The results show that the R-F model has more accurate prediction ability and better generalization ability, and it does not need complex data preprocessing and noise reduction. Here, the machine learning method is introduced to analyze the data characteristics, and the R-F rutting depth prediction model framework is innovatively proposed, which greatly improves the applicability and accuracy of the existing model framework.
- semi-rigid asphalt pavement,
- rutting depth prediction model,
- feature selection,
- R-F model,
- random forest
Citation: Bo Kou, Jinde Cao, Wei Huang, Tao Ma. The rutting model of semi-rigid asphalt pavement based on RIOHTRACK full-scale track[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 8124-8145. doi: 10.3934/mbe.2023353

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Abstract

Semi-rigid asphalt pavement has a wide range of application cases and data bases, and rutting is a typical failure mode of semi-rigid asphalt pavement. The establishment of an accurate rutting depth prediction model is of great significance to pavement design and maintenance. However, due to the lack of perfect theoretical system and systematic research data, the existing rutting prediction model of semi-rigid asphalt pavement is not accurate. In this paper, machine learning and mechanical-empirical model are combined to study the feature selection affecting the rutting evolution and rutting depth model of semi-rigid asphalt pavement. First, the particle swarm optimization random forest model is used to select the important features that affect the evolution of rutting depth. Second, the R-F model based on important features is proposed for the first time, which is compared with modification of rutting model in the Chinese Specifications for Design of Highway Asphalt Pavement (JTG D50-2017) and R-B model based on the improved Burgers model. The results show that the R-F model has more accurate prediction ability and better generalization ability, and it does not need complex data preprocessing and noise reduction. Here, the machine learning method is introduced to analyze the data characteristics, and the R-F rutting depth prediction model framework is innovatively proposed, which greatly improves the applicability and accuracy of the existing model framework.

References

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