Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track

Wang, Yu; Yan, Jiaojiao; Huang, Wei; Rutkowski, Leszek; Cao, Jinde

doi:10.1007/s11432-022-3647-7

Cited by 7 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [35] proposed a variable-order fractional Burgers rutting model by fitting the test data of RIOHTrack. In the paper, rutting can be quantitatively described as the time-varying mechanical deformation, considering The detected rutting tends to peak during the summer months.…”

Section: Comparison With a Mechanistic-empirical Modelmentioning

confidence: 99%

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

Cheng,

Wang,

Zhou

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R2 = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.

show abstract

Section: Comparison With a Mechanistic-empirical Modelmentioning

confidence: 99%