# Predicting Putting Development Using Machine Learning Methods Based on RIOCHTrack Data
## 4 Conclusions  
To enhance the accuracy of predicting rutting progression curves for various commonly constructed asphalt pavements in China, this paper utilized five machine-learning methods and data from 19 representative asphalt pavement sections of RIOHTrack. Climate, axle load, the performance of the surface mixture, the thickness of the base and subbase, and modulus, among others, are selected as input variables to train the model. Through careful analysis and discussion of the obtained results, the following conclusions were drawn:  
All ML models established in this paper are able to predict rutting well when the hyperparameters are searched thoroughly. Among them, the tree-based ML models such as GBDT, RF, and Extra Trees were proved to have a better prediction ability than SVR (R\({}^{2}\) = 0.9346) and ANN (R\({}^{2}\) = 0.9277).  
Compared with M-E models, the GBDT model used in the paper provides a high accuracy without any local calibration. More importantly, it can capture the season trend of rutting development. It is also suitable for all structures of RIOHTrack.  
After further calculating the SHAP value of the variable, we noticed that the axle load is the core factor affecting rutting. The effect of temperature on rutting is not as obvious as  
Figure 12: Global interpretation by SHAP based on GBDT.  
Figure 13: Interpretation of rutting prediction for one sample.  
imagined. In addition, for pavement performance, increasing the strength of the base is beneficial for reducing rutting.  
The \"Design Specification for Asphalt Pavement of Highways\" provides corresponding requirements for the allowable rutting deformation of asphalt mixture layers. When the rut depth exceeds the allowable value, timely maintenance measures should be taken. Accurate prediction of rutting depth can take effective control measures for asphalt pavement rutting, thereby improving the highway performance extending the service life of highways, and providing a maintenance reference for asphalt pavement.  
This research is mainly based on data mining and statistical methods. Further research should be conducted to apply more mechanism analyses of influence factors on rutting. At present, modeling data are insufficient. In the future, the model will be further adjusted and optimized as the RIOHTRack long-term rutting observation data increases. In addition, the machine learning model proposed in this article should be applied to predict other performances of RIOHTrack, such as cracking and skid resistance. We can also introduce these predicted pavement performance indicators into asphalt mixture mix design.  
Conceptualization, L.W.; methodology, C.C. and X.Z.; software, C.C.; validation, C.C.; formal analysis, X.Z.; investigation, C.C.; resources, X.W.; data curation, C.C.; writing--original draft preparation, C.C.; writing--review and editing, X.Z.; visualization, X.Z.; supervision, L.W. and X.W.; project administration, X.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from the Research Institute of Highway Ministry of Transport and are available from the authors with the permission of the Research Institute of Highway Ministry of Transport.  
The authors declare no conflict of interest.