# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
## 2 Previous Research
### [PERSON] et al. [8] further enhanced the capability of transfer functions to predict the fatigue performance of asphalt pavement using a gradient boost model. R programming language was used to develop the model. The damage index based on traffic volume, subgrade modulus, layer thickness, traffic volume and climatic conditions were obtained. The results showed a significant performance of prediction using the gradient boost model, with error decreased to 4.35%. However, in this research, the damage index was taken into account without any distress mechanism including rutting and fatigue cracking, thereby requiring the use of distress mechanism evaluation in future research.  
[PERSON] et al. [9] used a random forest algorithm for optimization of input variables for improvement in prediction accuracy of rutting prediction artificial neural network models in the US and Canada. Variables taken into account were pavement structural properties, climatic conditions and traffic conditions. Results showed good capability of the proposed model to predict rut depth progression, with the coefficient of determination at 0.932 and root mean square error of 1.108 mm. However, in this research, the number of cycles to fatigue damage were not included in the scope; therefore, in the current research, the fatigue damage evaluation based on the input data is performed.  
[PERSON] et al. [10] proposed a deep neural network model for rutting prediction of asphalt pavements with a minimum amount of input variables. The data for the deep neural network were extracted from a long-term pavement performance database. Performance of the DNN model was compared with that of the multivariate linear regression model. Results showed higher predictive performance of the model than conventional models. Furthermore, the proposed model could rank the input parameters based on their impact on rutting. However, in this research, the developed DNN did not incorporate the fundamentals of CatBoost and LightGBM for further optimizing the network's performance; therefore, in the current research, the number of folds is evaluated for each model type to increase its predictive performance.  
[PERSON] et al. [11] developed neural network-based models for the prediction of rutting, skid resistance, transverse cracking and surface distress. Mean impact values were employed to analyse the impact of input variables on different damage mechanisms by developing five different models based on the neural network. Results showed good capability of neural network models for prediction of pavement damage conditions, with a coefficient of determination of 0.8692. Furthermore, it was found that pavement age and maintenance type have the highest impact on putting regression in pavements.  
[PERSON] et al. [12] developed an ANN with four layers to analyse the forecast leading to development of rutting based on the input variables from the data, including traffic volume, climatic conditions, pavement characteristics and maintenance conditions. The random forest decision tree algorithm was used to compute feature importance in order to analyse its importance with the target. The outputs of the model were rut depth, wheel track width and cross-sectional area. Results showed that rutting was also affected by the thickness of the surface layer and voids in the asphalt mixture. Results concluded with the development of rutting evaluation index for scaling the rutting severity. Although different machine learning algorithms have been used in previously conducted research, in this research, a wide range of machine learning algorithms have been compared in terms of rutting and fatigue cracking, and furthermore, the correlation analysis of input and output variables has been performed.