Effects of Different Training Datasets on Machine Learning Models for Pavement Performance Prediction

With improvements in data collection, storage, and processing, machine learning (ML) is gaining momentum as a behavior prediction method in the field of engineering. Several studies have evaluated these algorithms' potential to predict pavement serviceability, however some challenges limit its use. Training data preprocessing has a great impact on the model's predictive performance, is highly dependent on the modeler's experience, and is not typically reported in engineering-related literature. The objective of this study was to assess the effects of data preprocessing, hyperparameter selection, and time series size on the model's evaluation metrics. Therefore, this paper analyzes the performance of three ML algorithms on maximum deflection (D-0) and international roughness index (IRI) prediction: support vector machine, random forest (RF), and artificial neural network (ANN). An R-2 and mean square error (MSE) analysis was conducted on 12 training datasets, with two sizes of historical data and five stages of data preprocessing. The results indicated that ANN was the most accurate technique with an R-2 of 0.99 and MSE of 20 x10(-3) mm on the D-0 prediction and an R-2 of 0.91 and MSE of 0.03 m/km on the IRI prediction. RF was also identified as an effective technique, generating similar results with less data preprocessing. The addition of structural and traffic categorical features to the training dataset resulted in the most significant improvement of the support vector regression and ANN performance metrics; the hyperparameter selection was effective only on IRI prediction, especially with the ANN algorithm.