Estimation of the XGBoost Regression Model Used in the Prediction of Pavement’s Mechanical and Geometrical Parameters Based on Static Interpretation of the FWD Test

The FWD is commonly used to conduct a non-destructive evaluation of the capacity of the pavement. The layered pavement is loaded locally by falling weight, and deflection is recorded at many points. Based on these results, if the pavement geometry is known, the mechanical properties of the pavement may be determined using the back-calculation approach. Analytical, numerical, or ML methods can be used for back-calculation. An analytical solution for a multi-layered structure leads to non-linear relationships for the thickness or stiffness of each layer, but provides an accurate solution. The other methods, like numerical or ML methods, are just approximation methods with different levels of accuracy. In this paper, the accuracy of the XGBoost ML regression model in predicting mechanical and geometrical pavement parameters was estimated. The database was generated from a static analytical solution of an axially symmetrical problem implemented in the form of JPav software and then explored by training regression models to predict the moduli and thickness of pavement layers. Two other databases were created using PCA (Principal Component Analysis) and FDM-like (Feature Difference Method) to compare models trained with the complete deflection database. The results showed that models trained with the complete deflection database had the best average prediction performance compared to the other two. In contrast, models trained with the database pre-processed by PCA showed a similar predicting performance to that of the previous models, but with a slight loss in precision. Models trained with the database pre-processed by the FDM-like approach exhibited excellent prediction on some features but performed worse on the rest. The primary objective of this work is to develop a model that enables the determination of pavement layer thickness and moduli from the deflections obtained in FWD tests. The analysis carried out allowed us to conclude that it is possible to obtain some pavement variables from the deflections, while others require a more sophisticated approach.