An Interpretable Pretrained Tabular Modeling Framework for Predicting IRI Across Multiple Pavement Structural Configurations

With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression models have limitations in capturing the coupled effects of traffic, structure, and environmental factors. To overcome this limitation, this study constructed a dataset containing 10,836 samples based on the Long-Term Pavement Performance (LTPP) database, integrating traffic load, pavement structure parameters, and climate variables. The variance inflation factor (VIF) and correlation analysis were used to validate the effectiveness of feature selection. We trained nine machine learning models and optimized the hyperparameters using a Bayesian optimization method with five-fold cross-validation to ensure good generalization ability. Results show that the TabPFN model, based on prior information, achieved the best overall performance with a coefficient of determination R2 = 0.9474 and a low prediction error (RMSE = 0.138) on the test set. Paired t-tests based on cross-validation further confirmed that TabPFN’s predictive performance is statistically superior to the baseline model. SHAP and generalized additive model (GAM) analyses indicate that traffic load is the main driver of IRI growth, while structural layer thickness, within a certain range, can mitigate pavement roughness. Climatic factors have indirect long-term effects through cumulative environmental exposure. Although the main drivers differ slightly among different pavement structures, traffic load consistently plays a dominant role. To enhance the model’s practical applicability, we also developed a user-friendly graphical interface (GUI) for fast and accurate IRI prediction.