Pavement performance prediction is necessary for road maintenance and repair (M&R) management and plans. The accuracy of performance prediction affects the allocation of maintenance funds. The international roughness index (IRI) is essential for evaluating pavement performance. In this study, using the road pavement data of LTPP (Long-Term Pavement Performance), we screened the feature parameters used for IRI prediction using the mean decrease impurity (MDI) based on random forest (RF). The effectiveness of this feature selection method was proven suitable. The prediction accuracies of four promising prediction models were compared, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). The two integrated learning algorithms, GBDT and XGBoost, performed well in prediction. GBDT performs best with the lowest root mean square error (RMSE) of 0.096 and the lowest mean absolute error (MAE) of 6.2% and the coefficient of determination (R2) reaching 0.974. However, the prediction accuracy varies in numerical intervals, with some deviations. The stacking fusion model with a powerful generalization capability is proposed to build a new prediction model using GBDT and XGBoost as the base learners and bagging as the meta-learners. The R2, RMSE, and MAE of the stacking fusion model are 0.996, 0.040, and 1.3%, which further improves the prediction accuracy and verifies the superiority of this fusion model in pavement performance prediction. Besides, the prediction accuracy is generally consistent across different numerical intervals.
Considering traditional methods' low accuracy and machine learning methods' lack of interpretability, this paper proposed a pavement performance model for IRI prediction based on XGBoost, and introduced SHAP to enhance the interpretability of individual features of the model. The data used are from the America LTPP data. Firstly, data cleaning and preprocessing were conducted. Secondly, 4 prediction models were built based on classical algorithms, namely, LightGBM, XGBoost, SVM, and multiple linear regression. Then, by comparison, it was found that XGBoost performed better. Finally, parameter tuning for this model was performed, with the RMSE as 0.317, MAE as 0.219, and R 2 as 0.742. In addition, considering the prediction model's lack of transparency, SHAP is utilized to perform the feature importance analysis and identify the main factors affecting the pavement performance, which can help the highway sector to improve the reliability of their subsequent prediction model analysis.
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