###### Abstract  
This study proposes an explainable extreme gradient boosting (XGBoost) model for predicting the international roughness index (IRI) and identifying the key influencing factors. A comprehensive dataset integrating multiple data sources, such as structure, climate and traffic load, is constructed. A voting-based feature selection strategy is adopted to identify the key influencing factors, which are used as inputs for the prediction model. Multiple machine learning (ML) models are trained to predict the IRI with the constructed dataset, and the XGBoost model performs the best with the coefficient of determination (\(R^{2}\)) reaching 0.778. Finally, interpretable techniques including feature importance, Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are employed to reveal the mechanism of influencing factors on IRI. The results demonstrate that climate conditions and traffic load play a critical role in the deterioration of IRI. This study provides a relatively universal perspective for IRI prediction and key factor identification, and the outputs of the proposed method contribute to making scientific maintenance strategies of roads to some extent. p +
Footnote †: journal: Applied sciences  
article  
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have been widely adopted to predict pavement performance. Support vector machines (SVMs), decision trees and Gaussian process regression (GPR) are presented as basic ML algorithms for pavement roughness prediction. [PERSON] et al. [15] developed a hybrid gray relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. Linear regression, optimized trees and optimized GPR are used for predicting IRI [16]. [PERSON] et al. [17] employed the least absolute shrinkage and selection operator (Lasso) and ridge regression, SVR and regression trees to estimate reliable IRI. Recently, ensemble learning methods have offered enhanced capabilities of processing high-dimensional data, effectively solving nonlinear problems. The random forest (RF) algorithm has been extensively employed as a robust approach for IRI prediction [18; 19]. [PERSON] et al. [20] constructed an RF model to fit the principles for the potential deterioration of the typical flexible pavement. [PERSON] et al. [18] developed a random forest regression (RFR) model and found that the initial IRI is the most important factor influencing the development of IRI. At the same time, they observed that cracks, ruts, average annual precipitation and service life also have significant impacts on IRI. In addition, the emergence of boosting ensemble learning methods, including gradient boosting decision trees (GBDTs) and XGBoost, has further improved the nonlinear processing capabilities of ML models. Based on XGBoost, [PERSON] et al. [21] evaluated the effectiveness of four preventive maintenance treatments on asphalt pavement roughness and revealed that the annual average traffic volume contributed the most to IRI, followed by precipitation, rut depth and temperature. [PERSON] et al. [22] also developed an XGBoost model for IRI prediction and found that hydraulic conductivity and the average of equivalent single-axle loads have marked impacts on IRI. [PERSON] et al. [23] conducted a study for IRI prediction of continuous reinforced concrete pavements and showed that the initial IRI value was the most critical factor, followed by site coefficients, the number of transverse cracks and pavement repairs. Overall, ML algorithms can be used to improve model prediction accuracy. An AdaBoost regression (ABR) model was developed to improve the prediction ability of IRI, with an \(R^{2}\) value of 0.9751 [24]. Although ML models exhibit great computational performance, their typically shallow structures limit their predictive capabilities. Deep learning (DL) algorithms possess strong multi-dimensional data extraction capabilities, enabling them to effectively capture nonlinear relationships within data and exhibit excellent predictive capabilities.