Interpretable Machine Learning‐Based Quantification of the Influence of As‐Constructed Air Voids on IRI of Asphalt Pavement

Abstract

Investigating the impact of as‐constructed air voids (AV s ) on the long‐term evolution of asphalt pavement International Roughness Index (IRI) through field tests is severely constrained by prohibitive monitoring costs. Furthermore, the complex coupling effects of environmental aging and traffic‐induced secondary compaction make it exceptionally challenging to isolate the specific contribution of AV s to IRI progression. Based on the Long‐Term Pavement Performance (LTPP) database, this study utilizes mainstream machine learning (ML) models to develop a comprehensive predictive framework for long‐term IRI evolution. A rule‐based data cleaning procedure guided by pavement deterioration theory was first implemented to eliminate physically inconsistent anomalous observations. Evaluation results demonstrated that this cleaning strategy effectively enhanced the validation performance across all candidate models, with the multilayer perceptron (MLP) model exhibiting the highest predictive accuracy and generalization capability. The Shapley additive explanations (SHAP) framework was introduced based on the selected MLP model to conduct interpretability analysis, verifying that the data‐driven feature mappings were consistent with established engineering principles. On this basis, a synthetic input matrix was constructed to quantitatively evaluate the model‐estimated marginal influence of AV s on long‐term IRI progression. Under the controlled baseline scenario, the model‐predicted influence of AV s on the IRI exhibits a non‐monotonic U‐shaped trend, with an AV s content of approximately 6% yielding the lowest predicted IRI. Ultimately, this study bridges the gap between methodological interpretability and practical application, providing a model‐based exploratory framework for compaction quality control in pavement construction.