Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach

Abstract The accurate prediction of the deterioration of Continuously Reinforced Concrete Pavement (CRCP) is essential for the effective management of pavements and the maintenance of infrastructure. In this study, a comprehensive approach that integrates descriptive statistics, correlation analysis, and machine learning algorithms is employed to develop models and predict punchouts in CRCP. The dataset used in this study is extracted from the Long-Term Pavement Performance (LTPP) database and contains a wide range of pavement attributes, such as age, climate zone, thickness, and traffic data. Initial exploratory analysis reveals varying distributions among the input features, which serves as the foundation for subsequent analysis. A correlation heatmap matrix is utilized to elucidate the relationships between these attributes and punchouts, guiding the selection of features for modeling. By employing the random forest algorithm, key predictors like age, climate zone, and total thickness are identified. Various machine learning techniques, encompassing linear regression, decision trees, support vector machines, ensemble methods, Gaussian process regression, artificial neural networks, and kernel-based approaches, are compared. It is noteworthy that ensemble methods such as boosted trees and Gaussian process regression models exhibit promising predictive performance, with low root mean square error (RMSE) and high R-squared values. The outcomes of this study provide valuable insights for the development of pavement management strategies, facilitating informed decision-making regarding resource allocation and infrastructure maintenance. Future research could focus on refining models, exploring additional features, and validating results through real-world implementation trials. This study contributes to advancing predictive modeling techniques for optimizing CRCP infrastructure management and durability.