Machine learning applications for predicting longitudinal cracking in continuously reinforced concrete pavement

The longevity of continuously reinforced concrete pavement (CRCP) depends on the accurate prediction of longitudinal cracking. In this work, longitudinal cracking is predicted using machine learning algorithms, and data from the long-term pavement performance (LTPP) database. Multiple models, such as support vector machines (SVM), ensemble trees, Gaussian process regression (GPR), linear regression, regression trees, artificial neural networks (ANN), and kernel approaches, are compared. The Random Forest approach is used in statistical studies and feature relevance evaluation to identify temperature and annual average daily truck traffic (AADTT) as important predictors. R-squared and root mean squared error (RMSE) metrics are used to assess the models. Regression trees and ensemble approaches also perform competitively, but the GPR model with a squared exponential kernel performs better than the others, obtaining the best R-squared value (0.78) and the lowest RMSE (11.84). The study illustrates the shortcomings of traditional regression models and the benefits of sophisticated machine learning methods for identifying intricate nonlinear correlations. Sensitivity analysis demonstrates that pavement age, traffic loads, and environmental factors—specifically, temperature and precipitation—have a considerable impact on longitudinal cracking.