Machine Learning Modelling of IRI in Continuously Reinforced Concrete Pavements Using LTPP Data: Comparative Evaluation of Advanced Algorithms
DOI:
https://doi.org/10.9744/ced.28.1.130-144Keywords:
machine learning, IRI, pavement performance, feature importance, sensitivity analysis, infrastructure managementAbstract
Accurately predicting International Roughness Index (IRI) is essential for effective pavement maintenance and long-term network sustainability. This study evaluates several advanced machine learning models for IRI prediction in Continuously Reinforced Concrete Pavement (CRCP) using a comprehensive dataset from Long-Term Pavement Performance (LTPP) program. Support Vector Machines, Artificial Neural Networks, Regression Trees, Ensemble Trees, and Gaussian Process Regression (GPR) were developed and assessed using Root Mean Square Error (RMSE) and R-squared (R²). The Matern 5/2 GPR model achieved the best performance, with R² = 0.97 and RMSE = 0.0776. Feature importance analysis using Random Forest identified initial IRI, construction number, layer thicknesses and temperature as the strongest predictors. Sensitivity analysis confirmed the influence of age, climate, and traffic on IRI. Using only the top ten variables produced nearly identical accuracy, improving computational efficiency. Overall, the study demonstrates the strong potential of ML for reliable and sustainable IRI prediction in rigid pavements.
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