AI-Enhanced Prediction of Pavement Crack Propagation: A Study Using Traffic Load, Environmental and Material Data

Abstract

This study develops an AI-based predictive model for forecasting pavement crack propagation by integrating traffic load, environmental conditions, and material property data. Traditional pavement management systems often struggle to accurately predict crack growth due to the complex interactions between these influencing factors. By leveraging data from various sources, including sensor-based traffic metrics, meteorological data, and material composition tests, this study identifies significant variables contributing to crack initiation and progression. The proposed model utilizes a blend of machine learning algorithms, including Random Forest and neural networks, with a cross-validation approach to ensure robustness. Results indicate that the model achieves high prediction accuracy, with an RMSE of 1.2 mm/year and an R-squared value close to 0.93. The findings support the use of AI-enhanced models as reliable tools for road infrastructure planning and maintenance, promising reductions in maintenance costs and improved pavement durability.