Evaluating the use of machine learning for road maintenance cost estimation

Accurate estimation of highway maintenance costs is essential for efficient resource allocation and long-term infrastructure sustainability. This study evaluates the effectiveness of advanced machine learning models, including ResNet, Transformer, and other neural network architectures, for forecasting maintenance costs using historical data from the Highway Maintenance Improvement Program (HMIP) provided by the North Carolina Department of Transportation (NC DOT). A comprehensive comparative analysis is conducted across multiple models using standard performance metrics, including R2, MAE, RMSE, and MSE. The results demonstrate that advanced architectures, particularly ResNet and Transformer, consistently outperform traditional statistical approaches and baseline machine learning models, achieving R2 values exceeding 0.95 and substantially lower prediction errors. In addition, an ablation study is performed to assess model robustness under reduced feature availability, showing that the proposed models maintain strong predictive performance even with a limited set of key variables. These findings highlight the ability of advanced machine learning models to capture complex nonlinear relationships in structured infrastructure data and demonstrate their practical value for improving decision-making in maintenance planning and life-cycle cost analysis (LCCA).