Machine Learning Models to Enhance the Prediction of Pavement Condition
Background
Accurately predicting the deterioration of transportation assets is essential for effective infrastructure management. While traditional approaches like regression and Markov models have long been used for this purpose, they struggle to handle the complexity, noise, and volume of modern data. Machine learning offers a powerful alternative — capable of processing large datasets, recognizing complex patterns, and generating real-time predictions. Despite its promise, transportation agencies have been slow to adopt these techniques due to concerns about interpretability and lack of quantitative evidence of their effectiveness. This project addresses that gap by providing objective, evidence-based measures of the benefits of machine learning for forecasting asset condition, helping CDOT make informed decisions about integrating these tools into their asset management process.
Research Objective
The objective of this project is twofold: (1) to explore the capabilities of machine learning algorithms in predicting the condition of pavement assets, and (2) to quantify the benefits of machine learning models when compared to traditional regression or Markov models currently used by Colorado DOT.
Research Methods
This research involves collecting pavement condition, traffic, maintenance, and environmental data to develop advanced predictive models of pavement deterioration. The models address key limitations of current approaches, including poor site-specific fit, inability to share information across sites, and lack of uncertainty quantification. Models will also use hierarchical frameworks to account for the spatial dependencies of pavement segments the network. Models will be validated using different approaches including k-fold holdout and multiple steps ahead validation. The research will also define realistic pavement management case studies in collaboration with CDOT, and quantify the benefits of the proposed models relative to traditional methods using metrics related to predictive accuracy, life-cycle costs, and inspection efficiency.
Expected Contributions
This project has the potential to enhance the current predictions of pavement condition. More accurate predictions of pavement condition will, in turn, result in better maintenance and inspection policies that will ultimately result in significant cost saving over the life-cycle of transportation assets.
Funding
Colorado Department of Transportation (CDOT): 2025-2027.