Predictive Modeling of the International Roughness Index Using Machine Learning Techniques

Abstract: This study presents a machine learning-based approach to predict the International Roughness Index (IRI), a widely recognized indicator of pavement performance that reflects surface irregularities affecting ride quality and vehicle operating costs. Using data from the Long-Term Pavement Performance (LTPP) program, this research aims to develop robust predictive models capable of estimating future IRI values, enabling proactive pavement management strategies. The accurate prediction of IRI is crucial for transportation agencies as it informs decisions regarding maintenance scheduling, rehabilitation, and the optimization of life-cycle costs for road networks.

In this research, several state-of-the-art machine learning algorithms are employed, including decision trees, support vector machines, random forests, and gradient boosting methods. These algorithms are used to model IRI based on various input features derived from pavement condition factors such as age, traffic loads, climatic conditions, material properties, and maintenance history. The comprehensive long-term measurements provided by the LTPP dataset offer a diverse and high-quality foundation for model training and validation, ensuring that the resulting predictions are reliable and applicable across different pavement types and environmental conditions.

The study involves a detailed preprocessing stage, where data cleaning, feature selection, and normalization techniques are applied to ensure the quality and relevance of the input variables. Cross-validation methods are used to prevent overfitting and improve the generalizability of the models. The performance of each machine learning algorithm is evaluated using metrics such as mean absolute error (MAE), root mean square error (RMSE), and R-squared, with comparative analysis conducted to identify the most accurate and efficient prediction models.

Results from the study demonstrate the superior performance of machine learning models in capturing the complex, nonlinear relationships between pavement condition factors and IRI, outperforming traditional statistical methods. Random forests and gradient boosting models, in particular, show strong predictive capabilities due to their ability to handle interactions between variables and accommodate high-dimensional data. These models provide valuable insights into the long-term deterioration patterns of pavement surfaces, allowing for the identification of critical factors that significantly influence roughness progression.

The findings suggest that machine learning techniques can serve as a powerful tool in modern pavement management systems, offering a data-driven approach to enhance decision-making processes. By providing accurate IRI predictions, transportation agencies can prioritize maintenance activities more effectively, allocate resources more efficiently, and extend the lifespan of road infrastructure. This research contributes to the growing body of knowledge on the application of artificial intelligence in civil engineering, paving the way for future studies that integrate real-time data, sensor networks, and advanced optimization techniques into pavement performance modeling.