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Shrestha, S. (2021) Network Level Decision-Making Using Pavement Structural Condition Information from the Traffic Speed Deflectometer. Doctoral Dissertation, Virginia Polytechnic Institute and State University.

has been cited by the following article:

• TITLE: Machine Learning Models for Pavement Structural Condition Prediction: A Comparative Study of Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)

  AUTHORS: Nazmus Sakib Ahmed

  KEYWORDS: Pavement Condition Assessment, Pavement Performance Modeling, Pavement Structural Condition, Falling Weight Deflectometer, Machine Learning

  JOURNAL NAME: Open Journal of Civil Engineering, Vol.14 No.4, November 28, 2024

  ABSTRACT: Effective pavement maintenance and rehabilitation decisions rely on both pavement functional and structural condition data. Traditionally, state transportation agencies prioritize pavement segments based on functional conditions, often neglecting structural assessments due to the time, cost, and labor involved with methods like the Falling Weight Deflectometer (FWD). The objective of this paper to develop machine learning models—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—to predict pavement Surface Curvature Index (SCI), a key indicator of pavement structural condition, as a cost-effective alternative to frequent FWD testing. Using 3016 samples from the Long-Term Pavement Performance (LTPP) program, the models were trained and tested with variables such as surface layer condition at year 0, thickness, pavement age, environmental, and traffic data. XGBoost outperformed RF, achieving R2, RMSE, and MAE values of 0.90, 0.64, and 0.41, respectively, compared to RF’s 0.80, 0.90, and 0.51. The study highlights the importance of machine learning applications in predicting pavement structural conditions, offering precise models that can help transportation agencies optimize maintenance planning and resource allocation.