Date of Award

Fall 2021

Document Type

Open Access Dissertation


Civil and Environmental Engineering

First Advisor

Nathan Huynh


State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, speed limit, pavement age, and soil regions. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the RF and XGBoost models outperform the logistic regression model by 16% and 14%, respectively. RF outperformed XGBoost by 2%. With RF found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. Truck percentages, AADT, and pavement age are found to be significant factors on a pavement’s structural condition.