Predicting Pavement Structural Condition Using Machine Learning Methods

Author(s)

Nazmus Sakib Ahmed
Nathan Huynh, University of South CarolinaFollow
Sarah Gassman, University of South CarolinaFollow
Robert Mullen, University of South CarolinaFollow
Charles Pierce, University of South CarolinaFollow
Yuche Chen, University of South CarolinaFollow

Document Type

Article

Abstract

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, and speed limit. 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 XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost 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. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

Digital Object Identifier (DOI)

https://doi.org/10.3390/su14148627

Publication Info

Published in Sustainability, Volume 14, Issue 14, 2022, pages 8627-.

Rights

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

APA Citation

Nazmus Sakib Ahmed, Huynh, N., Gassman, S., Mullen, R., Pierce, C., & Chen, Y. (2022). Predicting Pavement Structural Condition Using Machine Learning Methods. Sustainability, 14(14), 8627–8627. https://doi.org/10.3390/su14148627