Predicting Pavement Structural Condition Using Machine Learning Methods

Nazmus Sakib Ahmed, Nathan Huynh, Sarah Gassman, Robert Mullen, Charles Pierce and Yuche Chen
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Nazmus Sakib Ahmed: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USA
Nathan Huynh: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USA
Sarah Gassman: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USA
Robert Mullen: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USA
Charles Pierce: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USA
Yuche Chen: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29201, USA

Sustainability, 2022, vol. 14, issue 14, 1-16

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 (SCI 12 ) 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.

Keywords: Traffic Speed Deflectometer; pavement structural condition; machine learning models; logistic regression model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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