Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance

Kelum Sandamal (), Sachini Shashiprabha, Nitin Muttil and Upaka Rathnayake ()
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Kelum Sandamal: Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
Sachini Shashiprabha: Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
Nitin Muttil: Institute for Sustainable Industries & Liveable Cities, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia
Upaka Rathnayake: Department of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, F91 YW50 Sligo, Ireland

Sustainability, 2023, vol. 15, issue 12, 1-17

Abstract: Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R 2 ) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.

Keywords: explainable AI; international roughness index; pavement performance; supervised machine learning; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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