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

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 (R2) 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.

Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

“…Studies have demonstrated the ML algorithms' effectiveness in predicting pavement performances [9,30,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]. Likewise, commonly used algorithms include ANN, SVM and support vector regression (SVR) [40], adaptive boosting (AdaBoost) [68], random forest (RF) [69], gradient boosting decision trees (GBDT) [70], categorical boost (CatBoost) [71], and ensemble models [50].…”

“…Likewise, Luo et al [61] compared four models-GBDT, XGBoost, SVM, and MLR-to determine the best PPM, finding that GBDT was superior. Sandamal et al [63] employed five ML models-k-Nearest Neighbor (kNN) [74], SVM, DT, RF, and XGBoost-to predict the IRI of pavements on Sri Lankan arterial roads. Focusing on pavement age and cumulative traffic volume as the only predictors, they found that these models outperformed traditional techniques.…”

“…These features consolidate information about pavement, including records of maintenance and rehabilitation activities, traffic conditions, structural capacity, and exposure to various environmental conditions. [9] 2019 AdaBoost LTPP X X X Wang et al [53] 2021 Adaboost LTPP X X X Hossain et al [54] 2019 ANN LTPP X X Abdelaziz et al [30] 2020 ANN LTPP X X Zeiada et al [55] 2020 DT, SVM, EBT, GPR, ANN LTPP X X X Damirchilo et al [56] 2020 XGBoost LTPP X X X X Zhang et al [67] 2020 GBDT LTPP X X X Guo et al [57] 2021 LightGBM LTPP X X X Gharieb et al [58] 2021 ANN NRN X Marcelino et al [59] 2021 RF LTPP X X X X Naseri et al [60] 2022 RF LTPP X X X X Luo et al [61] 2022 GBDT, XGBoost, SVM LTPP X X X Song et al [62] 2022 ThunderGBM LTPP X X X X Sandamal et al [63] 2023 kNN, SVM, DT, RF, XGBoost Proprietary 1 X Abdualaziz et al [64] 2023 ANN LTPP Naseri et al [65] 2023 DT, SVM, RF, ANN LTPP X X X Sharma et al [66] 2023 GBDT, ANN, XRT, GLM, RF LTPP X X X In the models analyzed, a majority utilize data on traffic, with 89% of the studies incorporating this variable, while climatic factors and pavement structures are considered in 78%. This wide usage reflects a holistic approach to integrating diverse yet influential factors, underscoring the collective recognition of their importance in accurately predicting pavement conditions.…”

“…Authors like Song et al [62], Yao et al [10], and Sandamal et al [63] have used the SHAP method to improve their models' interpretability and better understand the factors driving their predictions. They could identify the most important features using SHAP and understand how they contributed to the predictions.…”