Work

Improving preventive maintenance strategies for enhanced pavement friction restoration performance via explainable machine learning

Year: 2026

Type: article

Abstract: Maintaining sufficient pavement surface friction is crucial for roadway safety, particularly under wet conditions. Preventive Maintenance (PM) treatments have proven effective in restoring pavement friction levels and reducing the likelihood of crashes. However, capturing the friction deterioration behavior after different PM treatments remains challenging due to complex interactions among traffic, climate, and pavement conditions. This study analyzed pavement friction number (FN) using data from the Long-Term Pavement Performance (LTPP) Special Pavement Study 3 (SPS-3) to evaluate the effectiveness of 4 PM treatments at improving and maintaining high levels of friction. Four machine learning models, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were developed to capture the complex relationships between 10 input features and FN. The SHapley Additive exPlanations (SHAP) was employed to interpret model predictions and quantify the contribution of each input and interaction variables. Among all models, XGBoost achieved the best performance, with an R 2 of 0.76 and RMSE of 5.46 on the test dataset. Results indicate that slurry seals are the most effective treatment for improving pavement friction over a five-year period, followed by chip seal. The XGBoost model, combined with a cluster-based data splitting strategy, provided the most robust and accurate FN predictions. Sensitivity analysis shows that slurry seal maintains FN above the investigatory level (FN = 40) when Annual Average Daily Truck Traffic (AADTT) is below approximately 2000 trucks/day, but its effectiveness declines sharply beyond this threshold. Additionally, pre-treatment surface condition strongly influences pavement friction performance, with higher International Roughness Index (IRI) values corresponding to degraded texture and lower friction. Finally, the SHAP framework offered interpretable insights into data-driven pavement management decisions. These findings provide transportation agencies with actionable insights to guide treatment selection and optimize maintenance planning for safer pavement management.

Source: Construction and Building Materials

DOI: 10.1016/j.conbuildmat.2026.145572

Authors: Jingtao Zhong, Christian Sabillon-Orellana, Miaomiao Zhang, Yuetan Ma, Xi Jiang, Baoshan Huang

Institutions: University of Tennessee at Knoxville, Hohai University, Changsha University of Science and Technology, Tongji University

Language: English

Topic: Asphalt Pavement Performance Evaluation

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,540