What question did this study set out to answer?

The aim is to create an efficient framework for classifying pavement conditions and prioritizing maintenance using machine learning.

April 30, 2026Open Access

Intelligent pavement management: a machine learning framework for condition classification and maintenance prioritization

BGBhoomika GuptaUniversity of Rajasthan MAMannya AgrawalUniversity of Rajasthan PNPreeti NarookaManipal Hospital

Key Points

The aim is to create an efficient framework for classifying pavement conditions and prioritizing maintenance using machine learning.
Developed a multi-model machine learning framework integrating distress indicators and traffic distributions.
Evaluated eight supervised learning models using k-fold cross-validation and confusion matrices.
Established a five-tier maintenance prioritization framework based on model outputs.
Ensemble models, especially Random Forest and XGBoost, had stable predictive performance across pavement classes.
Achieved balanced classification across Good, Fair, and Poor categories with high F1-scores.
Model outputs enabled actionable scheduling for pavement management across urban and rural networks.

Abstract

Efficient pavement maintenance requires reliable classification of pavement condition and clear prioritization of interventions. This study develops and benchmarks a multi-model machine learning framework for pavement condition classification in both urban and rural road networks, integrating distress indicators, road geometry, and motorized and non-motorized traffic distributions. Eight supervised learning models (Logistic Regression, Naïve Bayes, SVM, KNN, Decision Tree, Random Forest, XGBoost, and ResNet) were evaluated using k-fold cross-validation, class-wise F1-scores, and confusion matrices. Tree-based ensemble models, particularly Random Forest and XGBoost, consistently demonstrated stable and balanced predictive performance across Good, Fair, and Poor pavement classes. Model outputs were translated into a five-tier maintenance prioritization framework (Critical, High, Medium, Low, Monitor), enabling actionable scheduling for pavement management. Results confirm the effectiveness of ensemble models for decision-support in pavement management, and highlight the importance of context-specific modeling for urban and rural environments.

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