Development of Pavement Distress Severity and Density Prediction Models Using Machine Learning

Sedighian-Fard, Mohammad; Golroo, Amir; Dehghan Tezerjani, Hananeh

doi:10.22075/jtie.2025.39921.1749

Development of Pavement Distress Severity and Density Prediction Models Using Machine Learning

Document Type : Research Paper

Authors

Mohammad Sedighian-Fard ¹

Amir Golroo ²

Hananeh Dehghan Tezerjani ³

¹ Ph.D. Candidate, Department of Civil and Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, I. R. Iran.

² Associate Professor, Department of Civil and Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, I. R. Iran.

³ M.Sc. Student, Department of Civil and Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, I. R. Iran.

10.22075/jtie.2025.39921.1749

Abstract

Linear and alligator cracking are critical indicators of asphalt pavement performance. The accurate prediction of these ciritical cracking distresses are of significant importance in effective and efficient pavement maintenance planning. This study proposes a data-driven framework based on multimodal data from the Long-Term Pavement Performance (LTPP) database, incorporating traffic, climatic, and performance-related variables to predict distress severity, length, and area. Key features, including surface distress indices, overlay thickness, traffic characteristics, and climatic indicators, were extracted and refined through feature engineering. Machine learning-based models were developed for severity classification and quantitative distress prediction using Artificial Neural Networks (ANNs). Addressing class imbalance with SMOTE improved severity classification accuracy from 0.782 to 0.843 for linear cracking and from 0.845 to 0.930 for alligator cracking. The models demonstrated strong predictive performance, achieving R² values of 0.941 for linear crack length and 0.954 for alligator crack area, supporting their applicability in preventive maintenance and pavement life-cycle management.

Keywords

Pavement management system

Pavement distress prediction

Long-term pavement performance (LTPP)

machine learning

Subjects