Abstract
Pavement performance assessment and prediction are crucial for efficient infrastructure management and strategic planning of maintenance activities. Conventional techniques are insufficient and lack the efficiency and flexibility required for modern transportation networks. This study proposes a groundbreaking integrated approach that merges machine learning (ML) classification techniques with Geographical Information Systems (GIS) to evaluate road conditions using the Pavement Condition Index (PCI) and the International Roughness Index (IRI). Given that IRI data collection is more straightforward and cost-effective than gathering pavement distress data, this study aims to classify the IRI of flexible pavements to estimate PCI models using advanced ML algorithms (Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), Decision Trees (DT), and Random Forest (RF)) and accurately determine pavement conditions. This research gathered 1042 data points using a smartphone application, TotalPave, to measure the IRI values for the (Nizwa-Muscat) and (Muscat-Nizwa) routes in the Sultanate of Oman. It meticulously applied feature selection techniques to identify the pavement parameters significantly impacting pavement performance. The research then spatially visualized and analyzed the results to determine the critical pavement sections. Among the ML models, RF demonstrated outstanding performance with an accuracy rate of 99.9% and an F1-score of 99. SVM has the lowest accuracy of 85.8% and an F1-score of 40.3. A comprehensive assessment comprising a confusion matrix, uncertainty analysis, box and whisker plot, and noise sensitivity provides in-depth insights into the reliability and consistency of predictions. The ML and GIS methods revolutionized the way transportation agencies interpret and implement their findings. The proposed framework is not merely a tool, but a transformative solution that facilitates the formulation of proactive maintenance strategies and optimizes resource utilization by providing a scalable and intelligent decision-support tool designed specifically for pavement management systems (PMS).