Overview and Discussion of Pavement Performance Prediction Techniques for Maintenance and Rehabilitation Decision-Making

The pavement performance prediction techniques were studied for concrete and asphalt pavements. The future pavement conditions for optimized performance and maintenance can be predicted using various techniques. Different techniques including Mechanistic-Empirical Pavement Design Guide (MEPDG), Artificial Neural Network (ANN), Regression Analysis, Probabilistic Analysis, Expert Systems, Artificial Intelligence, Machine Learning, and Pavement Management Systems (PMS) were employed to systematically investigate and assess pavement performance. The performance of pavement is influenced by functional and structural factors of deterioration including materials, traffic, pavement type, and environmental conditions. Transport infrastructure development requires researcher collaboration for advancement. However, few studies used hybrid techniques and prediction models to accurately analyze pavement problems and make cost-effective, safe, and efficient repair plans within financial limits. Each performance technique has its advantages and limitations in systematic procedures to predict pavement performance. The focus of this study is on exploring approaches to predict pavement performance based on weather patterns and other variables, evaluating maintenance requirements based on actual pavement conditions, and optimizing maintenance costs. The research concludes that pavement engineers need advanced analysis models to precisely predict pavement performance for maintenance and repairs. To access the link between trends of pavement performance for maintenance, routine maintenance actions need to be properly documented. Based on varying pavement performance trends innovative materials, construction methods, and design methodologies may be incorporated for better performance are being used to improve structural integrity, durability, sustainability, and long-term performance. It would optimize the cost and frequency of the maintenance and repair requirements.