A Review and Analysis of Evolutionary Mathematical Algorithms for Pavement Condition Prediction

Pavement infrastructure plays a critical role in the economic development and transportation systems of any country. Maintaining pavements in good condition is essential for ensuring road safety, reducing vehicle operating costs, and prolonging the lifespan of roads. Traditional methods for predicting pavement deterioration are often limited in their ability to handle complex, non-linear relationships and uncertainty in environmental and traffic conditions. Therefore, there is a growing need for advanced computational techniques that can model such intricacies more accurately. Evolutionary mathematical algorithms, inspired by biological evolution and natural selection, offer a promising solution. These algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Evolutionary Strategies (ES), are capable of exploring large solution spaces efficiently. They are well-suited for optimizing complex systems where traditional methods fall short. Their ability to evolve solutions over iterations allows them to adapt to the changing patterns and uncertainties inherent in pavement condition data. This paper presents a comprehensive review on evolutionary mathematical algorithms for pavement condition prediction along with their salient features.