Advancing pavement Management: A comprehensive review of smart models for better decisions

Pavement management systems have evolved significantly, transitioning from traditional empirical methods to advanced mechanistic-empirical, probabilistic, and machine learning-based models. These advancements enable better integration of environmental, traffic, and material factors, enhancing predictive accuracy for maintenance and rehabilitation (M&R) strategies. This paper presents a comprehensive bibliometric analysis of the field, followed by an extensive review examining three key areas: the evolution of pavement deterioration models, the integration of Life Cycle Assessment (LCA) into decision-making processes, and the application of multi-objective optimization and decision support systems in pavement management. Recent studies in the literature demonstrate that integrated approaches yield substantial benefits, with recent studies documenting up to 30% reductions in maintenance costs and 17% decreases in carbon emissions. However, the review also identifies persistent challenges related to data quality, computational complexity, and organizational capacity constraints. The transformative potential of emerging technologies, particularly Large Language Models (LLM), for enhancing data interpretation, predictive modelling capabilities, and stakeholder communication was explored in this paper. By synthesizing current research, this review maps key trends, research gaps, and future directions for sustainable and resilient pavement management which can provide valuable insights for researchers, practitioners, and policymakers working to develop intelligent, data-driven infrastructure management systems that balance economic, environmental, and performance objectives.