Towards Data-Enabled Intelligent Road Construction: A Review of Hybrid Algorithms and Applications

Abstract

Contemporary road building projects are becoming increasingly complex, requiring advanced project management solutions to address challenges such as risk management, resource optimization, Adaptive scheduling, and real-time decision-making. Traditional deterministic methods are difficult to adapt to the complex constraints and uncertainties of large infrastructure projects. To achieve this goal, hybrid algorithms—including machine learning, meta-heuristic algorithms, and simulation-based optimization techniques—have emerged as a key method in intelligent road construction management. This paper provides a detailed discussion of the theoretical foundations, classification, and practical applications of hybrid computational intelligence models. Through research in key areas such as quality control, resource allocation, project scheduling, and real-time monitoring, this paper highlights the significant advantages of hybrid frameworks in terms of adaptability, efficiency, and robustness. Based on comparative studies and benchmarking, hybrid algorithms demonstrate outstanding performance in addressing uncertainty, improving resource utilization, and reducing overall project duration. Despite these developments, issues such as data quality, model generalization capabilities, interoperability with intelligent construction platforms, and consistency in benchmarking practices remain to be addressed. This review concludes that hybrid algorithms form the foundation for the digital transformation of road construction management. Continued efforts to standardize data, innovate methods, and achieve seamless integration between intelligent algorithms and evolving construction technologies will determine future development. The research findings provide valuable insights for researchers and practitioners dedicated to designing more efficient, adaptive, and resilient modern road construction project management solutions.