Abstract
Civil infrastructure has increasingly adopted Artificial Intelligence (AI), reshaping bridge engineering and management practices, particularly during the construction phase. This review provides a comprehensive assessment of AI applications, specifically machine learning (ML), deep learning (DL), and computer vision (CV), employed in bridge construction planning, scheduling, monitoring, quality assurance, and safety management. Given the growing complexity of bridge projects and the persistent demands for enhanced safety, efficiency, and cost-effectiveness, integrating intelligent, data-driven methodologies has become essential. Utilizing a mixed-methodology approach, this study combines scientometric and systematic literature reviews to critically analyze peer-reviewed publications spanning the years 2000–2025. The findings indicate substantial advancements in AI techniques, demonstrating notable improvements in resource optimization, risk prediction accuracy, and proactive safety management. However, the implementation of AI in bridge construction also faces challenges, such as high computational resource requirements, data quality issues, model scalability concerns, and integration complexities. By identifying key research trends, technological benefits, and existing limitations, this paper contributes valuable insights and proposes future research directions to enhance the practical integration of AI, ultimately aiming to improve the resilience, reliability, and longevity of bridge infrastructure.