• This review synthesizes Artificial Intelligence, Computer Vision, Digital-Twin technologies, and Structural Health Monitoring into a unified framework for concrete bridge assessment , moving beyond siloed approaches in existing literature. By treating these domains as an interconnected ecosystem, the study establishes a holistic foundation for intelligent bridge inspection and management. • The review emphasizes a quantitative and objective approach to SHM, channelling to root-cause-oriented diagnostic and prognostic models to enhance the remaining useful life of concrete bridges, which constitute the most prevalent bridge typology globally. • A comprehensive maintenance framework is proposed that accounts for bridge typology, construction type, and material behaviour, encompassing the full inspection-to-decision pipeline from task definition and data acquisition to anomaly detection, localization, metrology, visualization, root-cause analysis, structural condition assessment, inclusive decision-making, and prognosis. Bridges play a pivotal role in a country’s national connectivity and economic development. Ensuring their structural integrity through regular monitoring is essential for maintaining public safety and supporting continuous development. However, traditional inspection methods, though long established, often lack the precision, frequency, and scalability required for modern infrastructure maintenance, especially in the context of growing structural demands and environmental stressors. This review article presents a comprehensive synthesis of research conducted over the past decade on the use of Artificial Intelligence (AI) and digital-twin-based (DT) technologies for the structural health monitoring (SHM) of concrete bridge infrastructure. The study examines the transition from reactive-manual, labour-intensive inspection techniques to proactive-automated, data-driven, and contactless methods that facilitate high-frequency monitoring, improved accessibility, and enhanced safety. The review highlights key tools, platforms, and technological advancements in inspection and health monitoring systems, while critically examining their potential for anomaly detection, life cycle management, practicality of implementation, and predictive maintenance. The work also identifies prevailing challenges, research gaps, technology drifts, and future directions, ultimately advocating for the integration of intelligent SHM frameworks to ensure the long-term sustainability and resilience of bridge infrastructure.
Sunil et al. (Sun,) studied this question.
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