In recent centuries, millions of bridges have been constructed as vital infrastructure components. However, a significant proportion are operating beyond their intended service life, increasing their vulnerability to deterioration and natural hazards. Conventional inspection and maintenance practices, primarily based on manual observations and non-destructive testing, are often inefficient and incapable of providing continuous, real-time insights into structural performance. To address these challenges, Digital Twin technology has emerged as a transformative solution, enabling the creation of dynamic, data-driven virtual replicas of physical assets that facilitate intelligent, adaptive and predictive maintenance, real-time monitoring and infrastructure assessment. This study presents a comprehensive review of the application of Bridge Digital Twins for structural health assessment, consolidating the latest advancements in their conceptual frameworks, enabling technologies, sensory systems and real-world implementations. The paper presents a structured framework that maps the technological, analytical and operational layers of Bridge Digital Twins, identifying key performance indicators associated with resilience and adaptability. The review systematically examines the essential components of Bridge Digital Twins, maturity levels, classification schemes and model updating techniques, and critically discusses their limitations and practical challenges in real bridge applications. Critical challenges hinder large-scale adoption, data interoperability, standardisation, model validation and computational efficiency. Research gaps and future research directions are identified to guide the widespread adoption of Digital Twins in bridge infrastructure.
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