Thiyagarajan Jothi Saravanan

Self-consolidating concrete (SCC) made with sintered fly ash aggregates (SFAA) was investigated for its compressive strength and microstructural properties. Even with significant additions of mineral admixtures such as silica fume (SF) and metakaolin (MK) and 100% SFAA, the formulated SCC demonstrated compressive strength satisfying the minimum requirements for structural lightweight concrete, in accordance with established standards such as those recommended by the ACI 318-19, BS EN 206-1 and Eurocode 2 after 28 and 90 days of curing. Strength development was corroborated through SEM and EDS analysis, while non-destructive methods, including rebound hammer and ultrasonic pulse velocity (UPV), confirmed the enhancements in compressive strength.

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.

Structural health monitoring (SHM) is essential for ensuring the safety and durability of large structures by identifying and mitigating damage risks. Computer vision (CV)-based measurement systems offer advantages over conventional sensors, including broader coverage of inaccessible areas and lower installation costs. This work introduces a novel noncontact CV-based physics-guided framework for measuring structural vibration responses under ambient (stochastic) conditions from its vibration videos, localizing damages associated with mass and stiffness changes, and quantifying their severity. Homography transformation and integration of the speeded-up robust features and Kanade–Lucas–Tomasi tracking algorithms are employed to extract displacement response from vibration video under different camera orientations. Unlike conventional approaches, this study employs principal component analysis as an unsupervised data-driven approach to estimate the optimal system order from displacement responses. State-space matrices’ estimation and potential damage identification are performed using the numerical algorithms for subspace state-space system identification and stochastic damage locating vector approach, respectively. An improved Bayesian inference-based stochastic model updating algorithm is proposed to precisely locate the actual damaged elements of the structure and assess their severity. The proposed methodology is validated on a numerical truss model with simulated damage scenarios and a laboratory-scale wooden truss bridge using sensor-recorded data. The methodology is finally validated on the damage localization for a photo-realistic synthetic truss bridge model from its vibration videos. The improved accuracy of damage severity prediction compared to existing techniques demonstrates the method’s adeptness in precisely detecting structural damage. Furthermore, the methodology showcases its efficacy in locating damage across various severity levels, facilitating its application in real-time SHM.