Integrating structural health monitoring and digital twin technology: ai-driven frameworks and applications for indian infrastructure

Structural Health Monitoring (SHM) is undergoing a fundamental transformation with the emergence of Digital Twin (DT) technology and data-driven intelligence. This research presents a comprehensive and integrated assessment of SHM DT frameworks enhanced by artificial intelligence and machine learning, with a particular focus on large-scale civil infrastructure applications and the Indian context. The study synthesises theoretical foundations, validated global case studies, and real-world performance metrics to demonstrate how continuous sensor data, physics-based modelling, and learning algorithms can collectively replace traditional inspection-driven maintenance practices. The proposed paradigm shifts infrastructure management from periodic, time-based interventions toward real-time, condition-based and predictive decision-making. Advanced machine learning techniques, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, are shown to effectively surrogate computationally intensive finite element models, automate damage detection, and capture long-term structural behaviour under operational conditions. Verified international deployments report classification accuracies exceeding 97%, computational speed-ups of over two orders of magnitude, and substantial reductions in data transmission and storage costs through edge cloud hybrid architectures. For India’s critical infrastructure portfolio comprising over 125,000 railway bridges, extensive highway and metro networks, and thousands of water management structures, the study identifies a strong academic and technological foundation alongside persistent barriers to adoption. These include financial limitations, workforce shortages, regulatory fragmentation, environmental challenges, and data governance concerns. Drawing on global best practices and indigenous research capabilities, this work proposes a phased, scalable implementation roadmap tailored to Indian conditions. The findings establish that SHM enabled digital twins are technically viable, economically justified, and essential for enhancing infrastructure safety, resilience, and lifecycle performance in rapidly developing economies.