PREDICTIVE MAINTENANCE OF CIVIL INFRASTRUCTURE USING DIGITAL TWIN TECHNOLOGY AND AI-BASED STRUCTURAL PERFORMANCE MODELING TECHNIQUES

Abstract

The growing complexity, aging, and environmental vulnerability of civil infrastructure systems have accelerated the transition from traditional maintenance approaches toward predictive and intelligent asset management strategies. This review paper explores the integration of Digital Twin (DT) technology with Artificial Intelligence (AI)-based structural performance modeling techniques for predictive maintenance of civil infrastructure. The study critically examines the evolution from static Building Information Modeling (BIM) toward dynamic Digital Twin systems capable of real-time monitoring, simulation, and decision-making throughout an infrastructure asset’s lifecycle. Key architectural components including IoT-enabled sensing systems, cloud-edge communication networks, semantic synchronization frameworks, and cybersecurity mechanisms are discussed in relation to structural health monitoring applications. The review further analyzes advanced AI methodologies such as deep learning, Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), Transformers, and Physics-Informed Neural Networks (PINNs) for anomaly detection, Remaining Useful Life (RUL) prediction, and real-time structural performance assessment. Digital Twin-enabled predictive maintenance frameworks demonstrate substantial operational benefits, including reduced maintenance costs, minimized infrastructure downtime, improved fault detection accuracy, and enhanced lifecycle sustainability. The paper also highlights the role of reduced-order modeling, federated learning, edge computing, and AI-native 6G connectivity in achieving computational efficiency and real-time system responsiveness. Case studies involving bridges, dams, tunnels, healthcare facilities, and urban infrastructure systems demonstrate the practical effectiveness of AI-driven Digital Twins in extending service life and improving resilience. Despite significant advancements, challenges related to interoperability, high implementation costs, cybersecurity, and data governance remain critical barriers to widespread adoption. Overall, the integration of Digital Twin technology and AI-based predictive analytics represents a transformative approach for developing resilient, sustainable, and intelligent infrastructure management systems in future smart cities.