AI-Enabled Digital Twin Framework for Real-Time Structural Health Monitoring and Damage Prediction in Smart Civil Infrastructure

Abstract

With the growing complexity and aging of civil infrastructure, they must be monitored by an intelligent system and predictive maintenance solutions must be provided for the safety and reliability of the structures. The proposed architecture in this paper is founded on the idea of using the concept of AI to enable real-time monitoring and prediction of damage in smart civil infrastructure using the Digital Twin. The proposed framework integrates the three aspects of the IoT-based sensor network, cloud-edge computing and deep learning models and ties them to a dynamic Digital Twin to continuously monitor the circumstances of the structure and identify anomalies. Using a combination of AI algorithms, real-time data from accelerometers, strain gauges and vibration sensors is analysed to identify damage, predict cracks and evaluate structural performance. The Digital Twin connects physical and virtual infrastructure models to enable perpetual visualization and predictive analytics in order to plan maintenance proactively. The framework improves the accuracy of monitoring, lowers inspection costs and increases the resilience of infrastructure to structural failures. The proposed system can be effectively implemented to manage intelligent and sustainable infrastructure for bridges, buildings, tunnels and smart city infrastructure.