Structural Digital twin-based framework for real-time Health Monitoring

The safety and longevity of infrastructures are of critical concern in modern days, necessitating advanced systems for Structural Health Monitoring (SHM). Traditional inspection methods are often inefficient and difficult in terms of continuous real time monitoring of structural deterioration in infrastructures. Digital twin technology has emerged as a pivotal innovation in modern engineering, offering a dynamic framework for real-time monitoring, analysis, and control of physical assets. This research focuses on developing a comprehensive framework for digital twinning, with an emphasis on SHM. The proposed framework integrates sensor system data, finite computational models, and real-time feedback mechanisms to replicate and predict the behaviour of physical assets. The proposed framework has been validated through a fixed beam structure as a proof of concept for Digital twin application. The major issue in traditional digital twin application is seamless integration of the framework with numerical analysis to come up with meaningful usable results. The present work tries for continuous integration of the framework with a robust numerical structural analysis software for better analysis of complex structures. Therefore, in the present study, by using Python programming as the base for developing the real-time integration of data collected from physical structure and to communicate with numerical modelling software SAP2000 through its open API. The process has been automated to provide alarming system in case of thresholds results are exceeded, and continuously updates the digital model, ensuring a seamless interaction between the physical and virtual entities. This research provides a robust foundation for extending digital twinning beyond traditional SHM, highlighting its potential for predictive maintenance, operational optimization, and life-cycle management.