Augmented reality for structural health monitoring By using virtual sensing

Abstract

This dissertation undertook a comprehensive investigation into the intersection of Structural Health Monitoring (SHM), the System Equivalent Reduction Expansion Process (SEREP), Augmented Reality (AR), and the pivotal role of sensors. The primary objective was to understand the potential of SEREP, AR, and advanced sensor technologies in revolutionizing the SHM field. The literature review highlighted the limitations of traditional SHM methods and how the integration of AR, SEREP, and modern sensors could provide a transformative approach to SHM practices. The study methodology employed the SEREP technique due to its efficacy in systems with interacting linear and nonlinear components, heavily reliant on the precise data supplied by advanced sensors. The research affirmed the effectiveness of SEREP in accurately replicating model information without demanding extensive computational resources. The application of SEREP to the modeling and modal analysis of a Brake-Reuß beam further verified its robustness, with results closely mirroring the Finite Element Method (FEM) model. Furthermore, integration of the SEREP technique with the Butterworth Low Pass Filter within a virtual sensing approach, empowered by sensor technology, demonstrated significant potential in accurately estimating a structure's state. Simultaneously, AR emerged as a transformative tool, offering immersive, interactive visualizations of SHM processes, with the sensor data enabling real-time insights. Overall, the research substantiated the robustness of the SEREP technique, the transformative potential of AR, and the indispensable role of sensors in pioneering SHM practices. The findings provide a solid foundation for future research aimed at evolving safer, more efficient, and reliable structural health monitoring practices.