On the development of digital twin for structural health monitoring

The present study aims to explore advanced finite element model updating techniques to analyze and evaluate the health condition of structures. The final goal is the integration of Digital Twin technology into continuous Structural Health Monitoring system. Additionally, this thesis seeks to demonstrate the significance of individual contributions to the success of the model updating process, specifically examining the influence of the objective function, the number of modes considered in solving model updating problem, and the optimization algorithms employed. To minimize computational efforts, the potential inclusion of a surrogate model in the model updating procedure is also explored, and a comparison between different surrogate models applied to a real case study is presented. Furthermore, the robustness of the surrogate models in solving model updating problems is widely analyzed by referring to potential unexpected damage events that would lead to damage scenarios involving the global behavior of the structure. Finally, two real case studies are analyzed and utilized to test model updating procedures, surrogate models, objective functions, and damage scenarios with the ultimate goal of integrating these findings into the field of Digital Twin for Structural Health Monitoring. The present thesis delves into the valuable world of model updating, exploring its significance in refining computational models to mirror the actual behavior of a structure under operational conditions. By combining the principles of Structural Health Monitoring with the precision of model updating, the research attempts to unlock the full potential of Digital Twin for continuous structural health monitoring.