This study addresses the need for high-fidelity system identification in Digital Twin (DT) applications for Structural Health Monitoring (SHM). As infrastructure ages, its material properties may degrade due to various factors, including damage, corrosion, and fatigue. Accurate assessment of material properties is critical for ensuring safety and reliability. High-fidelity identification enables the detection of localized damages that traditional methods may not detect, directly impacting maintenance strategies and public safety. In this work, we present a formulation of the optimization problem that minimizes errors between observed and simulated displacements by varying material properties. Additionally, we utilize adjoint-based sensitivity analysis, combined with regularization techniques such as Vertex Morphing, to enhance the efficiency and robustness of the optimization process. Our case studies, which include detailed analyses of 2D and 3D structures using real-world data, demonstrate the effectiveness of our methods in accurately inferring material properties and revealing structural integrity. By implementing this advanced methodology, practitioners can achieve timely and accurate assessments of structural integrity, leading to better-informed decision-making regarding maintenance and safety protocols. This research contributes to the ongoing advancement of Digital Twin technology, promoting safer and more efficient infrastructure management.
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This study addresses the need for high-fidelity system identification in Digital Twin (DT) applications for Structural Health Monitoring (SHM). As infrastructure ages, its material properties may degrade due to various factors, including damage, corrosion, and fatigue. Accurate assessment of material properties is critical for ensuring safety and reliability. High-fidelity identification enables the detection of localized damages that traditional methods may not detect, directly impacting mainte...
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