| Title |
Hybrid Digital Twin Based on State-Space Equations for Bridge Safety Assessment |
| Authors |
김충길(Kim, Chunggil) ; 이유재(Lee, Yujae) ; 이재훈(Lee, Jaehoon) ; 방건혁(Bang, Geonhyeok) ; 허광희(Heo, Gwanghee) |
| DOI |
https://doi.org/10.12652/Ksce.2025.45.2.0139 |
| Keywords |
디지털 트윈; 상태공간방정식; 교량 안전; 유한 요소 모델; 실시간 데이터 Digital twin; State space representation; Bridges safety; Finite element model; Real-time data |
| Abstract |
In this study, a state-space equation based hybrid digital twin system was developed as a novel approach for bridge safety assessment. To overcome the data overload issue faced by conventional 3D model-based digital twins, an optimized model was designed using response data from specific target points. For this purpose, structural characteristics were analyzed based on a finite element (FE) model, and the Guyan Reduction method was applied to construct a state-space equation incorporating mass, stiffness, and damping coefficients. The state-space equation model was designed to quantitatively analyze the dynamic behavior of the structure, with initial parameters calibrated using experimental data. Furthermore, real-time data feedback was integrated to continuously reflect changes in the bridge’s condition, and a multi-objective optimization algorithm was employed to enhance the model’s reliability. During the optimization
process, mass, stiffness, and damping coefficients were adjusted to minimize discrepancies with experimental data, thereby improving the digital twin model’s accuracy in predicting structural responses. To evaluate the effectiveness of the developed hybrid digital twin, experiments were conducted on a scaled bridge model. Various loading conditions were applied during the experiment, and the model’s predicted results were compared with actual measured displacement data. The results demonstrated a high correlation between the digital twin model and the experimental data, with an error rate of less than 5 %. The state-space equation-based hybrid digital twin system enhances sensitivity to bridge condition changes by reflecting real-time structural responses and enables more precise maintenance and safety diagnostics through periodic state updates. |