| Title |
Dual-Stage Optimization of a Digital Twin for Bridge Load Identification |
| Authors |
이유재(Lee, Yujae);김충길(Kim, Chunggil);이재훈(Lee, Jaehoon);방건혁(Bang, Geonhyeok);허광희(Heo, Gwanghee) |
| DOI |
https://doi.org/10.12652/Ksce.2026.46.1.0011 |
| Keywords |
교량 하중 추정; 디지털 트윈; 이중 최적화; 변위 기반 역추정 Bridge load identification; Digital twin; Dual-stage optimization; Displacement-based inverse analysis |
| Abstract |
Bridges are critical components of road and railway transportation networks; however, they are continuously exposed to repetitive traffic loads, environmental effects, and abnormal actions such as overloading and vehicle impacts, which can accelerate structural deterioration and lead to serious safety risks. Conventional bridge assessment methods, including static load testing, dynamic characteristic analysis, and visual inspection, suffer from high cost, traffic interruption, indirect evaluation, and subjectivity, limiting their applicability to real-time load identification. This study proposes a dual-optimization-based digital twin framework to efficiently identify the magnitude and location of applied loads using measured displacement responses of bridges. The proposed approach first performs a dynamic optimization using measured dynamic response data to update model parameters, thereby reducing structural modeling uncertainties and improving physical consistency. Subsequently, a static optimization is conducted using measured displacement data to accurately estimate the load magnitude and its application location. Through this two-stage optimization process, the structural accuracy of the numerical model is enhanced and the discrepancy between measured and simulated responses is minimized, leading to improved reliability of load identification. To validate the proposed method, displacement measurement experiments were conducted on a scaled bridge model under various loading conditions. The experimental results demonstrate that the proposed digital twin effectively reproduces the measured structural responses and enables stable and accurate estimation of both load magnitude and location. In particular, the integration of dynamic and static measurement data through dual optimization significantly reduces estimation variability and provides consistent load identification performance under diverse measurement conditions. |