Real-Time Mechanical Modeling for Bridge Construction Based on Digital Twins and Parameter Inversion

Real-time mechanical analysis within digital twin (DT) systems requires high-fidelity models that synchronize with the “as-built” state of physical structures. This paper proposes a technical framework for constructing a “mechanical-core” DT by integrating computer vision (CV) sensing with automated finite element model (FEM) updating. Utilizing the Midas API, we developed a platform that automates data acquisition, modeling, and parameter inversion. A momentum-based optimization algorithm is implemented to invert the instantaneous elastic modulus of bridge segments during cantilever construction. The system was validated through a case study of a continuous box-girder bridge. Quantitative results indicate that the initial theoretical model, based on design-phase assumptions, exhibited a mean relative error of approximately 21.9% in vertical displacement. Following the real-time parameter inversion, this error was significantly reduced to less than 0.2% across all monitored construction stages. The rapid convergence (typically within three iterations) and the substantial increase in predictive accuracy demonstrate that the proposed framework effectively bridges the gap between raw sensing data and structural analysis, providing a reliable basis for proactive engineering decision-making.