## 5 Digital Twin Implementation Scope and Limitations  
The digital twin presented in this study represents a focused, test-assisted implementation developed specifically for the Cho'ponota L1 Bridge. Unlike traditional digital twins that rely on continuous, real-time data streams and persistent cloud-based infrastructure, this approach leverages a high-quality but temporally limited dataset obtained through  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bridge Span** & **Loading Status** & **Vehicle Load (KN)** & **Load Ratio** & **L23** & **Displacement Ratio** \\ \hline \multirow{4}{*}{S1} & ST2 & 300 & \(1.67\) & \(-4\) & \(1.50\) \\  & ST6 & 500 & \(-6\) & \(1.50\) \\  & ST4 & 300 & \(-4\) & \(1.50\) \\  & ST8 & 500 & \(1.67\) & \(-6\) & \(1.50\) \\ \hline \multirow{4}{*}{S2} & ST10 & 300 & \(1.67\) & \(-10\) & \(1.70\) \\  & ST16 & 500 & \(-17\) & \(1.70\) \\  & ST13 & 300 & \(1.67\) & \(-6\) & \(2.17\) \\  & ST19 & 500 & \(1.67\) & \(-13\) & \(2.00\) \\ \hline \multirow{4}{*}{S3} & ST22 & 300 & \(1.67\) & \(-5\) & \(2.00\) \\  & ST26 & 500 & \(-10\) & \(2.00\) \\ \cline{1-1}  & ST24 & 300 & \(1.67\) & \(-12\) & \(2.00\) \\ \cline{1-1}  & ST28 & 500 & \(1.67\) & \(-12\) & \(2.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Experimental determination of linearity ratio depending on vehicle load.  
ambient vibration monitoring and vehicular load testing. By integrating this data into a calibrated finite-element model, we create a virtual representation of the physical bridge that reflects its actual mechanical behavior under service conditions. This enables not only the diagnosis of existing stiffness loss and deformation but also the simulation of performance under hypothetical future scenarios.  
This test-driven digital twin framework fills a critical and underexplored niche in the broader landscape of digital twin applications for civil infrastructure. Most existing studies in the field emphasize long-term monitoring and the use of Internet of Things technologies, which are often impractical to implement on a wide scale due to cost, technical complexity, or accessibility constraints. By contrast, the methodology adopted in this study offers a more accessible and scalable alternative, particularly for bridge managers and infrastructure owners who require periodic assessments rather than real-time surveillance. The presented approach demonstrates that even in the absence of continuous sensor data, a reliable and diagnostically valuable digital twin can be established using structured, short-term field testing.  
However, this approach also entails certain limitations. First and foremost, the digital twin developed here is inherently static in nature, capturing the bridge's condition at the time of testing rather than providing ongoing updates. As a result, it cannot autonomously detect temporal changes such as environmental degradation, cumulative fatigue, or progressive damage. Second, the validity of the model is bounded by the range of structural behaviors represented in the collected data; behaviors not triggered during the test campaign--such as extreme seismic responses or long-term thermal effects--are outside the model's predictive scope. Third, this implementation does not currently incorporate machine learning or data-driven adaptability, which limits its ability to evolve as new data become available.  
Despite these limitations, the proposed framework offers a replicable model for asset verification in scenarios where real-time monitoring is either unnecessary or unfeasible. It illustrates how the core principles of digital twins (virtual mirroring, data integration, and predictive simulation) can still be realized using test-based calibration methods. Furthermore, it contributes to bridging the conceptual and operational gap between finite-element model updating and full-fledged digital twin systems. As such, it offers a hybrid path forward for digital twin deployment in civil engineering: one that is both pragmatic and scientifically rigorous.