## 1 Introduction
In dynamic load testing, the dynamic effects created by a truck passing over the bridge at specific speeds are measured using sensors placed on the structure. Increases in the truck's weight and speed during the test result in increased stress and displacement values [57]. For this reason, load tests are conducted with vehicles of varying weights and speeds, designed to be compatible with the bridge's design. The vehicles used in these tests typically weigh more than 20 tons and move at speeds ranging from 10 to 80 km/h [58; 59]. The frequencies, mode shapes, stresses, and displacements obtained from static and dynamic load tests are used to calibrate the finite-element model of the structure. In this way, the actual finite-element model representing the bridge is obtained based on real bridge data [60; 61; 62; 63; 64; 65; 66; 67; 68; 69]. In addition, stiffness separation methods have been proposed to enhance damage identification and reduce computational cost in large-scale truss structures by focusing on partial-model-based updating strategies [70; 71]. Although such approaches target different structural systems and objectives, they reflect the growing trend of incorporating model refinement techniques to improve structural diagnostics. In civil and structural engineering, DTs offer a framework for monitoring, diagnostics, and lifecycle assessment of bridges and other infrastructures [72; 73]. Studies have demonstrated the use of computer vision, sensor fusion, and InSAR techniques for twin-driven monitoring [74], and data-driven models such as DCNN-LSTM for simulating thermal displacements in large-span bridges [75]. Semantically enriched modelssuch as BIM-integrated DTs have also enhanced asset interoperability and documentation in smart cities and cultural heritage domains [76; 77]. Common architectural frameworks, including ISO 23247-2 and RAMI 4.0, define structural guidelines for integrating these technologies [78; 79]. The primary objective of this study is to assess whether the Cho'ponota L1 Bridge has undergone a loss of stiffness due to excessive deflection observed after the application of dead loads and to evaluate the structural usability under service conditions. To this end, a comprehensive field campaign was conducted, including visual inspections, vehicular static and dynamic load testing, and ambient vibration measurements via the OMA method. These procedures were implemented to determine the bridge's dynamic characteristics--namely, its natural frequencies and mode shapes--and to identify the sources of the observed structural response anomalies. The resulting test data, including frequency content, displacement values, stress distributions, and mode shapes, were used to calibrate a finite-element model of the bridge. While these methods follow established structural assessment protocols, the present study advances the methodology by framing the calibrated FE model as a test-assisted Digital Twin. This concept represents a novel intermediary approach between traditional one-off assessments and fully integrated, continuously updated digital twin systems. Despite the increasing interest in digital twin applications in civil infrastructure, there is a clear research gap regarding field-validated, limited-duration digital twin implementations, particularly in scenarios where full-time monitoring is infeasible. By integrating high-fidelity test data into a dynamic and diagnostically interpretable numerical model, this study provides a replicable digital twin framework for post-construction verification and stiffness assessment in existing bridges. The following sections detail the testing procedures, sensor layout, and data processing stages that form the basis of test-assisted digital twin structural performance evaluation.