## 1 Introduction
In the literature, various studies have focused on identifying potential damages using NDT methods, including Operational Modal Analysis (OMA) tests [6; 7; 8; 9; 10; 11; 12; 13], fiber optic sensors [14; 15; 16; 17; 18; 19; 20; 21], laser-based measurement systems [22; 23; 24; 25], acoustic emission methods [26; 27; 28], ultrasonic testing [29; 30; 31; 32; 33], and computer vision, artificial intelligence, machine learning, and deep-learning-based approaches [34; 35; 36; 37; 38]. These methods facilitate both the reliable analysis of the current condition and the identification of the root causes of structural issues. Non-destructive methods are particularly advantageous for bridges where structural alterations are undesirable and for the preservation of historically significant bridges [39; 40; 41]. The Digital Twin (DT) paradigm has become a cornerstone in modern infrastructure management by enabling virtual representations of physical systems, continuously updated through sensor integration, computational modeling, and intelligent analysis. The concept, initially developed by NASA and formalized by [PERSON] and [PERSON] (2017) [42], has since evolved into a multidisciplinary framework applied in various domains such as manufacturing, medicine, and infrastructure [43; 44; 45; 46]. A DT typically comprises three fundamental elements: the physical asset, its virtual counterpart, and the data connection linking the two [42]. Depending on data connectivity and interaction, DTs are often classified as Digital Models, Digital Shadows, or full Digital Twins, while Predictive DTs incorporate AI-driven feedback loops for simulation and control [47; 48]. In recent years, the digital twin paradigm has been increasingly adopted for civil infrastructure systems, particularly for bridges, due to its potential for enabling data-driven diagnostics, predictive maintenance, and lifecycle management [49; 50; 51; 52; 53]. The integration of sensor fusion techniques, such as InSAR, computer vision, and machine-learning algorithms, has further enhanced the ability of digital twins to capture complex structural behaviors [53]. Additionally, model-updating approaches continue to play a central role in ensuring the fidelity for stiffness and damage assessment purposes [54; 55]. Building upon these advancements, this study proposes a test-assisted digital twin methodology specifically tailored for post-construction stiffness verification of concrete bridges. The service life of a bridge is directly related to the environmental impacts and the repetitive traffic loads it experiences. During the design phase, anticipated vehicular loads are considered, and these load conditions are incorporated into the design calculations. However, in some cases, a bridge may fail to exhibit sufficient resistance to these planned loading conditions after it becomes operational, resulting in stress and displacement values exceeding acceptable limits. This situation can significantly reduce the intended service life of the bridge. To prevent such issues, bridges are subjected to comprehensive static and dynamic load tests after construction and before being opened for service. These tests are of great importance to verify whether the stress, displacement, and acceleration values measured on the bridge elements remain within specified limits. It is critical to carefully plan these testing and loading procedures to avoid causing damage to the structure. There are documented cases of bridge collapses during the load testing phase [56]. However, when conducted according to proper loading protocols, these tests serve as effective tools to confirm the compliance of a bridge with its design specifications and to reliably evaluate its performance. In these tests, two types of vehicular load testing are performed: static and dynamic. In static load testing, trucks are positioned at predetermined locations on the bridge, and sensors installed on the structure record data such as accelerations, displacements, and stresses.