# Genetic Algorithm-Based Model Updating in a Real-Time Digital Twin for Steel Bridge Monitoring
## 1 Introduction  
The aging global infrastructure, particularly with regard to bridges, demands innovative approaches to ensure structural integrity, safety, and longevity. Structural Health Monitoring (SHM) has emerged as a critical tool for early damage detection and preventive maintenance, yet traditional methods often rely on periodic inspections or offline analyses, which lack real-time insights. The introduction of digital twin technology--dynamic, virtual replicas of physical systems--has revolutionized SHM by enabling continuous, data-driven monitoring and predictive analytics. Digital twins integrate real-time sensor data, physics-based models, and machine learning to mirror a structure's behavior, offering unprecedented capabilities to predict failures, optimize maintenance, and extend service life. The inherent challenges in predicting structural behavior under dynamic loads requires the development of accurate numerical models, predominantly constructed using the finite element method (FEM) [1, 2, 3]. These models serve as critical tools for simulating a structure's real-world conditions, enabling predictions of its dynamic response (e.g.,resonance frequencies, displacement patterns, and stress distributions) under operational or extreme loading scenarios [4]. Recent studies have highlighted the application of FEM in analyzing composite steel-UHTCC (Ultra-High Toughness Cementitious Composite) bridge structures, offering valuable insights into their mechanical performance. For instance, [PERSON] et al. [5] investigated the local instability and interactive mechanisms of UHTCC-encased rectangular steel tubular columns, demonstrating the material's ability to enhance structural stability under compressive loads. Similarly, [PERSON] et al. [6] explored the flexural performance and crack width prediction of steel-UHTCC composite bridge decks with wet joints, emphasizing the importance of accurate FEM modeling for predicting serviceability limits. Additionally, [PERSON] et al. [7] examined the flexural behavior of novel profiled steel-UHTCC assembled composite bridge decks, showcasing the potential of FEM in optimizing design parameters for improved load-bearing capacity. Such simulations are indispensable for optimizing maintenance strategies and ensuring long-term structural integrity. However, constructing reliable numerical models is often hindered by severe data limitations: historical records of material properties (e.g., concrete compressive strength, steel yield stress), boundary conditions (e.g., foundation rigidity, joint fixity), and damage states (e.g., crack propagation, corrosion) are frequently incomplete, outdated, or absent entirely [8; 9]. Consequently, engineers must rely on idealized assumptions about material behavior, structural connectivity, and degradation mechanisms--factors that profoundly influence model accuracy but rarely reflect real-world complexities. For instance, simplifications in modeling aging concrete's time-dependent stiffness loss or poorly documented retrofit interventions can lead to significant discrepancies between simulated and observed behavior. To address these limitations, structural health monitoring (SHM) systems have emerged as a cornerstone of modern infrastructure management [10]. By deploying sensor networks (e.g., accelerometers, strain gauges, temperature sensors) to collect real-time data on structural responses--such as vibration modes, load distributions, and environmental effects--SHM provides empirical insights into a structure's operational state. These datasets are pivotal for diagnosing anomalies (e.g., localized stiffness reduction, unbalanced loads) and informing data-driven model calibration. For example, SHM-derived modal parameters (e.g., natural frequencies, damping ratios) can be used to iteratively refine FEM assumptions through optimization algorithms (e.g., genetic algorithms, Bayesian inference), minimizing the gap between simulated and measured behavior [11; 12].