# Genetic Algorithm-Based Model Updating in a Real-Time Digital Twin for Steel Bridge Monitoring
## 1 Introduction
This synergy between experimental observation and computational modeling not only enhances predictive accuracy but also enables adaptive maintenance planning, ensuring interventions are prioritized based on quantifiable risk rather than heuristic judgment. Digital twins originated in the manufacturing and aerospace industries but have gained traction in civil engineering over the past decade. Pioneering studies by [PERSON] [13] and [PERSON] et al. [14] established their potential for infrastructure monitoring, emphasizing the synergy between sensors and computational models. For bridges, digital twins have been applied to track dynamic responses, assess load capacities, and simulate extreme-event scenarios [15; 16; 17; 12; 18; 19]. Key challenges include achieving model fidelity, handling sensor noise, and ensuring computational efficiency for real-time updates. A critical component of digital twins is operational modal analysis (OMA), which extracts modal parameters (natural frequencies, damping ratios, mode shapes) from ambient or operational vibrations. Techniques like Stochastic Subspace Identification (SSI) have become cornerstones of OMA due to their robustness in output-only scenarios [20]. Recent advancements integrate SSI with finite element (FE) models to calibrate digital twins [21; 22], though discrepancies between numerical and experimental results persist due to material variability and modeling assumptions. Model updating--a process to align FE models with real-world data--has been addressed through optimization algorithms. Genetic algorithms (GA) and Bayesian inference are widely used to minimize errors in frequencies and mode shapes [23; 24; 25; 26]. However, few studies have combined high-density sensor networks, SSI-based modal identification, and multi-objective optimization in a unified digital twin framework, particularly for steel-truss railway bridges with complex dynamic behavior. This study advances digital twin technology for bridge SHM by developing a real-time monitoring system that integrates (a) high-resolution sensor data from 18 accelerometers strategically placed to capture modal shapes; (b) SSI-based OMA to extract free-vibration responses and identify physical modes using stabilization diagrams; and (c) finite element model calibration via a genetic algorithm, optimizing steel elastic modulus to align numerical and experimental frequencies and mode shapes. The methodology's efficiency is demonstrated through a case study on a three-span steel-truss railway bridge, showcasing how real-time data and model updating enhance predictive maintenance. The integration of OpenSeesPy [27] for FE modeling and PyGAD [28] for optimization underscores the practicality of the approach for field applications. Following this, Section 2 details the bridge's geometry and sensor configuration. Section 3 outlines the SSI methodology and modal identification process. Section 4 presents the FE model development and calibration results, and Section 5 discusses implications for SHM. The conclusions highlight the twin's role in transforming bridge maintenance paradigms. By bridging gaps between sensor data, computational models, and real-world behavior, this work provides a scalable blueprint for digital twin deployment in critical infrastructure.