# A BIM-Enabled Workflow for the Rehabilitation of Heritage Steel Bridges
## 2 Current State of the Art
### Traditional Methods and Emerging Technologies for Bridge Inspection, Monitoring and Management
As highlighted by the literature [11], such systems allow for efficient condition assessments and the optimization of maintenance strategies based on structural health and risk analyses. Often, BMSs limit their scope to modules [12], i.e., (i) data management, dealing with data about bridges and the status oftheir components; (ii) diagnosis, consisting of condition rating and deterioration assessment; (iii) prognosis, gathering all the activities connected to the prediction of future bridge conditions; and (iv) decision-making, identifying optimal management actions, and guiding decisions based on engineering judgment. The modules are generally interconnected. While diagnosis and prognosis rely on data pertaining to bridges and their components, decision-making is influenced by the outcomes of diagnosis and prognosis and can also impact data management. Structural health monitoring (SHM) systems deploy sensors [13] (e.g., fiber optic sensors (FOS), piezoelectric sensors, vision-based displacement measurement systems, and magnetostrictive sensors (MSSs)) to monitor factors like stress, structural vibration, and/or temperature. These systems continuously track structural health, allowing operators to identify damage early and mitigate risks. For example, MEMS (micro-electromechanical system) sensors are increasingly used for their precision and compactness, enabling data capture on bridge behaviour over time. The integration of advanced computer-aided design (CAD) tools with finite element analysis (FEA) capabilities provides engineers with a powerful framework for designing and evaluating bridges [14; 15], especially in scenarios requiring extensive rehabilitation. Modern CAD tools allow engineers to create detailed and accurate 3D representations of bridges, incorporating geometric, material, and even environmental factors. When combined with advanced FEA capabilities, these models can simulate a structure's behaviour under various conditions, such as multiple load distributions, environmental stresses, and potential failure scenarios. Using advanced numerical models, the CAD-FEA approach enables bridge specialists to (i) identify weak points before the rehabilitation begins and (ii) evaluate multiple design alternatives and test extreme conditions without physical prototyping. The added value of these tools can be further augmented by incorporating feedback from real-world bridges. Leveraging the capabilities of IoT-enabled monitoring systems [5] that provide reliable data on stresses, vibrations, and/or environmental conditions on existing bridges, digital models are updated and augmented to reflect actual conditions rather than purely theoretical scenarios. With drones equipped with advanced sensors becoming more and more accessible, they are frequently employed to access hard-to-reach areas (e.g., the underside of bridge decks or tall piers), where manual inspections are hazardous and/or impractical. Taking this one step further, the rise in the use of artificial intelligence (AI) coupled with drones enhances both efficiency and accuracy as AI algorithms, combined with high-resolution images collected by drones, are increasingly used to automatically detect cracks, corrosion, and other structural anomalies. This combined approach significantly reduces reliance on manual assessments [16], while enhancing reliability. Furthermore, the integration of AI enables the classification of detected defects based on severity, prioritizing areas that require immediate attention and streamlining maintenance workflows. Another trending technology for infrastructure assessment is satellite-based monitoring, which is used to complement the aforementioned technologies by providing macroscopic data on bridge performance over time. Synthetic Aperture Radar (SAR) and similar satellite techniques enable the detection of structural movements (e.g., displacements or vibrations), which may indicate emerging issues in the bridge's load-bearing capacity.