# 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  
Nowadays, engineering processes related to infrastructure engineering still rely heavily on manual processes performed by qualified bridge engineers. On-site inspections form the backbone of these methods, focusing on the assessment of critical elements within the structure, including decks, beams, joints, and supports. Specialists and technical experts visually examine these components, searching for signs of deterioration (e.g., corrosion, cracking, spalling, and misalignment) while also evaluating the bridge's load-bearing capacity in order to identify weaknesses that require intervention. While visual inspection is usually a straightforward and cost-effective process, reliance on subjective assessments and the potential for human error limits its efficiency. Hidden damage (e.g., internal cracks or flaws within elements' material) can go unnoticed without additional diagnostic tools. To address these gaps, specialized techniques, such as (i) non-destructive testing (NDT) methods like ultrasonic testing, magnetic particle inspection, and radiographic testing, are employed to detect internal defects and assess material properties without damaging the structure; (ii) simulated or live load testing are used to evaluate the bridge's performance under controlled conditions and help engineers estimate the real loadbearing capacity and determine the load-deflection curve; and (iii) hammer sounding is used to identify delamination in elements by listening to changes in sound pitch when tapping the surface. However, on-site inspections often rely heavily on the personal expertise and subjective assessments of bridge specialists and/or technical experts. While these assessments are effective at identifying visible issues, they have notable limitations that include the difficulty of detecting hidden defects and the labour-intensive nature of manual, in-person evaluations. To address these shortcomings, advanced technologies are increasingly being employed to complement traditional methods. This integration combines the practicality and familiarity of manual inspections with the precision and efficiency of modern tools. The result is a more comprehensive approach, leading to a better understanding of a bridge's condition, enhancing the ability to detect potential issues early and significantly improving maintenance and rehabilitation processes. Several emerging technologies can be particularly transformative for bridge operation, maintenance, rehabilitation and, ultimately, overall lifecycle management. Building Information Modelling (BIM) enhances collaboration among all stakeholders, enabling the development of information reach 3D models to reduce inefficiencies and communication gaps. In addition, several other technologies can be considered to significantly improve bridge operation, maintenance, and rehabilitation. These include advanced materials, smart construction technologies, digital twins combined with bridge management systems (BMSs), and structural health monitoring systems, all of which streamline and enhance the overall efficiency of bridge-related processes. The adoption of smart construction methods, including artificial intelligence (AI), drones, and LiDAR sensors, is revolutionizing bridge assessments. For example, drones equipped with sensors enable precise inspections in hard-to-reach areas, while 3D scanning [10], LiDAR, and GPS ensure accurate site positioning. Moreover, integrated systems like SCADA (Supervisory Control and Data Acquisition) enable the real-time monitoring of structural health, offering early warning signs of deterioration or damage. Digital twins can be employed to create virtual representations of physical assets, enabling real-time analysis and predictive maintenance. Modern bridge management systems (BMSs) integrate these technologies to enhance decision-making across a bridge's lifecycle.