Bridges are well known as the most critical parts of infrastructures, serving as vital links for transportation networks. However, ensuring their proper functionality, structural safety, and durability requires careful and timely maintenance and regular assessment of their condition. A large infrastructure and a huge number of objects together with economic constraints make this task exceedingly difficult. The traditional bridge maintenance approach is primarily based on periodic visual inspections that can be accompanied by additional destructive or non-destructive tests. Such practice is well-established and accepted among practitioners, but data about bridge conditions, collected through a time-consuming process may pose challenges in interpretation. In the end, it might not provide a comprehensive picture of a bridge’s overall health. Therefore, advanced methods are now being used to enhance the overall process. They are based on the usage of new technologies such as unmanned aerial vehicles (drones) equipped with high-resolution cameras, light detection and ranging sensors (Lidar), and other sensors embedded within the structure to obtain more detailed information about bridge condition. This information can then be used together with computational methods like finite element modelling to assess bridge conditions and structural performance on a much higher level.
This article presents an approach to assess the condition of an existing case study bridge using state-of-the-art technologies and methods. The condition of the bridge is assessed using a 3d model generated from the photos of the bridge acquired by an unmanned aerial vehicle equipped with a high-resolution camera. Captured bridge geometry is subsequently employed to develop a finite element model, serving as a baseline for vibration-based damage detection analysis.

bridge assessment, unmanned aerial vehicle, finite element model updating, operational modal analysis, dynamic parameters