Abstract
Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.
Highlights
In the past decade, Europe, North America and South America suffered more than 50 bridge collapses due to deterioration-related issues, such as fatigue fracture and aging of materials, which culminated in more than 150 fatalities and close to 20 billion USD in overall losses, during which more than a million people were affected
The methodology comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary data for the second key step, (ii) 3D photogrammetry/construction, where one built 3D models that offer a permanent record of geometry for each bridge asset, which could be used for navigation and control purposes, (iii) crack identification and segmentation, where deep learning-based data analytics and modelling are applied for processing and analysing drone image data and to perform damage assessment
This work introduced an automatic crack segmentation methodology for the inspection of bridges via the use of segmentation of images obtained from Unmanned Aerial Vehicle (UAV)
Summary
Europe, North America and South America suffered more than 50 bridge collapses due to deterioration-related issues, such as fatigue fracture and aging of materials, which culminated in more than 150 fatalities and close to 20 billion USD in overall losses, during which more than a million people were affected. The interaction of these factors, their dependence on various variables, and their negative synergy effect on performance of the bridge, are hard to be detected and assessed by the conventional inspection procedures, see e.g., Okasha and Frangopol [4], Phares et al [5], and Liu et al [6] This means that the current damage and fatigue detection procedures produce inspection and monitoring tools which are non-resilient and are not capable of coping with the ever-changing social and economic needs. To tackle the problems and improve the existing bridge management practices, a new knowledge with proactive methods and tools is needed In this connection, in this paper, UAV-assisted bridge inspection methodology is proposed for improving inspection accuracy and pinpointing defects (such as cracks in steel elements, fractures in concrete elements, etc.) early on.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
