ABSTRACT

Every year 4700km of new tunnels are built with an annual growth value of 7%. This results in the fact, that the total amount of tunnels which must be inspected will increase in the future. Furthermore, their operation reliability must be guaranteed with safe and cost-efficient means. Building upon Amberg Technologies’ and Amberg Engineering’s experience from several international tunnel projects, we have come up with a new platform which allow us to optimize the tunnel inspection process. Today, tunnel assessment is mostly based on a slow and subjective human inspection process. Automatic defect detection will provide a more objective and quantifiable approach to the task of tunnel inspection. By manual inspection it is difficult to assess anomalies objectively, especially cracks at an accuracy of 0.2 mm with random size and shape. It is also difficult to compare them to the historic state of previous assessment campaigns. Thus, it makes sense to aim at an automated detection procedure and a fully digitized workflow for tracking the defects over time. Recent developments in the field of big data and artificial intelligence can be applied to the field of tunnel inspection and bring it to a higher degree of automatization. Amberg is focusing on developing a computer-based and BIM-compatible implementation of the new methods for damage classification and tunnel assessment. This paper will explain the principle of the new platform and show some initial test projects.