TACK - Tunnel Automatic CracK Monitoring using Deep Learning
Rock tunnels in Sweden are normally supported with a thin layer of fibre reinforced shotcrete in combination with rock bolts. Cracks in the shotcrete could lead to a failure of the support system and their existence and width should, therefore, be noted during the inspections of tunnels that are routinely performed. In the latest years, this work has been performed by in-situ surveys that are expensive and time-consuming. Recently, several studies highlight the potential of semi-automatic methods where a mobile mapping equipment (usually mounted on a vehicle) is used to capture the scene and to reconstruct the 3D model of a tunnel using a set of geomatics sensors (i.e., visible and infrared cameras, laser scanning, IMU). This digital representation of the tunnel is subsequently analyzed manually by visual inspection with the aim of seeking the crack and mark its extent. It is clear that, due to a large amount of collected data, these methods are inefficient and affected by human errors.
The aim of this research and development project is to investigate and develop a new technique to detect cracks on tunnel lining and bridges using a hybrid approach of deep learning and photogrammetry. With this technique, cracks will be automatically detected and measured from the imagery acquired using customized mobile mapping systems which leads to a highly efficient monitoring that can increase the overall safety of infrastructures.
The TACK project is an ongoing collaboration between research and industrial partners:
- KTH Royal Institute of Technology
- Sapienza University of Rome
- WSP Sweden.
Post Doc Researcher at University of Rome La Sapienza
Project Manager at WSP Geoinformatics
Project awards and achievements
The project was selected by the Royal Swedish Academy of Engineering Sciences as one of the top 100 innovative research projects (IVA's 100 list 2020 "From knowledge to sustainable competitiveness" - www.iva.se/projekt/research2business/ivas-100-lista-2020/)