TACK - Tunnel Automatic CracK Monitoring using Deep Learning 

Project introduction

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. 


This research and development project aims to develop a novel technique to detect and monitor cracks in tunnel lining using a hybrid approach of deep learning and photogrammetry. With this technique, cracks can be automatically detected and measured from the imagery collected using customized mobile mapping systems to increase the monitoring efficiency and the overall safety of infrastructures. 

The core methodology includes the following steps:

Project partners

The TACK project is an ongoing collaboration between research and industrial partners: 

Project team

Andreas Sjölander


Concrete Structures Division

 KTH Royal Institute of Technology

Valeria Belloni 


Geodesy and Geomatics Division

Sapienza University of Rome

Andrea Nascetti

Associate Professor 

Geoinformatics Division

KTH Royal Institute of Technology

Mattia Crespi

Full Professor

Geodesy and Geomatics Division

Sapienza University of Rome

Roberta Ravanelli 


Geodesy and Geomatics Division

Sapienza University of Rome

Peter Östrand  

 Project Manager 


WSP Sweden

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/)

Project supporters