Combined Convolutional Neural Networks and Fuzzy Spectral Clustering for Real Time Crack Detection in Tunnels


A computer vision module is proposed for crack detection in tunnels, a challenging process due to the low visibility, the curvature from, and the structures of the cracks which, though being very narrow in width, they are very deep. Our system is embedded on a robot which surveys tunnels in real-time as it is moving in the infrastructure. Initially, a Convolutional Neural Network is employed to detect the cracks which, however, yields only approximate regions due to the great complexity of the scene. Then, a combined fuzzy spectral clustering is then introduced to refine the detected crack regions exploiting spatial and orientation coherency. The algorithms have been tested in real-life tunnels in Egnatia Highway. Our scheme yields high detection accuracy than existing methods and the capacity of the robot to touch the crack to allow in-situ measurements within a precision of 2-3cm in a tunnel of 7m height.

Current Challenges and Future Trends in Analytical Fragility and Vulnerability Modelling

Abstract: The lack of empirical data regarding earthquake damage or losses has propelled the development of dozens of analytical methodologies for the derivation of fragility and vulnerability functions. Each method will naturally have its strengths and weaknesses, which will consequently affect the associated risk estimates. With the purpose of sharing knowledge on vulnerability modeling, identifying shortcomings in the existing methods, and recommending improvements to the current practice, a group of vulnerability experts met in Pavia (Italy) on April 2017. Critical topics related with the selection of ground motion records, modeling of complex real structures through simplified approaches, propagation of aleatory and epistemic uncertainties, and validation of vulnerability results were discussed, and suggestions were proposed to improve the reliability and accuracy in vulnerability modeling

Project Interactions

Latest News

Contact us



This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No  769129