Modern tunnels in hard rock are usually constructed by drill and blast with the rock reinforced by shotcrete (sprayed concrete) in combination with rock bolts. The irregular rock surface and the projection method of shotcrete lead to a tunnel lining of varying thickness with unevenly distributed stresses that affect the risk of cracking during shrinkage of the young and hardening material. Depending on water conditions, shotcrete is sprayed directly either onto the rock surface or over a drainage system, creating a fully restrained or an end-restrained structural system. In this paper, a method for nonlinear numerical simulations has been demonstrated, for the study of differences in stress build-up and cracking behaviour of restrained shotcrete slabs subjected to shrinkage. Special focus was given to the effects of the irregular shape and varying thickness of the shotcrete. The effects of glass fibre reinforcement and bond were implemented in the study by changing the fracture energy in bending and in the interaction between shotcrete and the substrate. The study verifies that an end-restrained shotcrete slab is prone to shrinkage induced cracking and shows the importance of a continuous bond to avoid wide shrinkage cracks when shotcrete is sprayed directly onto the rock.
Abstract. Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).
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