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The monitoring and prevention of cracks in tunnel structures is an essential problem and the traditional methods are arduous to suffice the rapid increasing of tunnel mileage. Many researches, nowadays, mainly focus on the automatic identification and intelligent detection of cracks. However, crack identification and localization of tunnel structures are merely preliminary for effective prevention. Therefore, how to intelligently analyze and predict the further evolution of cracks will be the top priority of tunnel disaster prevention. In this article, a novel method is proposed which combinates deep learning (DL) techniques and finite element method to detect and analyze the cracks which is significant for the intelligent prevention. The innovation of this article lies that the position of cracks is identified automatically with the DL based on Mask R-CNN method and the evolution of cracks is predicted efficiently with FEM model.

Intelligent monitoring of tunnel structures based on vision measurement technologies