Tunnel disease recognition method

Abstract

The present invention discloses a tunnel disease recognition method. The method comprises the following steps: an inspection van travels through a tunnel, a 3D camera collects 3D data of a tunnel wallface, performs network partitioning on the 3D data, and forms a spatial grid diagram by using spatial point coordinates; the inspection van performs initial state scanning on the tunnel in a non- destructive state, to obtain an initial state spatial grid diagram; the inspection van performs scanning detection on the tunnel, to obtain a detection spatial grid diagram; and performs feature comparison and analysis on the obtained detection spatial grid diagram and the initial state spatial grid diagram, to obtain disease recognition information, wherein the disease recognition information comprises a disease position and a disease category. According to the method, all-around detection and three-dimensional network type point position detection are performed on an inner part of the tunnel, and state information of a tunnel inner wall can be obtained efficiently and accurately, so that a position and a type of a position occurred on tunnel surface can be accurately recognized, and maintenance personnel can perform fast repair, thereby ensuring safe operation of the tunnel.

Description

technical field [0001] The invention belongs to the technical field of tunnel maintenance, and in particular relates to a tunnel disease identification method. Background technique [0002] During tunnel construction and operation, due to geological conditions, climate conditions and various factors in the operation process, the surface of the tunnel is prone to water leakage, crack damage, tunnel freezing damage, corrosion and other diseases. The emergence of diseases will not only reduce the strength of concrete, cause damage to the tunnel surface, affect the structural safety, but may also reduce the service life of the tunnel and cause a huge safety hazard to the operation of rail transit. These diseases affect the safety of the tunnel, especially in certain To a certain extent, it threatens the normal operation of the tunnel. [0003] Traditionally, for the inspection of tunnel structural diseases, manual inspection, manual recording, taking photos and other means are ...

AI Technical Summary

Problems solved by technology

At present, in the process of tunnel disease detection, the calculation of the location of the disease is calculated linearly, which leads to the inability to quickly draw a conclusion during the calculation, which affects the efficiency of the calculation of the location of the disease. At the same time, when detecting the comprehensive index of the disease, there is no effective The correct detection of the system will lead to a large error in the comprehensive index of the disease, resulting in a decrease in the accuracy of the comprehensive index of the disease, affecting the positioning and maintenance of the tunnel

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