Geological detection method for tunnel face

Abstract

The invention discloses a method for detecting the geological condition of a tunnel face in the tunnel construction process. The method comprises the steps that based on a deep learning algorithm, a sample data set is used for training an image instance segmentation neural network to obtain an image instance segmentation model, and the sample data set comprises a plurality of rock slag sample images of different geological levels; each rock slag sample image is provided with a geological category label, and the contours of blocky rock slag and flaky rock slag are marked in the images. And the image instance segmentation model is called to analyze the to-be-identified rock slag image to obtain contour data segmented in the to-be-identified rock slag image corresponding to each rock slag in the solid slag and a probability value of the solid slag belonging to each geological level. And the content values of the blocky rock slag, the flaky rock slag and the rock powder in the solid muck are calculated according to the contour data, and the geological level of the tunneling tunnel face is determined in combination with the initial classification result, so that the geological analysis accuracy is not reduced while the defect of manual detection of the geological condition of TBM tunnel construction is overcome, and the intelligent degree of tunnel construction is improved.

Description

technical field [0001] The present application relates to the technical field of tunneling, in particular to a method for detecting geological conditions of a tunnel face. Background technique [0002] When TBM (Tunnel Boring Machine, full-face tunnel boring machine) passes through weak surrounding rocks such as soft rocks, fault zones and weathered rocks, accidents such as jamming, landslides, and water inrush often occur due to strong extrusion deformation and damage. To predict the surrounding rock conditions during tunneling construction, it is usually necessary to analyze the geology of the face. [0003] Traditional geological analysis usually uses manual observation, expert experience or radar detection. These methods all rely on manual means, and it is difficult to judge geological risks. Most mountain tunnels or high-altitude areas are not conducive to manual detailed geological survey work, which further increases the difficulty of geological analysis relying on m...

AI Technical Summary

Problems solved by technology

[0005] In the above-mentioned related technologies, due to the influence of slag sheet stacking, occlusion, coverage, powdery and water content, and cutter head cutting, edge extraction, watershed and other image processing extract slag features The effect is poor, resulting in low recognition accuracy of common machine learning classification models

Under good geology, the particle size of rock slag is small, a large number of flaky rock slags are stacked together, the extracted contours are blurred, multiple contours are extracted from a single block, and the contours of multiple pieces of slag are connected together; The water content of the slag is relatively high, the boundary of small particles is not clear, it is difficult to distinguish, and the recognition accuracy is low

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