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Tunnel face surrounding rock intelligent grading method and device based on deep learning

A technology of deep learning and grading method, applied in the field of tunnel exploration, can solve the problems of incomplete evaluation, poor accuracy, low efficiency, etc., to facilitate identification and analysis, improve model accuracy, and eliminate irrelevant information.

Pending Publication Date: 2022-03-04
广西路信云信息技术有限公司 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] The purpose of the present invention is to overcome the problems of low efficiency, insufficient evaluation and poor accuracy existing in the prior art, and provide an intelligent classification method for tunnel face surrounding rock based on deep learning

Method used

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  • Tunnel face surrounding rock intelligent grading method and device based on deep learning
  • Tunnel face surrounding rock intelligent grading method and device based on deep learning
  • Tunnel face surrounding rock intelligent grading method and device based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] Such as figure 1 As shown, a deep learning-based intelligent classification method for tunnel face surrounding rock includes the following steps:

[0063] Input the photos of the face to be predicted, segment and process the photos of the face to be predicted into a group of sub-images, and transfer the group of sub-images to the pre-built surrounding rock classification prediction model for prediction, and according to each sub-image The statistical information of the surrounding rock grade prediction result output the surrounding rock grade prediction result of the face photo to be predicted;

[0064] Wherein, the surrounding rock grade prediction results include Grade III, Grade IV and Grade V; the segmentation process is to cut the photo of the face to be predicted into n*n sub-images of the same size, and the value of n is 3 , 4, 5 (too large sub-images will lead to misjudgment of the recognition results, and too small sub-images will make it difficult to obtain b...

Embodiment 2

[0080] This embodiment is a specific embodiment when n*n is 3*3 in embodiment 1.

[0081] Due to the special properties of the surrounding rock of the face, that is, the surrounding rock of the face has high hardness, few fractures, and good overall integrity, but joints and fissures are particularly developed in a small local area, or small karst caves and weak interlayers are developed. The ratio is very small, and the prediction result of the model is often grade III or grade IV surrounding rock, but the real grade of surrounding rock is grade V.

[0082] Therefore, in order to make the prediction result closer to the real value, in this embodiment, the required predicted face photo is first divided into 9 sub-images in 3*3, and then the sub-images are sequentially transmitted to the surrounding rock classification prediction model for prediction, and each sub-image is output Image prediction result, let N 5 , N 4 , N 3 They are the number of surrounding rocks of level V...

Embodiment 3

[0135] Such as Figure 11 As shown, an intelligent grading device for tunnel face surrounding rock based on deep learning includes at least one processor, and a memory connected to the at least one processor in communication; the memory stores information that can be processed by the at least one processor. Instructions executed by the processor, the instructions are executed by the at least one processor, so that the at least one processor can execute the method for intelligently grading tunnel face surrounding rock based on deep learning described in the foregoing embodiments. The input-output interface may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data; the power supply is used for providing electric energy for the tunnel face surrounding rock intelligent grading device based on deep learning.

[0136] Those skilled in the art can understand that all or part of the steps for implementing the above-mentioned method embodiments c...

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Abstract

The invention relates to the field of tunnel exploration, in particular to a tunnel face surrounding rock intelligent grading method and equipment based on deep learning. The method comprises the following steps: marking and preprocessing tunnel face surrounding rock photo samples, and establishing a face image database for training; and then, a surrounding rock grading prediction model based on deep learning is established, and the model is trained according to the tunnel face image database, so that a prediction model with more accurate identification is constructed. According to the method, the to-be-detected tunnel face picture is segmented by adopting image segmentation, each sub-image is predicted, and finally, comprehensive evaluation and prediction are performed according to the prediction result of each sub-image, so that the data on which the prediction result is based is more comprehensive, and efficient and accurate discrimination of the tunnel face surrounding rock is also realized.

Description

technical field [0001] The invention relates to the field of tunnel survey, in particular to a method and equipment for intelligently classifying the surrounding rock of a tunnel face based on deep learning. Background technique [0002] Surrounding rock classification is an important basic work in tunnel engineering. It is an important basis for the design of tunnel support structures and determination of excavation methods, as well as the basis for tunnel safety risk assessment. From the perspective of the development history of the surrounding rock classification in China, the traditional main methods of surrounding rock classification for tunnel engineering include single-factor index classification, multi-factor index classification, and multi-factor comprehensive classification methods. Its development trend is from single factor index to multi-factor index comprehensive evaluation, from qualitative analysis and empirical judgment to quantitative analysis, from manual ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/13G06T5/00G06N3/04G06N3/08
CPCG06T7/0002G06T7/11G06T7/13G06N3/08G06T2207/20081G06T2207/20132G06T2207/30204G06N3/045G06T5/90
Inventor 宋冠先梁铭解威威马文安朱孟龙彭浩覃晓凤胡以婵
Owner 广西路信云信息技术有限公司
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