Surrounding rock grading method

Abstract

The invention provides a surrounding rock grading method, and belongs to the field of tunnel engineering. The surrounding rock grading method comprises the following steps: constructing a sample library, wherein the sample library comprises drilling parameters and surrounding rock grades of corresponding positions; constructing a surrounding rock grading model based on a neural network, and substituting the sample library into the surrounding rock grading model for training; and inputting drilling parameters into the trained surrounding rock grading model to obtain the surrounding rock grade of the corresponding position. According to the surrounding rock grading method, the surrounding rock grading model is constructed through the neural network technology, surrounding rock grades are automatically judged according to drilling parameters generated in the drilling process, investment of constructors can be effectively reduced, and potential risks in the surrounding rock grading evaluation process are reduced.

Description

technical field [0001] The present application relates to the field of tunnel engineering, in particular to a surrounding rock classification method. Background technique [0002] Tunnel engineering is essentially a geological engineering. In the process of tunnel construction, various stratum environments will be encountered. At the same time, various geological disasters will inevitably be faced during the excavation process. Correct surrounding rock classification is conducive to timely adoption of Effective support measures to ensure construction safety and construction progress. [0003] At present, there are many classification methods for surrounding rocks, such as: fuzzy mathematical theory, gray prediction and fractal geometry and other non-linear surrounding rock classification methods. However, in actual engineering, because the indicators of the above methods often require on-site testing of classification indicators such as rock mass strength, the personal safe...

AI Technical Summary

Problems solved by technology

However, in actual engineering, because the indicators of the above methods often require on-site testing of classification indicators such as rock mass strength, the personal safety of construction personnel cannot be guaranteed.

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