Method suitable for TBM tunneling tunnel surrounding rock grading joint prediction and application

A technology of surrounding rock classification and prediction method, applied in neural learning methods, special data processing applications, biological neural network models, etc. The effect of subjectivity, increased objectivity, objective results

Active Publication Date: 2019-08-09
SHANDONG UNIV
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Problems solved by technology

[0007] In order to solve the above-mentioned problems, this disclosure proposes a method and application of surrounding rock graded joint prediction suitable for TBM excavation tunnels. This disclosure has a feedback mechanism based on advanced geological prediction and...

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  • Method suitable for TBM tunneling tunnel surrounding rock grading joint prediction and application

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[0031] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0033] It should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof...

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Abstract

The invention provides a method suitable for TBM tunneling tunnel surrounding rock grading joint prediction and application. The method comprises: preliminarily judging the classification of the surrounding rock of the unexcavated section in front of the tunnel TBM according to geological exploration data, detecting the condition of the surrounding rock of the unexcavated section by utilizing an advanced geological prediction method, obtaining surrounding rock parameters, and correcting and preliminarily judging a surrounding rock classification result according to the obtained detection data;according to the obtained surrounding rock parameters of the non-excavated section obtained through the advanced geological forecast, using a data analysis method for the non-excavated section, and obtaining TBM tunneling parameters of the section; and establishing a neural network model, calling the obtained TBM tunneling parameters as input values to the neural network model for prediction, outputting non-excavated section surrounding rock classification, and obtaining a final prediction result by combining a corrected surrounding rock classification result.

Description

technical field [0001] The disclosure belongs to the field of surrounding rock classification of tunnel engineering, and relates to a combined prediction method and application of surrounding rock classification suitable for TBM excavation tunnels. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Full-face rock tunnel boring machine TBM (tunnel boring machine) is widely used in deep buried long tunnel projects due to its many advantages such as fast excavation speed, high efficiency, safety, environmental protection, automation and high degree of informatization. [0004] During the tunnel construction process, the geological conditions ahead are ever-changing, ambiguous and unpredictable. In particular, compared with traditional construction methods such as drilling and blasting, TBM is more sensitive to geological changes, and the feasi...

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Application Information

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IPC IPC(8): G06F16/21G06N3/04G06N3/08
CPCG06F16/212G06N3/084G06N3/045
Inventor 薛翊国屈聪邱道宏李广坤孔凡猛张开崔久华
Owner SHANDONG UNIV
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