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Neural network based rock destroy strength determination method

A neural network and failure strength technology, which is applied in neural learning methods, biological neural network models, and testing material strength by applying stable tension/pressure. It can solve problems such as failure, difficulty in accurate measurement of rock mass strain, and nonlinearity.

Inactive Publication Date: 2013-07-31
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

[0003] For a variety of rocks, the relationship between the applied stress and strain is complex and non-linear, and the internal structure of the rock mass may be damaged, resulting in tiny cracks, etc., making it difficult to accurately measure the strain of the rock mass.

Method used

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  • Neural network based rock destroy strength determination method
  • Neural network based rock destroy strength determination method
  • Neural network based rock destroy strength determination method

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Embodiment Construction

[0016] In order to make the above objects, features and advantages of the present invention more obvious and comprehensible, the present invention will be further described in detail below in combination with relevant theories and specific implementation methods used.

[0017] Laboratory data for 7 types of rocks were used, including: sandstone, quartzite, marble, limestone, granite, dolomite, coal. Data includes uniaxial tension, uniaxial compression, and triaxial compression. For uniaxial tensile tests , ; for uniaxial compression tests , ; Triaxial compression test , . The specific parameters of related rocks are shown in Table 2.

[0018] Table 2 Specific parameters of relevant rocks (including uniaxial tension, uniaxial compression and triaxial compression tests)

[0019]

[0020] Note: The applied load increases with the number of tests.

[0021] Predictions are made using a feedforward neural network (FFNN). The effective use of FFNN must first determ...

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Abstract

The invention discloses a neural network based rock destroy strength determination method. The method is characterized in that a neural network is used to research the rock strength criterion under a uniaxial or triaxial loading condition, various collected rock related data are randomly divided into training and verifying subsets, the compressive strength and the smallest principal stress are used as input values and the largest principal stress value is used as an output value to train the neural network, and the trained neutral network is used to predict largest principal stress value during the test rock destroy. The method mainly comprises a step of experiment data analysis and a step of ANN based largest principal stress value prediction. The method shows the mean square deviation of the neutral network prediction result is reduced by 30-40%, and the determination coefficient is increased by 0.05-0.08 and is more close to 1. The use of the ANN to predict the rock strength has a wide adaptive loading range, so the method is suitable for rock-kind-changeable complex non-linear conditions, and is flexible and accurate.

Description

technical field [0001] The invention relates to the research on rock failure strength in geotechnical engineering, in particular to a method for determining rock failure strength based on neural network. Background technique [0002] For geotechnical design, the strength of rock is one of the most important considerations. Several years of research have resulted in some empirical rock strength guidelines. In these guidelines, the maximum principal stress value is the uniaxial compressive strength and the minimum principal stress function, and its coefficients are obtained by inversion regression on the experimental data. However, the empirical strength criterion cannot predict completely and accurately for a large range of loading stress domains and various types of rocks. This is due to the fact that the data used in the parameter regression is a specific type of rock and a specific stress range. If the criterion after inverting the parameters is used to predict th...

Claims

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Application Information

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IPC IPC(8): G01N3/08G06N3/08
Inventor 刘文生吴作启崔铁军由丽雯杨逾邵军张媛孙琦杜东宁
Owner LIAONING TECHNICAL UNIVERSITY
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