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Method for building fine grain tailing project property index estimation empirical formula based on linear regression

A technology of fine-grained tailings and engineering nature, applied in calculation, special data processing applications, instruments, etc., can solve problems affecting the reliability of design results, lack of test data, high test cost, etc., to save geotechnical test investment and high calculation efficiency , reliable results

Active Publication Date: 2014-02-05
WUHAN SURVEYING GEOTECHN RES INST OF MCC
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Problems solved by technology

[0004] The primary problem in the stability evaluation and design of tailings ponds is to determine the physical and mechanical parameters of the tailings medium. Especially in the preliminary design stage of tailings ponds, there is generally a lack of test data. Even if the project has some test data, it is often due to the High cost and small amount of data, which affects the reliability of design results

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  • Method for building fine grain tailing project property index estimation empirical formula based on linear regression
  • Method for building fine grain tailing project property index estimation empirical formula based on linear regression
  • Method for building fine grain tailing project property index estimation empirical formula based on linear regression

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[0033] The following describes the implementation of the present invention by specific examples, please refer to figure 1 The invention is a flow chart of a method for establishing an empirical formula for estimating an engineering property index of fine-grained mine tailings based on linear regression.

[0034] 1) Collection and sorting of fine-grained tailings physical and mechanical index sample data: through the collection and sorting of engineering geological survey reports and geotechnical test results of 39 tailings ponds across the country, the sampling depth and physical properties of iron ore tailings were collected and counted , Direct shear test strength index, compression index, and permeability index are used as sample data, and a total of 578 data records are included;

[0035] 2) Preliminary screening of sample data: For the sample data obtained in step 1), use the commonly used μ±3σ method to eliminate abnormal data. When the test data sample n is greater tha...

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Abstract

A method for building a fine grain tailing project property index estimation empirical formula based on linear regression includes the following steps: 1) collecting and settling fine grain tailing data physico-mechanical index sample data; 2) adopting the mu+ / -3sigma abnormal value rejection principle to conduct preliminary screening on the sample data; 3) conducting secondary screening on the sample data based on mathematical statistics; 4) building a linear regression mathematical model; 5) determining model parameters; 6) conducting model goodness inspection; 7) building the fine grain tailing project property index estimation empirical formula. The method has the advantages of providing forceful support for scientificity and reliability of a data regression analysis result through massive test data information, providing reliable parameter choice for fine grain tailing base stability evaluation and design in future, greatly saving project investment cost and soil engineering test investment, avoiding unnecessary project building cost, enabling a research result to have popularization and application value and being simple, practical, reliable in result, efficient in computing and the like.

Description

technical field [0001] The invention belongs to the field of geotechnical engineering investigation and design, and relates to a method for establishing an empirical formula for estimating an engineering property index of fine-grained mine tailings based on linear regression, and specifically relates to establishing an empirical formula for estimating an engineering property index of fine-grained mine tailings based on linear regression analysis. The quantitative analysis of fine-grained tailings engineering property indicators based on mathematical statistics and least squares regression analysis is used to estimate its empirical formula. technical background [0002] Tailings is a kind of slag, which is discharged in the form of slurry and stored in the tailings pond. The stability of the tailings accumulation dam directly affects the normal use of the tailings pond. Statistical analysis of a large number of tailings pond disease accidents shows that fine-grained tailings ...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 程江涛于沉香蔡清万凯军陈定安
Owner WUHAN SURVEYING GEOTECHN RES INST OF MCC
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