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Microstructure Characteristics And Engineering Mechanics Of Metamorphic Rocks In Longmen Mountain Research On Nature Correlation

Posted on:2021-06-21Degree:MasterType:Thesis
Country:ChinaCandidate:B SuFull Text:PDF
GTID:2480306473983939Subject:Geological Engineering
Abstract/Summary:PDF Full Text Request
In engineering mechanics activities,you need to rely on the parameters you have obtained in the experiment to establish the geomechanical system you want to establish to analyze the problem,but because there are so many samples in the field,the transportation is inconvenient,how to do when the sampling is relatively small Obtaining the strength of the rock is the problem to be solved in this article.The author of this paper uses microscopic parameters to predict the strength of the rock.The work done in this study is as follows:(1)Prepare thin slices of rock samples collected in the field,observe the slices with a microscope and take pictures in the laboratory to obtain slice images,use IPP software to batch process and analyze the microscopic images,and extract the microscopic parameters from the microscopic images.(2)Indoor macro-mechanical test(uniaxial compression test,dry density measurement test,porosity measurement test)to obtain macro mechanical property parameters.(3)Perform univariate regression analysis on the micro-characteristic parameters and macro-mechanical property parameters to obtain a univariate linear regression model between different parameters.The regression relationship between each parameter analyzes the relative relationship between micro-parameters and macro-mechanical property parameters Advantage factor with better relationship.(4)Use mathematical methods(gray correlation and multiple regression)to analyze the data,and use correlation analysis to verify the univariate linear regression model,analyze the dominant factors,perform multiple linear regression on the macro and micro parameters,and fit the formula.
Keywords/Search Tags:metamorphic rock, microscopic parameters, correlation analysis, multiple linear regression
PDF Full Text Request
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