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Study On Corrosive Mechanism And Zoning Evaluation Of Steel Under Soil Environment In Shanxi Province

Posted on:2021-11-23Degree:MasterType:Thesis
Country:ChinaCandidate:Z Y GuoFull Text:PDF
GTID:2481306110998249Subject:Architecture and Civil Engineering
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With the rapid development of national economy and construction,the underground space structure needs to be developed continuously.The servic e durability of underground facilities has become a hot topic for scholars and engineers at home and abroad.The water content,salt,acid and alkali in soil are main indicators that affect the durability of underground structures and facilit ies.This paper obtained the natural environment,soil environment,hydrogeologic a l environment background and relevant data of Shanxi Province and the indoor corrosion behavior test data of two kinds of steel materials in different pollut ed soil.Based on this,the significance indicators are selected by Random Forest algorithm,and the multi factor significance indicator analysis model is establis hed.It provides important reference for steel corrosion evaluation and protection.The main conclusions were obtained as follows:(1)Through studying the effect of various indicators of different polluted soil on the corrosion rate of different steel grades,we can obtain following results by analyzing the significance scores.The significant indicators of the effect of different polluted soil on the corrosiveness of steel are different.Water content,p H value,saturation and porosity are the significant indicators of the corrosive effect of X70 steel without distinguishing the types of pollutants,while redox potential is the common significant indicator in other polluted soils.Water content,p H value and porosity are the significant indexes of Q235 Steel's corrosivity without distinguishing the types of pollutants,while redox potential is the common signific ant indicator in other polluted soils.The significant indicators of corrosion rate of X70 steel and Q235 steel in the same polluted soil are different.In the case of no distinction of pollutant types,the common significant indicators of X70 steel and Q235 steel are water content,p H,saturation and porosity,while the different significant indicator of X70 steel include saturation indicator.In the soil of each pollutant,the common signific ant indicator is redox potential indicator,and other significant indicators are different with different pollutants in soil.(2)Through the statistical analysis of soil environment data in Shanxi Province,we can receive the following results.The p H value of soil in Shanxi Province ranges from 7.00 to 9.30,and its dispersion degree is small.The soil in most areas is weakly alkaline,while in a few other areas is weakly acidic and alkaline.The average content of Mg2+,Cl-and SO42-in the soil of Shanxi Provinc e range from 37.00 to 160 mg/kg.Obviously,we can observe that the distributio n of ion content is in a small value area and the corrosiveness of soil environment to steel is weak in Shanxi Province.The soluble salt content of soil in Shanxi Province ranges from 110.00 to 4800.00 mg/kg,with a relatively large degree of dispersion.The total salt content data is relatively scattered,and the salt content in different regions is quite different.(3)Based on the study of soil environmental data in Shanxi Province and the analysis of the correlation of soil salt,this paper obtained influence ions of main salts in soils of urban areas in Shanxi Province and the distribution map of the main ions in the soil of each urban area of Shanxi Province.The main ions of soil in Taiyuan,Xinzhou and Linfen are Mg2+,Cl-and SO42-.In Datong City,Yuncheng City and Jincheng City,the main ion is SO42.The main ions in other areas of Shanxi Province are Cl-and SO42-.(4)Through the establishment of multiple linear regression model based on the indoor simulation test data of X70 steel and Q235 steel,this paper analyzed the distribution map of main soil ions in each urban area of Shanxi Province,and formed the regression model of soil environment corrosion rate in Shanxi Province.There are three types of soil environmental corrosion models in Shanxi Province included the corrosion rate model of Cl-+SO42-+Mg2+composite ionic soil in Taiyuan,Xinzhou and Linfen City;the corrosion rate model of SO42-+Na+/K+composite ionic soil in Datong City,Yuncheng City and Jincheng City;corrosion rate model of Cl-+SO42-+Na+/K+composite ionic soil in Shuozhou,Luliang,Yangquan,Jinzhong and Changzhi City.(5)Based on the study of the corrosion test of steel in soil and the analys is of the soil environment zoning in Shanxi Province,the corrosion mechanis m of the soil to steel in each zoning of Shanxi Province was obtained.The corrosive anions in three kinds of soil environment in Shanxi Province are Cl-and SO42-,which will destroy the protective film produced by corrosion during the process of corrosion reaction,and continuously catalyze the corrosion reaction to form pitting and pitting corrosion on the surface of steel sheet.Due to the action of Cl-and SO42-in the soil,the corrosion rate will increase in the later stage of corrosion to form finally stable corrosion products Fe2O3 and Fe3O4.On the basis of the simulation test,theoretical analys is and collection data of soil environment in Shanxi Province,this paper selected the signific a nc e indicators of soil corrosiveness to steel by using Random Forest method.This method own the advantage that it can solve the problem of high-order interac tio n of independent variables And the corrosiveness evaluation of soil environment to steel in Shanxi Province was analyzed,and the multiple linear regression model of corrosion rate was constructed in this paper.Therefore,the results obtained in this paper can provide important theoretical and application basis for the corrosion protection and evaluation of soil environment,and have the great signific anc e for social development and economic construction.
Keywords/Search Tags:Soil Environment of Shanxi Province, Steel Corrosion, Significance Index, Correlation Analysis, Multiple Linear Regression Model
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