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Comprehensive Evaluation Of Safety Performance Of RC Bent Column In An Experimental Plant In Kunming Area During Different Service Periods

Posted on:2022-08-26Degree:MasterType:Thesis
Country:ChinaCandidate:H H XiongFull Text:PDF
GTID:2492306524455774Subject:Architecture and Engineering
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Reinforced concrete structure(RC structure)is currently the most widely used structural form.RC structures that have been exposed to service in the natural environment for a long time are generally affected by environmental erosion and have carbonization and steel corrosion,which lead to the gradual degradation of the mechanical properties of the structure and increase the risk of premature failure.However,there are few existing researches that consider the impact of environmental factors,and at the same time,comprehensively load-resistant and seismically perform research on the safety performance evaluation of important RC components.Therefore,it is extremely urgent and realistic to carry out such work in actual projects.In view of this,this paper takes the RC bent column of an experimental building in Kunming area as a research case to study the load-bearing capacity and seismic performance degradation law of the bent column in different service periods under the influence of environmental factors,and comprehensively evaluate its safety performance.Finally,this paper develops a non-destructive testing system KUSYPJZ-T1 that can quickly evaluate its safety performance based on the width of the concrete rust expansion cracks of the bent column protective layer and combined with the neural network.(1)In order to establish an accurate ABAQUS finite element model to analyze the degradation law of the mechanical properties of bent columns,the constitutive relationship between steel and concrete was studied.Based on the Mander model,the constitutive structure of confined concrete after rusting of stirrups was corrected,and the model was verified using literature test data.The ABAQUS plastic damage model of the concrete in the core area is established based on the Najar theory.The double-line model of the steel bar considering the bond-slip effect is adopted,and the influence of the steel bar corrosion on the slip effect is considered.(2)In order to evaluate the performance laws of bent columns in different service periods,the influence of harmful substances in the atmosphere on the deterioration process of bent columns was considered according to the actual service environment of the plant,and the degradation progress rate and the initial corrosion moment of internal steel bars were analyzed;at the same time;According to the deterioration process,multiple groups of damage conditions including different service periods are designed,and they are loaded monotonously and repeatedly using ABAQUS.(3)In order to quantify the safety performance of RC bent columns in different periods of service,the load-bearing safety factor is defined to characterize the change law of the load-bearing performance of bent columns.The energy consumption and deformation parameters are used to study the seismic damage of bent columns in different service periods,Comprehensive load-bearing and seismic capabilities have evaluated the safety performance of bent columns in different service periods and over-limit service periods,and proposed reinforcement recommendations for bent columns in over-limit service.(4)In order to achieve rapid evaluation and non-destructive testing of the safety performance of bent columns in actual projects,the relevant research on the calculation model of the width of rust expansion cracks and the corrosion rate of steel bars is summarized,and the safety performance evaluation method of bent columns based on the crack width is proposed.In this paper,based on the ABAQUS analysis result data under different working conditions,combined with the BP neural network model,a software system for quickly evaluating the safety performance of bent columns is developed.
Keywords/Search Tags:RC bent column, steel corrosion, bearing performance, seismic performance, safety performance, BP neural network
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