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Study On Surface Deformation Characteristics And Compression Simulation Of Coal Gangue Filling Reclamation Area

Posted on:2019-04-25Degree:MasterType:Thesis
Country:ChinaCandidate:J W LiFull Text:PDF
GTID:2371330545991423Subject:Surveying and Mapping project
Abstract/Summary:PDF Full Text Request
Coal resources play a major role in China's energy resources.On the one hand,The exploitation and utilization of coal has promoted economic and social development.On the other hand,coal mining has caused the collapse of the ground,destroyed the surface and the balance of ecological environment.In order to control coal mining subsidence areas and use coal gangue as the filling matrix for land reclamation and filling mining in subsidence areas,not only can consumed a large amount of coal gangue,reduced environmental pollution,but also the depth of surface subsidence and the surface collapse degree can be reduced.However,coal gangue is greatly affected by external factors and easily deformed,eventually resulting in different degrees of settlement on the surface of the filling area.Therefore,the surface deformation characteristics of coal gangue filling area and its impact on the surface environment are one of the key issues that need to be solved.This paper selects coal gangue with different particle size in Panji mining area as the filling matrix for retanning test,constructs an outdoor test model,collects and analyzes model settlement data,and combines numerical analysis model.To explore the mechanism of deformation and settlement of gangue with different gradations in natural accumulation;(1)The settlement rate of the three test models in the natural storaging coal gangue grading and filling retreading experiment showed two-stage change characteristics.The settlement rate of the model showed elastic change in the early stage of the experiment,and the settlement rate curve showed a "wave type " overall.The settlement rate of the model is rapid during the previous monitoring period,and the settlement rate of the model tends to change smoothly with the passage of time,the rate of sinking at the end of the experiment is the smallest;The settlement value of the three gradation filling models varies during the monitoring period.The early settlement of each test model increased rapidly and showed a linear change.The sedimentation value of the later settlement value was smaller and tended to change steadily.(2)Rainfall and snow have great influence on the settlement rate of the test model.By analyzing data from monitoring after rain or snow,Rainfall and snow increase the load of the model gangue pile,and increase the settlement rate of the test model in a short time and rainfall,snowfall and duration have hysteresis effect on surface deformation of the model.(3)By comparing different numerical analysis models,in the early stage of the settlement of the three experimental models,AR model prediction data has the best small error fitting effect,GM model prediction data fitting effect is poor,and the prediction data fitting curve fluctuates greatly.The main reason is that the amount of measured data is small,and the overall dynamic trend of data cannot be reflected;at the later stage of settlement of the experimental model,the prediction accuracy of each predictive analysis model is improved.(4)In the coal gangue compressive deformation simulation experiment,the stress-strain and axial strain-body strain curves of the gradation models of different schemes show three phases of change.The peak strength of the large particle coal gangue model is smaller than that of the small particle coal gangue model,mainly because some of the fine particles fill the gaps between the coarse particles,and the coarse and fine particles contact and engage with each other,increasing the compressive strength.With the increase of the axial strain,the volume of the single-rank coal gangue model changes greatly,and the volume of the mixed-gradation model model changes less.
Keywords/Search Tags:coal gangue, compression deformation, block size, surface deformation, Prediction model, experiment model
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