Mining Goaf-induced Rock Movement Characteristics And Effects Of Overburden Caving

The goaf is one of the most important forms of disasters induced by mining engineering. As mining progresses, when the exposed area of cavity increases to a certain extent, it will induce a catastrophic accident. In this paper, the horizontal projec "goaf Monitoring and the roof of goaf and falling law in Longqiao iron mine" as the research background around mining-induced rock movement and goaf falling effect cover the themes of work, combined with indoor test, numerical simulation, theoretical analysis, field monitoring and survey methods, the iron ore mining Longqiao effect produced by mining and key block feature parameter were revealed.Shortcomings existing of methods to get rock mechanics parameters was analyzed. The rock with a robust regression method to determine the shear strength model was given, and the various methods of rock mass parameters is reviewed and studied their in metal mining on application. Aiming at solve problems in determining the shear strength of rock in the graphic method and Least Squares (LS):(1) It only applies to a higher degree of experimental data related to the situation but not to discrete data and the existence of outliers; (2) When the test data obtained by the experimentation method are analyzed, the effect of anomalous values in test data increase markedly because the quadratic sum of residual error is adopted in calculation by the least square method. In view of this, a rock of stability of shear strength parameters (Robust) regression analysis is proposed. This method is related to the experimental data in the poor, dispersion and the existence of outlier cases, and can be weakened the value of discrete and abnormal positioning of the ability to improve the robustness and reliability of estimated parameters. In order to reduce the effect of anomalous values in test data, the sum of residual absolute value is used to replace the quadratic sum of residual error in the robust regress method, then the quadratic of anomalous value could be avoided, and the effect of anomalous value could be reduced availably. It shows that the results of the two methods are same when the relativity of the test data is good, but the results of the robust regress method are better than the least square method when there are have the anomalous values in test data. In addition, to facilitate and improve the numerical precision, Longqiao iron stress was measured.Based on the pre-site investigation, site of the original rock stress measurement, indoor rock mechanical parameters of the mechanical testing and handling of works, goaf forming process and the stability of simulation and prediction analysis of the goaf surrounding the principal stress, normal stress, shear stress and deformation were analyzed using FLAC3D. The law of rock deformation and instability iron was initially obtained.Simulation results of goaf stability show that the formation of goaf, the goaf around the corner will be the first to reach the ultimate state of shear failure. As the mined area increases, the destruction of the corner region of extended gradually expanded roof became clear the central tensile stress, tensile stress may eventually become damaged. According to the conclusions, follow-up exploration of the mine design and management should pay attention to the problems and proposed a reasonable design of the proposed mining.Traditional finite element method is difficult to analyze large rock deformation and block cross-off situation, according to key block theory, limit equilibrium analysis and reliability theory, across key block down the possibility was researched. In the conventional theory of first order second moment reliability analysis methods and second order second moment method, the need for the basic random variables are normal state of transformation or the equivalent, must be known to the probability distribution of random variable, sometimes too much trouble, and this kind of transformation is also the leading non-linear performance function to be a source of error. To eliminate these errors, we introduce an asymptotic integration method and the second time asymptotic integration method, a good solution to the problem.The establishment of the goaf on the characteristics of rock block analysis, the method combines block theory, reliability theory and vector analysis can determine the key block size, moving direction, sliding mbde, the reliability and effectiveness of block probability and so on.Through proposed model, based on the model of thinking and mathematical formulas to calculate the preparation of Matlab program, the results showed that in 15 mining areas, movable block with more than 270, the movable block in the critical safety factor is less than 2 Block 48, in which the key factor of safety less than 1.5,33 bulk, accounting for 68.73%. And the characteristic parameters of each block, the probability of reliability and reliability were given.Iron ore mining under the 2009 Longqiao Goaf after the actual spatial distribution, combined with the overall stability of the analysis of geometric modeling request, and iron and technical personnel Longqiao discussions, analysis, in determining the present study ore mining conditions corresponding relationship between time and space, the mining of the mineral appear to rule throughout the study, mainly from the maximum principal stress, minimum principal stress, the resulting vertical displacement and surface deformation, and several other fronts.The Detector will be in Italy a new application of ground penetrating radar equipment to the iron ore mined area Longqiao cover thickness detection, and discusses the principles and specific methods of detection. The equipments for the IDS TR40MHz antenna at the antenna, the maximum detection depth reach up to 50 m. Detected results show that the mine Air District cushion thickness is greater than 25 m, to meet national safety rules of order the natural caving pillar-free space zone cushion thickness must be more than two segmentation height.This article discusses the three types of GM (1,1) model and least squares support vector machines approach:parallel, series and residual-type gray squares support vector machine model, and iron by Longqiao examples show that such a new information processing and forecasting method is feasible and effective. Research shows falling rock and overburden thickness of ballast stone Grey forecasting model of least squares support vector machine modeling ideas and crucial step, and apply this model to cover caving height and thickness of ballast stone to predict the results show that,3 gray squares support vector machines is better than a single prediction GM (1,1) and pure LSSVM, the indexes had been lower, reflecting the advantages of multi-information fusion. Residual-based model is superior to parallel and series models, which cover with LSSVM to describe the thickness of rock and stone ballast sequence of random item are correct and reliable. In the series-type model, the combination is better than a single prediction GM (1,1) model prediction, in the practical application of GM (1,1) model forecasts can not know in advance which model the best, so use the model blindness can be reduced forecast, there are some practical value. Fully shows GLSSVM this new method of information processing and forecasting superiority can be extended in engineering practice.