A predictive methodology for multi-seam mining induced subsidence

Ghabraie, Behrooz; Ren, Gang; Barbato, James; Smith, John Grieve

doi:10.1016/j.ijrmms.2017.02.003

Cited by 52 publications

(24 citation statements)

References 12 publications

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“…The second group includes the profile function (Asadi et al, 2004;Díez and Álvarez, 2000;Torano et al, 2000;Waddington and Kay, 1995), graphical method (NCB, 1975), application of artificial neural networks (Ambrožič and Turk, 2003;Gruszczyński, 2007;Weifeng and Xiaohong, 2013) and influence function (Álvarez-Fernandez et al, 2005;Cui et al, 2013Cui et al, , 2000Ghabraie et al, 2017;Huayan et al, 2002;Liao, 1993;Nicieza et al, 2005;Ren et al, 1987Ren et al, , 2014Sheorey et al, 2000;Singh and Singh, 1998). Based on large collections of observations, the relationships between measurement results and geometric conditions from a mining operation are determined.…”

Section: Knothe's Modelmentioning

confidence: 99%

Influence of model parameter uncerainties on forecasted subsidence

Gruszczyński¹

2018

Acta Geodynamica Et Geomaterialia

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Surface deformation due to underground exploitation affects the safety of overlying structures. Forecasting can predict risks to surface structures and facilitates actions designed to improve their resilience and reduce the potential impact of mining activities. However, forecasting accuracy is limited. Therefore, in practice, model parameters are determined within a certain margin to ensure that critical values of deformation indicators for surface objects are not exceeded. For economic reasons, it is important to minimize these margins while also ensuring that safety is maintained. One important factor influencing forecasting accuracy is the uncertainty in deformation model parameters used for calculations. Therefore, it is critical to adopt an appropriate methodology for determining and addressing the uncertainties in deformation model parameters used in forecasting. This study presents methods for estimating the Knothe's model parameters needed to forecast surface deformation caused by underground mining and defining the uncertainties in those forecasts. Depending on the parameter uncertainties, one of two methods for propagation is proposed: the Monte Carlo method or the law of propagation of uncertainty. Using this approach, it is possible to account for uncertainty and reduce forecast margins. A case study of hard coal mining in the Upper Silesian Coal Basin region of Poland is presented. ARTICLE INFO

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Section: Knothe's Modelmentioning

confidence: 99%

Influence of model parameter uncerainties on forecasted subsidence

Gruszczyński¹

2018

Acta Geodynamica Et Geomaterialia

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“…Approaches with most practical value for subsidence prediction can be generalized to profile functions [13][14][15][16], influence functions [17][18][19][20], stochastic models [21,22], and numerical models [23][24][25]. In recent years, various modern approaches to the treatment of surface subsidence have been introduced, among which the cellular automata (CA) [26] and smoothing spline [27] theories should be mentioned.…”

Section: Introductionmentioning

confidence: 99%

Surface Subsidence Prognosis above an Underground Longwall Excavation and Based on 3D Point Cloud Analysis

Pal

Rošer²,

Vulić

2020

Minerals

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Impacts of underground mining have been reduced by continuous environmental endeavors, scientific, and engineering research activities, whose main object is the behavior and control of the undermined rock mass and the subsequent surface subsidence. In the presented Velenje case of underground sublevel longwall mining where coal is being exploited both horizontal and vertical, backfilling processes and accompanying fracturing in the coal layer, and rock mass are causing uncontrolled subsidence of the surface above. 3D point clouds of the study were acquired in ten epochs and at excavation heights on the front were measured at the same epochs. By establishing a sectors layout in the observational area, smaller point clouds were obtained, to which planes were fitted and centroids of these planes then calculated. Centroid heights were analyzed with the FNSE model to estimate the time of consolidation and modified according to excavation parameters to determine total subsidence after a certain period. Proposed prognosis approaches for estimating consolidation of active subsidence and long term surface environmental protection measures have been proposed and presented. The C2C analysis of distances between acquired 3D point clouds was used for identification of surface subsidence, reclamation areas and sink holes, and for validation of feasibility and effectiveness of the proposed prognosis.

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“…Ghabraie et al [21] predicted the subsidence of two-seam mining by the 3D finite element analysis method. Ghabraie et al [26] studied the differences between the single-seam and multiseam subsidence parameters and proposed an improved influence function to predict the subsidence of multiseam mining. Ghabraie et al [25] used a gravel physical multiseam model to simulate the effect of different longwall mining methods on surface subsidence.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Method for Subsidence Prediction on Two-Seam Longwall Mining

Zhang

et al. 2019

Energies

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The purpose of mining subsidence prediction is to establish a reliable assessment for surface subsidence resulting from underground mining. In this study, a new method for predicting subsidence in two-seam mining is proposed. First, the surface subsidence due to mining the upper seam is monitored. Then, taking the subsidence data as indicators, the optimal mechanical parameters of overlying strata can be obtained by orthogonal experimental design and inverse analysis of numerical simulation. Finally, further subsidence is calculated and predicted by the numerical model. A case of two-seam underground mining is studied using this methodology. This coal mine is located in the Dongsheng coal field in Inner Mongolia, China. Based on GPS surface subsidence monitoring and parameter inversion, the subsidence induced by two-seam mining is estimated and predicted. This study shows that the ratio of the height of overlying strata to mining thickness (H/M), mining configuration and adjacent mining have a significant effect on the surface subsidence caused by two-seam mining. By parameter inversion, the proposed optimal parameters can be applied to predict the subsidence of a nearby mine with similar stratigraphic conditions. Furthermore, this methodology can also be used to predict the subsidence caused by mining of more than two seams.

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A predictive methodology for multi-seam mining induced subsidence

Cited by 52 publications

References 12 publications

Influence of model parameter uncerainties on forecasted subsidence

Influence of model parameter uncerainties on forecasted subsidence

Surface Subsidence Prognosis above an Underground Longwall Excavation and Based on 3D Point Cloud Analysis

A Comprehensive Method for Subsidence Prediction on Two-Seam Longwall Mining

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