Mining subsidence and displacement prediction using influence function methods

Ren, Gang; Reddish, D.J.; Whittaker, B.N.

doi:10.1016/s0167-9031(87)90966-2

Cited by 46 publications

(26 citation statements)

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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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“…The stability of PKS blocks can then be assessed by rock samples from the 702 borehole and Eq. (8). Δ can be approximated to be 9.5 m because the maximum surface subsidence is 9.5 m. Field measurements indicate that the PKS breaking interval of the adjacent LW panel is 15.2 m and, therefore, it is assumed that the PKS breaking step of the present panel is 15.2 m as well.…”

Section: Mechanism Of Surface Stepped Subsidencementioning

confidence: 99%

“…To minimise adverse impacts of subsidence, many research papers and reports have been published pertaining to subsidence problems for both operational and abandoned coal mines. Among these endeavours mainly related to mining subsidence mechanisms, 1-4 subsidence prediction methods, [4][5][6][7][8][9][10][11][12] ground-movement investigations, [13][14][15][16] and subsidence control measures. 5,[17][18][19] Most of these studies are restricted to continuous or trough subsidence profiles, which are very common forms of subsidence.…”

Section: Introductionmentioning

confidence: 99%

Surface stepped subsidence related to top-coal caving longwall mining of extremely thick coal seam under shallow cover

2015

International Journal of Rock Mechanics and Mining Sciences

153

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“…Here, we adopt the empirical kernel, trough at the surface, as expected from physical considerations. Further detail on several alternative kernels is described in Ren et al [1987].…”

Section: Inversion Methodologymentioning

confidence: 99%

Measuring UCG cavity development with InSAR

White¹,

Mellors²

2012

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Mining subsidence and displacement prediction using influence function methods

Cited by 46 publications

References 1 publication

Influence of model parameter uncerainties on forecasted subsidence

Influence of model parameter uncerainties on forecasted subsidence

Surface stepped subsidence related to top-coal caving longwall mining of extremely thick coal seam under shallow cover

Measuring UCG cavity development with InSAR

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