Evaluation of reliability of subsidence prediction based on spatial statistical analysis

Hejmanowski, Ryszard; Malinowska, Agnieszka

doi:10.1016/j.ijrmms.2008.07.012

Cited by 30 publications

(19 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These assumptions can result in large discrepancies between the forecasts and true deformation. Therefore, it is critical to adopt an appropriate methodology for determining and addressing the uncertainty in deformation model parameters used in forecasting (Hejmanowski and Malinowska, 2009). This study focuses on determining the uncertainties in Knothe's deformation model parameters, propagating these uncertainties, and evaluating their effects on forecast results.…”

mentioning

confidence: 99%

Influence of model parameter uncerainties on forecasted subsidence

Gruszczyński¹

2018

Acta Geodynamica Et Geomaterialia

View full text Add to dashboard Cite

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

show abstract

mentioning

confidence: 99%

Influence of model parameter uncerainties on forecasted subsidence

Gruszczyński¹

2018

Acta Geodynamica Et Geomaterialia

View full text Add to dashboard Cite

show abstract

“…Specialists dealing with the protection of mining areas have recently discussed the issue of reliability and higher accuracy of deformation occurrence predictions (Kowalski 2005;Niedojadło 2008;Hejmanowski and Malinowska 2009). Such considerations stem from the increasing financial and social costs of damage done by this type of mining activity.…”

Section: Introductionmentioning

confidence: 99%

Modeling of time dependent subsidence for coal and ore deposits

Hejmanowski

2015

Int J Coal Sci Technol

Self Cite

View full text Add to dashboard Cite

Coal and ore underground mining generates subsidence and deformation of the land surface. Those deformations may cause damage to buildings and infrastructures. The environmental impact of subsidence will not be accepted in the future by the society in many countries. Especially there, where the mining regions are densely urbanized, the acceptance of the ground deformations decreases every year. The only solution is to limit the subsidence or its impact on the infrastructure. The first is not rentable for the mining industry, the second depends on the precise subsidence prediction and good preventing management involved in the mining areas. The precision of the subsidence prediction depends strictly on the mathematical model of the deformation phenomenon and on the uncertainty of the input data. The subsidence prediction in the geological conditions of the raw materials used to be made on the basis of numerical modeling or the stochastic models. A modified solution of the stochastic model by Knothe will be presented in the paper. The author focuses on the precise description of the deposit shape and on the time dependent displacements of the rock mass. A two parameters' time function has been introduced in the algorithm.

show abstract

“…As a consequence, it is crucial to correctly assess the mining risk and the potential impact this might have on building objects. The main problem related with building damage risk in mining areas is the complexity of factors and difficulty in actual evaluation of potential damage that can be brought about to the objects [1][2][3][4][5][6]. Another problem lies in the mass character of the effect, especially in view of the fact that some methods enable only individual evaluation of building damage risk.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy inference-based approach for building damage risk assessment on mining terrains

Malinowska

2011

Engineering Structures

View full text Add to dashboard Cite

Evaluation of reliability of subsidence prediction based on spatial statistical analysis

Cited by 30 publications

References 3 publications

Influence of model parameter uncerainties on forecasted subsidence

Influence of model parameter uncerainties on forecasted subsidence

Modeling of time dependent subsidence for coal and ore deposits

A fuzzy inference-based approach for building damage risk assessment on mining terrains

Contact Info

Product

Resources

About