Abstract

Underground mining often causes negative effects such as subsidence on the mine surface. The impact of underground extraction activity is even more dangerous for the mine under the residential area. Surface subsidence from underground mining activity is a problem in most countries. With the purpose of reducing the harm and warning of underground mining impacts to surface, many subsidence forecasting methods have been developed. In this paper the authors introduce the theory of exponential function and its application in forecasting the surface deformation due to underground mining in Thong Nhat coal mine. Exponential function parameters are evaluated for 4 stations showing maximum RMSE of 62.5 mm. The maximum difference between predicted subsidence using these parameters and measured value is -84.5 mm, equivalently to 5.3%.

1. Introduction

Consequences of underground is creating workings in the ground, rock lost equilibrium and tends to move to obtain new equilibrium, the movement spreading to the mine surface causes the formation of surface subsidence trough. The prediction of the consequences of mining is an important task for the mine surveyors. The prediction of surface subsidence enables to efficiently repair the mining damage and has a positive impact on the economic results of mining.

Subsidence causes damage in different objects on the surface. Thus, the preliminary aim of mine surveyors is estimating the impact of underground mining on surface above mines. They started to measure the subsidence of points on the mine surface, in order to be able to control the subsidence process and to reduce the damages caused by the underground excavation activity.

Several prediction methods have been developed such as empirical prediction methods, model prediction methods and prediction methods based on influential functions (T. Ambrožič & Turk, 2003). Each method has its own advantages and disadvantages and conditions for individual applications. However, empirical prediction methods have high reliability because of building from the surveying data.

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