Surface subsidence prediction of longwall underground coal mining prototype by using numerical and empirical method

Underground coal mining method is an alternative mining method that can be used to fulfill the increasing demand of coal. One of underground coal mining methods which are widely applied in the world is longwall method. This method is categorized as caving method. Mined out panel area is allowed to fall. This area is known as go out and fall (goaf) area. By allowing the goaf area to fall, stress load at mining front and power roof support is reduced so that mining activity can be carried out safely. Hence, surface subsidence would potentially occur at the surface area. As consequences, surface subsidence analysis including the likelihoods of subsidence profile, maximum value of subsidence and area of surface subsidence is needed. The previous research reveals that the maximum value of surface subsidence calculated using empirical formulae of the UK National Coal Board is different to that of numerical analysis. Since the surface subsidence prediction is very important to minimize the effect of longwall underground coal mining to the environment in the surface area, prediction using another method needs to be carried out to confirm the differences occur between these two methods. Physical model can be used to provide an alternative surface subsidence prediction. The result of physical model's surface subsidence prediction can then predict the surface subsidence of prototype by using dimensional analysis. To confirm the result from the previous research, surface subsidence analysis using numerical method, which are distinct element and finite-difference method, and UK National Coal Board empirical method was carried out for the prototype that is represented by the physical model. Prototype for panel extraction of 145 meter width, 465 meter length and 11.5 meter height of excavation at 40 meter depth, based on the results of the analysis using physical models will have 11.2 meter surface subsidence. The prediction from physical model is in good agreement with the numerical ones.