Abstract:

The stability of underground goaf is affected by many factors, especially the conditions of mining and geology. These factors always have different influences, and some of them are interconnected. The above features bring great difficulty to evaluate the ground collapse risk quantitatively. In order to appropriately evaluate the stability of underground goaf, the T-S fuzzy neural network model was introduced in this paper. According to the ground collapse information of Xishan mining area of Beijing, eight factors influencing the stability of underground goaf were selected as the evaluation indexes at first, and then the grading standards were also built up. These factors include the complexity of geological structure, the type of overburden layer, thickness of quaternary cover, the strength of overlying strata, the dip angle of coal seam, the ratio of mining depth and thickness, the depth of underground goaf and the number of underground goaf in space. Based on the training samples which were generated by means of linear interpolation algorithm, the T-S fuzzy neural network model was constructed. Finally eight new samples of Xishan mining area in Beijing were evaluated by the trained T-S fuzzy neural network model. The results were Ⅰ,Ⅱ,Ⅲ,Ⅱ,Ⅲ,Ⅱ,Ⅲ and Ⅱ, respectively. The results coincided with the actual situation. The study shows that it is feasible to evaluate the stability of underground goaf by using the T-S fuzzy neural network model.

Key words: underground goaf, ground collapse, evaluation, T-S fuzzy neural network model