现代地质Issue(2)：461-465,5.
基于T-S模糊神经网络的采空塌陷危险性判别
Evaluation of Underground Goaf Stability Based on T-S Fuzzy Neural Network Model
摘要
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 fea-tures 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 under-ground 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 quaterna-ry 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 con-structed.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 co-incided 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.关键词
采空区/地面塌陷/评价/T-S模糊神经网络模型Key words
underground goaf/ground collapse/evaluation/T-S fuzzy neural network model分类
天文与地球科学引用本文复制引用
张连杰,武雄,谢永,吴晨亮..基于T-S模糊神经网络的采空塌陷危险性判别[J].现代地质,2015,(2):461-465,5.基金项目
国家自然科学基金项目（41172289）；国家科技支撑计划课题（2012BAJ11B04）。 （）
