基于尺寸效应的采空区危险度RS-TOPSIS法辨识

为准确预测地下采空区危险性,选用采空区结构的跨度、暴露面积、高度、埋深、矿柱尺寸布置等5个采空区危险度结构尺寸影响因素作为评价指标,建立采空区危险度粗糙集-逼近理想解排序法(RS-TOPSIS)综合评价体系。基于粗糙集理论(RS)中的粗糙依赖度,通过计算评价指标与评价等级间的粗糙依赖度得到指标权重。以40个采空区探测系统(CMS)实测采空区作为评价对象,根据单指标分类区间下限构造5个不同等级的典型采空区,结合逼近理想解的排序法(TOPSIS),实现采空区危险度5级贴近度的分类,并辨识实测采空区危险度。研究结果表明,用为采空区群矿山建立的采空区危险度基于结构尺寸效应的RS-TOPSIS法,能够实现危险度5级分类辨识,辨识结果与采空区危险度数值分析结果吻合度为92.5%。

Hazard Degree Identification of Mine Goafs by RS-TOPSIS Method Based on Scale Effect of Structure

In order to accurately predict the risk of underground goaf,six structure size factors,i.e.span of mine goaf,exposed area,height,buried depth,and pillar size,were selected as the evaluation indicators,and a RS-TOPSIS comprehensive evaluation system of the hazard degree of mine goafs was set up.On the basis of rough dependence degree in RS theory,the weights of evaluation indicators were confirmed by calculating rough of evaluation results on evaluation indicators.40 measured mine goafs by CMS were taken as evaluation objects,and five typical mine goafs of different grades were created according to the lower interval limits of single index evaluation.Then,combined with the TOPSIS,five-category classification of hazard degree was realized based on closeness degree and the hazard degree of measured mine goafs were identified.The results show that the structure scale effect-based RS-TOPSIS evaluation system can realize five-category identification of mine goafs.And the results agree well with that obtained by numerical simulation method and the coincidence rate is 92.5%.