Goaf-collapse sites stability evaluation based on principal component hierarchical clustering model
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摘要: 对采空塌陷场地进行稳定性评价是保证后续工程建设安全的重要前提。针对某磷矿急倾斜矿层采空塌陷场地稳定性评价影响因素多、地质采矿条件复杂的问题，提出了基于主成分层次聚类模型的稳定性判别方法。在确定采空塌陷场地稳定性评价范围的基础上，筛选了表征采空塌陷场地稳定性的8个主要指标作为学习样本进行训练，经主成分降维后，建立采空塌陷场地稳定性评价的AGNES（AGglomerative NESting）层次聚类模型并应用于该磷矿采空塌陷场地稳定性评价中。实验结果表明，采空塌陷场地稳定性影响因素的前4项主成分的累计贡献率为81.8%，较好地表征原始样本指标所包含的信息，采空塌陷场地稳定性总体能够与此区域城市规划不同用地性质的土地承载力相适应，并与其他手段评价结果相比较证明了主成分层次聚类模型应用于采空塌陷场地稳定性评价中的可行性和有效性。Abstract: Stability evaluation of goaf-collapse sites is the primary problem to be solved in the subsequent engineering construction. In this paper, a principal component hierarchical clustering analysis method for a goaf-collapse site stability evaluation has been proposed to solve the problem caused by multiple influencing factors and complicated geological and mining conditions of a steeply pitching phosphate orebody. On the basis of determining the stability evaluation range of the goaf-collapse site, 8 major indicators representing stability of goaf-collapse sites were selected after principal component analysis (PCA) as learning samples for training. AGNES (AGglomerative NESting) hierarchical clustering analysis model for evaluating stability of goaf-collapse sites was established. After dimensionality reduction, first four principal components of cumulative contribution rate were 81.8%. The results show that the goaf-collapse site can adapt to different land bearing capacity of urban planning in the study area, the discriminant result is consistent with other methods, it indicates that the hierarchical cluster analysis model has a good discriminant ability. The proposed approach demonstrates the feasibility and effectiveness in the field of stability assessment of goaf-collapse sites.
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