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针对采动区建筑物损害程度的影响因素较多且各因素呈现非线性、多重共线性等特点,提出了基于核主元分析(KPCA)和模糊聚类方法相结合的建筑物损害评价新方法。采用核主元分析方法,借助核函数在高维特征空间中对数据集进行降维,获取相互独立的非线性主元,然后利用模糊ISODATA算法进行聚类分析实现建筑物损害程度的分类评价。实例分析结果表明,所提方法体现了建筑物损害分类评价的模糊性,兼顾了各种因素的不同影响,同时避免了因素之间的相互干扰,其评价结果与实际符合较好,具有较高的实用价值。
In view of the fact that there are many influencing factors of building damage in mining area and the factors are nonlinear and multicollinearity, a new method of building damage assessment based on kernel principal component analysis (KPCA) and fuzzy clustering is proposed . The kernel principal component analysis (PCA) was used to reduce the dimensionality of datasets in high-dimensional feature space by kernel function to obtain independent principal components of nonlinearity. Then the fuzzy ISODATA algorithm was used to carry out cluster analysis to classify and evaluate the degree of building damage. The case study shows that the proposed method embodies the ambiguity of the classification of damage classification of buildings, takes into account the different influences of various factors and avoids the mutual interference among factors. The evaluation results are in good agreement with the actual ones and have higher The practical value.