An evaluation of Mahalanobis Distance and grey relational analysis for crack pattern in concrete structures
An evaluation of Mahalanobis Distance and grey relational analysis for crack pattern in concrete structures
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DOI：
10.1016/j.commatsci.2012.07.002
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发表时间：
2012-12
影响因子：
3.3
通讯作者：
Wei-Cheng Lai-;Ta-Peng Chang;Jin-Jun Wang;Chia-Wei Kan;Wei-Wen Chen
中科院分区：
文献类型：
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作者：
Wei-Cheng Lai-;Ta-Peng Chang;Jin-Jun Wang;Chia-Wei Kan;Wei-Wen Chen
马氏距离（MD）和灰色关联度（GRG）是分析多变量模式的有效方法。本文研究了分子动力学和广义相对论在混凝土结构裂缝模式识别中的应用。在小数据量的情况下，MD分析中使用的样本组协方差矩阵是奇异的。本文利用合并协方差矩阵作为样本组协方差矩阵的一种替代估计来解决这类问题。结果表明，MD和GRG能够对时域数据集之间的差异进行分类，从而识别混凝土结构中出现的裂缝类型。最后介绍了学习矢量量化（LVQ）人工神经网络，并与MD和GRG进行了比较。
Mahalanobis Distance (MD) and grey relational grade (GRG) are useful methods for analyzing patterns in multivariate cases. Developed in this paper is the application of MD and GRG for crack pattern recognition in concrete structure. In case of small data sizes, the sample group covariance matrices used in MD analysis are singular. This paper uses the pooled covariance matrix as an alternative estimate for the sample group covariance matrix to solve this kind problem. The results show that MD and GRG are capable of classifying the distinction among the data sets in time domain and thus identify the type of crack developed in concrete structure. Finally, learning vector quantization (LVQ) artificial neural network is introduced and used to be compared with MD and GRG.