Structural Damage Detection Based on Semi-supervised Fuzzy C-means Clustering

被引：0

作者：

Liu, Zhen ^{[1
]}

Zhou, Qifeng ^{[1
]}

Chi, Qijun ^{[1
]}

Zhang, Yuanyuan ^{[1
]}

Chen, Youling ^{[1
]}

Qi, Sen ^{[1
]}

机构：

[1] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China

来源：

2014 PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2014) | 2014年

关键词：

Damage detection; Semi-supervised; Fuzzy C-means clustering; data fusion;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Structural damage detection is a key part of structural health monitoring. In recent years, intelligent detecting methods are used in this field and show good performance. This paper proposed a structural damage detection method based on data fusion and semi-supervised fuzzy C-means clustering. Compared with other intelligent method, our method can detect the damage location and extent, meanwhile, provide a confidence. Experiment results on a benchmark model show effectiveness of the proposed methods.

引用

页码：551 / 556

页数：6

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