Structural damage detection method based on random forests and data fusion

CORC > 厦门大学 > 信息技术－已发表论文

	Structural damage detection method based on random forests and data fusion
	Zhou, Qifeng ; Ning, Yongpeng ; Zhou, Qingqing ; Luo, Linkai ; Lei, Jiayan ; Zhou QF(周绮凤) ; Luo LK(罗林开) ; Lei JY(雷家艳)
刊名	http://dx.doi.org/10.1177/1475921712464572
	2013
英文摘要	Natural Science Foundation of Fujian Province of China [2011J01373]; Fundamental Research Funds for the Central Universities [2010121065]; A structural damage detection method by integrating data fusion and random forests was proposed. The original acceleration signals were translated into energy features by wavelet packet decomposition. Then the processed energy features were fused into new energy features by data fusion. This can further enlarge the differences among all types of damages. Finally, random forests as an effective classifier was used to detect the multiclass damage. Numerical study on the benchmark model and an eight-storey steel shear frame structure model was carried out to validate the accuracy of the proposed damage detection method. The experiment results indicate that the damage detection method based on random forests and data fusion can improve damage detection accuracy in comparison with random forests alone, support vector machine alone, and support vector machine and data fusion techniques. Moreover, the proposed method has significantly better stability than several other methods.
语种	英语
出版者	SAGE PUBLICATIONS LTD
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92510]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Zhou, Qifeng,Ning, Yongpeng,Zhou, Qingqing,et al. Structural damage detection method based on random forests and data fusion[J]. http://dx.doi.org/10.1177/1475921712464572,2013.
APA	Zhou, Qifeng.,Ning, Yongpeng.,Zhou, Qingqing.,Luo, Linkai.,Lei, Jiayan.,...&雷家艳.(2013).Structural damage detection method based on random forests and data fusion.http://dx.doi.org/10.1177/1475921712464572.
MLA	Zhou, Qifeng,et al."Structural damage detection method based on random forests and data fusion".http://dx.doi.org/10.1177/1475921712464572 (2013).