Artificial immune pattern recognition for structure damage classification
文献类型：期刊论文
| 作者 | Chen B(陈波); Zang CZ(臧传治) ; Chen B(陈波); Zang CZ(臧传治)
|
| 刊名 | Computers & Structures
 |
| 出版日期 | 2009 |
| 卷号 | 87期号:21-22页码:1394-1407 |
| 关键词 | Structural health monitoring Artificial immune pattern recognition Structure damage classification |
| ISSN号 | 0045-7949 |
| 产权排序 | 2 |
| 中文摘要 | Damage detection in structures is one of the research topics that have received growing interest in research communities. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage classification problem. This paper presents an Artificial Immune Pattern Recognition (AIPR) approach for the damage classification in structures. An AIPR-based structure damage classifier has been developed, which incorporates several novel characteristics of the natural immune system. The structure damage pattern recognition is achieved through mimicking immune recognition mechanisms that possess features such as adaptation, evolution, and immune learning. The damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to evolve its pattern recognition antibodies towards the goal of matching the training data. In addition, the immune learning algorithm can learn and remember different data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control-American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group and a three-story frame provided by Los Alamos National Laboratory. The validation results show that the AIPR-based pattern recognition is suitable for structure damage classification. The presented research establishes a fundamental basis for the application of the AIPR concepts in the structure damage classification. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Interdisciplinary Applications ; Engineering, Civil |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | REMOTE-SENSING IMAGERY ; NEURAL-NETWORKS ; DIAGNOSIS ; MODELS ; SYSTEM |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000271369500013 |
| 公开日期 | 2012-05-29 |
| 源URL | [http://ir.sia.cn/handle/173321/7064]  |
| 专题 | 沈阳自动化研究所_工业信息学研究室 |
| 推荐引用方式 GB/T 7714 | Chen B,Zang CZ,Chen B,et al. Artificial immune pattern recognition for structure damage classification[J]. Computers & Structures,2009,87(21-22):1394-1407. |
| APA | Chen B,Zang CZ,Chen B,&Zang CZ.(2009).Artificial immune pattern recognition for structure damage classification.Computers & Structures,87(21-22),1394-1407. |
| MLA | Chen B,et al."Artificial immune pattern recognition for structure damage classification".Computers & Structures 87.21-22(2009):1394-1407. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。