文章基本信息

标题：Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis
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作者：Rongshuai Li ; Akira Mita ; Jin Zhou 等
期刊名称：Journal of Intelligent Learning Systems and Applications
印刷版ISSN：2150-8402
电子版ISSN：2150-8410
出版年度：2013
卷号：5
期号：1
页码：48-56
DOI：10.4236/jilsa.2013.51006
出版社：Scientific Research Publishing
摘要：This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.
关键词：Structural Health Monitoring; Adaptive Immune Clonal Selection Algorithm; Symbolic Time Series Analysis; Real-Valued Negative Selection; Building Structures