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Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

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DOI: 10.4236/jilsa.2013.51006    3,760 Downloads   5,676 Views   Citations

ABSTRACT

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

R. Li, A. Mita and J. Zhou, "Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 1, 2013, pp. 48-56. doi: 10.4236/jilsa.2013.51006.

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