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Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis
Department of System Design Engineering, Keio University, Yokohama, Japan.
Department of System Design Engineering, Keio University, Yokohama, Japan.
Department of System Design Engineering, Keio University, Yokohama, Japan.
- 1 Department of System Design Engineering, Keio University, Yokohama, Japan.
- 2 Department of System Design Engineering, Keio University, Yokohama, Japan.
- 3 Department of System Design Engineering, Keio University, Yokohama, Japan.
Journal of Intelligent Learning Systems and Applications·Volume 05 (2013)·Pages 48–56·Published 22 February 2013·DOI10.4236/jilsa.2013.51006
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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 quan tify damage to civil engineering structures in the presence of measurement noise.
KeywordsStructural Health MonitoringAdaptive Immune Clonal Selection AlgorithmSymbolic Time Series AnalysisReal-Valued Negative SelectionBuilding Structures
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