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

Rongshuai Li, Akira Mita, Jin Zhou

Department of System Design Engineering, Keio University, Yokohama, Japan..

DOI: 10.4236/jilsa.2013.51006                

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.

Keywords

Structural Health Monitoring; Adaptive Immune Clonal Selection Algorithm; Symbolic Time Series Analysis; Real-Valued Negative Selection; Building Structures

Conflicts of Interest

The authors declare no conflicts of interest.

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