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

• 作者: Akira Mita Jin Zhou Rongshuai Li
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 数学
• 关键词: Structural Health Monitoring Adaptive IMMUNE CLONAL SELECTION Algorithm SYMBOLIC Time Series Analysis Real-Valued Negative SELECTION Building Structures
• 年，卷（期）: 2013,（1）
• 页码范围: 48-56
• 页数: 9页
• 分类号: O1

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.