Abnormal state detection of building structures based on symbolic time series analysis and negative selection

Rongshuai Li, Akira Mita, Jin Zhou

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

A symbolization-based negative selection (SNS) algorithm that combines the advantages of symbolic time series analysis (STSA) and negative selection (NS) is proposed for detecting the abnormal states of building structures. In SNS, no prior knowledge of a structure's abnormal state is needed. Only the response of the structure in a current state is used as input data. In addition, this approach can work even with one sensor, so it is highly practical and flexible. A state sequence histogram (SSH) transformed from raw acceleration data by using STSA can capture the main features of structure dynamics and alleviate the effects of harmful noise. SSHs of the normal and abnormal states of a structure are defined as self and non-self elements, respectively. A new detector generation strategy and matching mechanism is proposed that makes the procedure more effective, along with guidelines for appropriate parameters in SNS. Numerical simulations and experimental verifications for different abnormal state cases were conducted to demonstrate the feasibility of the proposed method.

Original languageEnglish
Pages (from-to)80-97
Number of pages18
JournalStructural Control and Health Monitoring
Volume21
Issue number1
DOIs
Publication statusPublished - 2014 Jan

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Time series analysis
Detectors
Sensors
Computer simulation

Keywords

  • building structures
  • damage detection
  • negative selection
  • state sequence histogram
  • symbolic time series analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanics of Materials

Cite this

Abnormal state detection of building structures based on symbolic time series analysis and negative selection. / Li, Rongshuai; Mita, Akira; Zhou, Jin.

In: Structural Control and Health Monitoring, Vol. 21, No. 1, 01.2014, p. 80-97.

Research output: Contribution to journalArticle

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