A novel bridge structure damage diagnosis algorithm based on statistical pattern recognition

Haitao Xiao, Cheng Lu, Harutoshi Ogai, Koushik Roy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a structure damage detection algorithm based on statistical pattern recognition to analyze the acquired data to evaluate the health level of bridge. In this algorithm a novel statistical pattern recognition damage detection algorithm including a new damage sensitive index DSPR is proposed to determine the severity and location of damages. This paper also presents simulation and experiment, including a detection experiment of making artificial damage to a real bridge, to show that our design choices are indeed quite effective.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
PublisherSociety of Instrument and Control Engineers (SICE)
Pages775-780
Number of pages6
ISBN (Electronic)9784907764463
DOIs
Publication statusPublished - 2014 Oct 23
Event2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2014 - Sapporo, Japan
Duration: 2014 Sep 92014 Sep 12

Publication series

NameProceedings of the SICE Annual Conference

Conference

Conference2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2014
CountryJapan
CitySapporo
Period14/9/914/9/12

    Fingerprint

Keywords

  • Statistical pattern recognition
  • WSN (wireless sensor network)
  • bridge diagnosis
  • system design

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Xiao, H., Lu, C., Ogai, H., & Roy, K. (2014). A novel bridge structure damage diagnosis algorithm based on statistical pattern recognition. In Proceedings of the SICE Annual Conference (pp. 775-780). [6935224] (Proceedings of the SICE Annual Conference). Society of Instrument and Control Engineers (SICE). https://doi.org/10.1109/SICE.2014.6935224