Local damage assessment of a building using Support Vector Machine

H. Hagiwara, Akira Mita

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A damage detection method utilizing the Support Vector Machine (SVM) is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. Modal frequencies of a structure are used for pattern recognition in the proposed method. Typically, only two vibration sensors detecting a single input and a single output for a structural system can easily determine modal frequencies. For training SVMs the relationship between changes normalised by original modal frequencies, before suffering any damage, is utilized. The SVM trained for single damage was also found to be effective for detecting damage in multiple stories. The SVM based damage assessment is able to identify damage qualitatively as well as quantitatively.

Original languageEnglish
Title of host publicationWIT Transactions on the Built Environment
PublisherWITPress
Pages235-244
Number of pages10
Volume72
Publication statusPublished - 2003

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ASJC Scopus subject areas

  • Architecture
  • Building and Construction
  • Safety, Risk, Reliability and Quality
  • Civil and Structural Engineering
  • Computer Science Applications
  • Arts and Humanities (miscellaneous)

Cite this

Hagiwara, H., & Mita, A. (2003). Local damage assessment of a building using Support Vector Machine. In WIT Transactions on the Built Environment (Vol. 72, pp. 235-244). WITPress.