Damage Diagnosis of a Building Structure Using Support Vector Machine and Modal Frequency Patterns

Akira Mita, Hiromi Hagiwara

Research output: Contribution to journalConference articlepeer-review

18 Citations (Scopus)


A method using the support vector machine (SVM) to detect local damages in a building structure with the limited number of sensors is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. The method is verified to have capability to identify not only the location of damage but also the magnitude of damage with satisfactory accuracy. In our proposed method, feature vectors derived from the modal frequency patterns are used after proper normalization. The feature vectors contain the information on the location and magnitude of damages. As the method does not require modal shapes, typically only two vibration sensors are enough for detecting input and output signals to obtain the modal frequencies. The support vector machines trained for single damage is also effective for detecting damage in multiple stories.

Original languageEnglish
Pages (from-to)118-125
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 2003 Nov 26
EventSmart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures - San Diego, CA, United States
Duration: 2003 Mar 32003 Mar 6


  • Damage detection
  • Health monitoring
  • Modal frequency change
  • Support vector machine

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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