Local Damage Assessment Of A Building Using Support Vector Machine
Author(s)
H. Hagiwara & A. Mita
Abstract
Local damage assessment of a building using
Support Vector Machine
H. Hagiwara & A. Mita
Department of System Design Engineering, Keio University
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.
1 Introduction
Many approaches for structural health monitoring (SHM) have been proposed
for
Keywords
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