Structural damage detection using empirical mode decomposition: Experimental investigation

You Lin Xu, J. Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

125 Citations (Scopus)

Abstract

This paper presents an experimental investigation on the applicability of the empirical mode decomposition (EMD) for identifying structural damage caused by a sudden change of structural stiffness. A three-story shear building model was constructed and installed on a shaking table with two springs horizontally connected to the first floor of the building to provide additional structural stiffness. Structural damage was simulated by suddenly releasing two pretensioned springs either simultaneously or successively. Various damage severities were produced using springs of different stiffness. A series of free vibration, random vibration, and earthquake simulation tests were performed on the building with sudden stiffness changes. Dynamic responses including floor accelerations and displacements, column strains, and spring releasing time instants were measured. The EMD was then applied to measured time histories to identify damage time instant and damage location for various test cases. The comparison of identified results with measured ones showed that damage time instants could be accurately detected in terms of damage spikes extracted directly from the measurement data by EMD. The damage location could be determined by the spatial distribution of the spikes along the building. The influence of damage severity, sampling frequency, and measured quantities on the performance of EMD for damage detection was also discussed.
Original languageEnglish
Pages (from-to)1279-1288
Number of pages10
JournalJournal of Engineering Mechanics
Volume130
Issue number11
DOIs
Publication statusPublished - 1 Nov 2004

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Structural damage detection using empirical mode decomposition: Experimental investigation'. Together they form a unique fingerprint.

Cite this