A neural network approach for structural identification and diagnosis of a building from seismic response data

Chiung-Shiann Huang*, Shih-Lin Hung, C. M. Wen, T. T. Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

82 Scopus citations

Abstract

This work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back-propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a five-storey steel frame, subjected to different strengths of the Kobe earthquake, in shaking table tests.

Original languageEnglish
Pages (from-to)187-206
Number of pages20
JournalEarthquake Engineering and Structural Dynamics
Volume32
Issue number2
DOIs
StatePublished - 1 Jan 2003

Keywords

  • Damage assessment
  • Earthquake responses
  • Neural network
  • System identification

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