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Premium A neural network approach for structural identification and diagnosis of a building from seismic response data
Author(s)
Huang C. S.,
Hung S. L.,
Wen C. M.,
Tu T. T.
Publication year2003
Publication title
earthquake engineering and structural dynamics
Resource typeJournals
PublisherJohn Wiley & Sons
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. Copyright © 2002 John Wiley & Sons, Ltd.
Subject(s)acceleration , acoustics , artificial neural network , biology , botany , chemistry , classical mechanics , computer science , earthquake engineering , earthquake shaking table , earthquake simulation , engineering , frame (networking) , ground motion , identification (biology) , incremental dynamic analysis , machine learning , modal , peak ground acceleration , physics , polymer chemistry , seismic analysis , structural engineering , telecommunications , weighting
Language(s)English
SCImago Journal Rank2.218
H-Index127
eISSN1096-9845
pISSN0098-8847
DOI10.1002/eqe.219

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