Identification of added mass on a two-storey framed structure utilising FRFs and ANNs

U. Dackermann, J. Li, B. Samali

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

1 Citation (Scopus)

Abstract

![CDATA[This paper presents a vibration-based damage detection method that utilises frequency response functions (FRFs) to identify added mass on a two-storey framed structure. Added mass is used to simulate frequency changes due to structural damage. Artificial neural networks (ANNs) are employed to map changes in FRFs to locations of the added mass. In order to obtain suitable inputs for neural network training, principal component analysis (PCA) techniques are adopted to reduce the size of the FRF data and to filter noise. A hierarchy of neural network ensembles is used to take advantage of individual measurement characteristics from different sensors. The method is tested on laboratory and numerical models of a two-storey framed structure. From the two kinds of structures, FRF data are determined and compressed utilising PCA techniques. The PCA-reduced FRFs are then used as input patterns for training and testing of ANN ensembles predicting different locations of added mass.]]
Original languageEnglish
Title of host publicationIncorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010
PublisherCRC Press
Pages757-762
Number of pages6
ISBN (Print)9780415616577
DOIs
Publication statusPublished - 2011
EventAustralasian Conference on the Mechanics of Structures and Materials -
Duration: 11 Dec 2012 → …

Conference

ConferenceAustralasian Conference on the Mechanics of Structures and Materials
Period11/12/12 → …

Keywords

  • frequency response (dynamics)
  • neural networks (computer science)
  • structural health monitoring
  • vibration

Fingerprint

Dive into the research topics of 'Identification of added mass on a two-storey framed structure utilising FRFs and ANNs'. Together they form a unique fingerprint.

Cite this