Damage prediction in structural mechanics using partitioning approach

A. Lazarevic, R. Kanapady, Kumar K Tamma, C. Kamath, Vipin Kumar

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

In this paper, a novel data mining approach to address damage detection within the large-scale complex structures is proposed. Every structure is defined by the set of finite elements that also represent the number of target variables. Since large-scale complex structures may have extremely large number of elements, predicting the failure in every single element using the original set of natural frequencies as features is exceptionally time-consuming task. Therefore, in order to reduce the time complexity we propose a hierarchical localized approach for partitioning the entire structure into substructures and predicting the failure within these substructures. Unlike our previous sub-structuring approach, which is based on physical substructures in the structure, here we propose to partition the structure into sub-structures employing hierarchical clustering algorithm that also allows localizing the damage in the structure. Finally, when the identified substructure with a failure consists of sufficiently small number of target variables the extent of the damage in the element of the substructure is predicted. A numerical example analyses on an electric transmission tower frame is presented to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)202-210
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5098
DOIs
StatePublished - Sep 19 2003
EventData Mining and Knowledge Discovery: Theory, Tools and Technology V - Orlando, FL, United States
Duration: Apr 21 2003Apr 22 2003

Keywords

  • Clustering
  • Damage detection
  • Data mining
  • Finite element analysis
  • Neural network

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