Damage prediction in structural mechanics using partitioning approach

Aleksandar M. Lazarevic; Ramdev Kanapady; Kumar K. Tamma; Chandrika Kamath; Vipin Kumar

doi:10.1117/12.487388

21 March 2003 Damage prediction in structural mechanics using partitioning approach

Aleksandar M. Lazarevic, Ramdev Kanapady, Kumar K. Tamma, Chandrika Kamath, Vipin Kumar

Proceedings Volume 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V; (2003) https://doi.org/10.1117/12.487388
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Aleksandar M. Lazarevic, Ramdev Kanapady, Kumar K. Tamma, Chandrika Kamath, and Vipin Kumar "Damage prediction in structural mechanics using partitioning approach", Proc. SPIE 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, (21 March 2003); https://doi.org/10.1117/12.487388

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