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21 March 2003 Damage prediction in structural mechanics using partitioning approach
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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.
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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);

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