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 language | English (US) |
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Pages (from-to) | 202-210 |
Number of pages | 9 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5098 |
DOIs | |
State | Published - 2003 |
Event | Data Mining and Knowledge Discovery: Theory, Tools and Technology V - Orlando, FL, United States Duration: Apr 21 2003 → Apr 22 2003 |
Keywords
- Clustering
- Damage detection
- Data mining
- Finite element analysis
- Neural network