Name
Abstract | ||
|---|---|---|
A computational method on damage detection problems in structures was developed using neural networks. The problem considered in this work consists of estimating the existence, location and extent of stiffness reduction in structure which is indicated by the changes of the structural static parameters such as deflection and strain. The neural network was trained to recognize the behaviour of static parameter of the undamaged structure as well as of the structure with various possible damage extent and location which were modeled as random states. The proposed techniques were applied to detect damage in a cantilever beam. The structure was analyzed using finite-element-method (FEM) and the damage identification was conducted by a back-propagation neural network using the change of the structural strain and displacement. The results showed that using proposed method the strain is more efficient for identification of damage than the displacement. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-00264-9_16 | Studies in Computational Intelligence |
Keywords | Field | DocType |
back-propagation,damage detection,finite element method,neural network | Deflection (engineering),Stiffness,Cantilever,Computer science,Finite element method,Artificial intelligence,Artificial neural network,Backpropagation,Machine learning,Structural engineering | Journal |
Volume | ISSN | Citations |
192 | 1860-949X | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ismoyo Haryanto | 1 | 0 | 0.34 |
| Joga Dharma Setiawan | 2 | 14 | 1.78 |
| Agus Budiyono | 3 | 34 | 12.21 |