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Prediction of Scour Depth Using Incorporation of Cluster Analysis into Artificial Neural Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | 이창환 안재현 Heon, Lee Joo Kim, Tae-Woong |
| Copyright Year | 2009 |
| Abstract | A local scour around a bridge pier is known as one of important factors of bridge collapse. Two approaches are usually used in estimating a scour depth in practice. One is to use empirical formulas, and the other is to use computational methods. But the use of empirical formulas is limited to predict a scour depth under similar conditions to which the formulas were derived. Computational methods are currently too expensive to be applied to practical engineering problems. This study presented the application of artificial neural networks (ANN) to the prediction of a scour depth around a bridge pier at an equilibrium state. This study also investigated various ANN algorithms for estimating a scour depth, such as Backpropagation Network, Radial Basis Function Network, and Generalized Regression Network. Preliminary study showed that ANN models resulted in very wide range of errors in predicting a scour depth. To solve this problem this study incorporated cluster analysis into ANN. The incorporation of cluster analysis provided better estimations of scour depth up to 42% compared with other approaches. |
| Starting Page | 111 |
| Ending Page | 120 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| Volume Number | 29 |
| Alternate Webpage(s) | http://www.jbuwater.co.kr/file/031.PDF |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
