Design of Jetty Piles Using Artificial Neural Networks

Joint Authors

Lee, Yongjei
Lee, Sungchil
Bae, Hun-Kyun

Source

The Scientific World Journal

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-08-07

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Medicine
Information Technology and Computer Science

Abstract EN

To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted.

To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases.

The trained ANN was verified with another FE analysis case and then used as a structural analyzer.

The multilayer neural network (MBPNN) with two hidden layers was used for ANN.

The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles.

The results from the MBPNN agree well with those from FE analysis.

Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

American Psychological Association (APA)

Lee, Yongjei& Lee, Sungchil& Bae, Hun-Kyun. 2014. Design of Jetty Piles Using Artificial Neural Networks. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049491

Modern Language Association (MLA)

Lee, Yongjei…[et al.]. Design of Jetty Piles Using Artificial Neural Networks. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1049491

American Medical Association (AMA)

Lee, Yongjei& Lee, Sungchil& Bae, Hun-Kyun. Design of Jetty Piles Using Artificial Neural Networks. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1049491

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1049491