Three-dimensional responses of buried corrugated pipes and ANN-based method for predicting pipe deflections
This study investigated localized responses, such as circumferential stresses, on corrugation and pipe deflections. Also, this study examined the effect of corrugation geometry on the overall and localized response of corrugated pipes with refined three‐dimensional modeling of the entire soil–pipe i...
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Veröffentlicht in: | International journal for numerical and analytical methods in geomechanics 2012-01, Vol.36 (1), p.1-16 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This study investigated localized responses, such as circumferential stresses, on corrugation and pipe deflections. Also, this study examined the effect of corrugation geometry on the overall and localized response of corrugated pipes with refined three‐dimensional modeling of the entire soil–pipe interaction system, including corrugation. To investigate the availability of the traditional two‐dimensional method, the results from the three‐dimensional finite element method (FEM) were compared with those from the two‐dimensional FEM. The soil–pipe modeling techniques of this study were verified by comparing the FEM results by Utah State University and analytical results. An artificial neural network (ANN)‐based model to predict vertical deflections of buried corrugated pipes was developed to overcome the shortcomings of existing methods and obtain results that are close to the level of accuracy of FEM results. In order to train an ANN, analyses on a large amount of data were executed with various standardized pipe geometries and burial depths regulated by the Korea Highway Corporation using the two‐dimensional FEM verified in this study. The widely used back propagation algorithm was adopted. The ANN‐based model developed in this study was shown to be an effective tool by comparing the results with test data and sensitivity analyses were executed based on the data from the developed ANN. Copyright © 2010 John Wiley & Sons, Ltd. |
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ISSN: | 0363-9061 1096-9853 |
DOI: | 10.1002/nag.986 |