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dc.contributor.authorShahin, M.en
dc.contributor.authorMaier, H.en
dc.contributor.authorJaksa, M.en
dc.identifier.citationJournal of Geotechnical and Geoenvironmental Engineering, 2002; 128(9):785-793en
dc.description© 2002 American Society of Civil Engineersen
dc.description.abstractOver the years, many methods have been developed to predict the settlement of shallow foundations on cohesionless soils. However, methods for making such predictions with the required degree of accuracy and consistency have not yet been developed. Accurate prediction of settlement is essential since settlement, rather than bearing capacity, generally controls foundation design. In this paper, artificial neural networks ~ANNs! are used in an attempt to obtain more accurate settlement prediction. A large database of actual measured settlements is used to develop and verify the ANN model. The predicted settlements found by utilizing ANNs are compared with the values predicted by three of the most commonly used traditional methods. The results indicate that ANNs are a useful technique for predicting the settlement of shallow foundations on cohesionless soils, as they outperform the traditional methods.en
dc.description.statementofresponsibilityMohamed A. Shahin; Holger R. Maier; and Mark B. Jaksaen
dc.publisherASCE-Amer Soc Civil Engineersen
dc.titlePredicting settlement of shallow foundations using neural networksen
dc.typeJournal articleen
pubs.library.collectionCivil and Environmental Engineering publicationsen
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]en
dc.identifier.orcidJaksa, M. [0000-0003-3756-2915]en
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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