Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/33462
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dc.contributor.authorShahin, Mohamed Aminen
dc.contributor.authorMaier, Holger R.en
dc.contributor.authorJaksa, Mark Brianen
dc.date.issued2000en
dc.identifier.urihttp://hdl.handle.net/2440/33462-
dc.description.abstractIn recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems and have demonstrated some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this report, the relationship between the statistical properties of training, testing and validation sets and model performance and the effect of the proportion of data used for training, testing and validation on model performance are investigated for the case study of predicting the settlement of shallow foundations on cohesionless soils. In addition, a novel approach for data division, which is based on a self-organising map, is introduced and evaluated for the above case study. The results obtained indicate that the statistical properties of the data in the training, testing and validation sets need to be taken into account to ensure that optimal model performance is achieved. The data division method introduced in this paper is found to negate the need to choose which proportion of the data to use for training, testing and validation and to ensure that each of the subsets are representative of the available data.en
dc.description.statementofresponsibilityM A Shahin, H R Maier, M B Jaksaen
dc.language.isoenen
dc.publisherUniversity of Adelaide. Department of Civil and Environmental Engineeringen
dc.relation.ispartofseriesResearch report (University of Adelaide. School of Civil and Environmental Engineering); R171en
dc.source.urihttp://www.ecms.adelaide.edu.au/civeng/research/reports/docs/R171.pdfen
dc.titleEvolutionary data division methods for developing artificial neural network models in geotechnical engineeringen
dc.typeReporten
dc.contributor.schoolSchool of Civil, Environmental and Mining Engineeringen
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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