The use of neural networks for CPT-based liquefaction screening

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dc.contributor.authorErzin Y.
dc.contributor.authorEcemis N.
dc.date.accessioned2024-07-22T08:13:51Z
dc.date.available2024-07-22T08:13:51Z
dc.date.issued2015
dc.description.abstractThis study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently. © 2014, Springer-Verlag Berlin Heidelberg.
dc.identifier.DOI-ID10.1007/s10064-014-0606-8
dc.identifier.issn14359529
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/16428
dc.language.isoEnglish
dc.publisherSpringer Verlag
dc.subjectForecasting
dc.subjectLiquefaction
dc.subjectMean square error
dc.subjectOptimal systems
dc.subjectSilt
dc.subjectArtificial neural network models
dc.subjectCoefficient of determination
dc.subjectCone penetration resistance
dc.subjectCyclic tri-axial tests
dc.subjectLiquefaction resistance
dc.subjectMean absolute error
dc.subjectPrediction performance
dc.subjectRoot mean square errors
dc.subjectNeural networks
dc.titleThe use of neural networks for CPT-based liquefaction screening
dc.typeArticle

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