Artificial neural network in CPT base liquefaction prediction

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dc.contributor.authorRahman, M.M.
dc.contributor.authorLo, S.R.
dc.contributor.authorMofiz, S.A.
dc.date.accessioned2010-06-08T22:50:45Z
dc.date.available2010-06-08T22:50:45Z
dc.date.issued2010en
dc.description.abstractVarious simplified procedue have been developed, using case studies that liquefied or not during earthquake, to estimate liquefaction potential of soils. In order to address the collective knowledge built up in conventional liquefaction engineering, this paper proposed to use a artificial neural network, ANN as an alternative tools. ANN has the capability to train itself with available data sets and extrapolate outcome for unknown senerio based on the training. It is particularly helpful for large data sets when human brain is inefficient. Various ANN models have already been in used for liquefaction assessment. However, this paper is more objective in applying ANN in liquefcation prediction. First, the data bases used for training and testing are well verified and well accepted in literature. Second, the inputs for the model are selected on their physical meaning with respect to liquefaction. Third, the source of data sets for training and testing are diffrent. The ANN model achieved a comparable accuracy with other publications where large number of inputs has been used.en
dc.identifier.citationRahman, M.M., Lo, S.R., Mofiz, S.A. (2010) Artificial neural network in CPT base liquefaction prediction. Taipei, Taiwan: 17th Southeast Asian Geotechnical Conference, 10-13 May 2010.en
dc.identifier.urihttp://hdl.handle.net/10092/3963
dc.language.isoen
dc.publisherUniversity of Canterbury. Civil and Natural Resources Engineeringen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subject.marsdenFields of Research::290000 Engineering and Technology::290800 Civil Engineeringen
dc.subject.marsdenFields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processingen
dc.subject.marsdenFields of Research::300000 Agricultural, Veterinary and Environmental Sciences::300100 Soil and Water Sciencesen
dc.titleArtificial neural network in CPT base liquefaction predictionen
dc.typeConference Contributions - Published
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