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dc.contributor.authorLeong, Hsiao Yun
dc.contributor.authorOng, Dominic Ek Leong
dc.contributor.authorSanjayan, Jay G
dc.contributor.authorNazari, Ali
dc.contributor.authorKueh, Sze Miang
dc.date.accessioned2019-05-29T12:33:21Z
dc.date.available2019-05-29T12:33:21Z
dc.date.issued2018
dc.identifier.issn0899-1561
dc.identifier.doi10.1061/(ASCE)MT.1943-5533.0002246
dc.identifier.urihttp://hdl.handle.net/10072/375625
dc.description.abstractThe identification of significant input variables to the output provides very useful information for mix design for soil-fly ash geopolymer in order to obtain the optimum compressive strength. The importance of input variables to the output of soil-fly ash geopolymer is quantified by Garson’s algorithm and connection weights approach in an artificial neural networks (ANN) model, whereas model analysis and fitness method are used in a genetic programming (GP) model. The former approaches in the ANN model use the connection weights among the input, hidden, and output layers to evaluate the importance of the input variables. The latter methods in the GP model assess the frequency of variables used in the model and the value of fitness for the evaluation. The assessment results identify the percentages of fly ash, water, and soil as important input variables to the output. The percentage of hydroxide and the ratios of silicate to hydroxide and alkali activator to ash are ranked as less important input variables. The positive or negative relationships between these input variables and the output demonstrate a very significant influence on the strength development of soil-fly ash geopolymer, showing a positive or negative effect on the compressive strength.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAmerican Society of Civil Engineers
dc.publisher.placeUnited States
dc.relation.ispartofchapter4018129
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto17
dc.relation.ispartofissue7
dc.relation.ispartofjournalJournal of Materials in Civil Engineering
dc.relation.ispartofvolume30
dc.subject.fieldofresearchCivil Geotechnical Engineering
dc.subject.fieldofresearchCivil Engineering
dc.subject.fieldofresearchMaterials Engineering
dc.subject.fieldofresearchcode090501
dc.subject.fieldofresearchcode0905
dc.subject.fieldofresearchcode0912
dc.titleEffects of Significant Variables on Compressive Strength of Soil-Fly Ash Geopolymer: Variable Analytical Approach Based on Neural Networks and Genetic Programming
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionPost-print
gro.rights.copyright© 2018 American Society of Civil Engineers (ASCE). This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorOng, Dominic E.L.


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