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Neural Net Expansion Model for Fissured Strong Expansive Soil

dc.contributor.authorZhang-jun, D.
dc.contributor.authorOjekunle, V.O.
dc.contributor.authorShan-xiong, C.
dc.identifier.citationZhang-jun, D. (2016). Neural Net Expansion Model for Fissured Strong Expansive Soil. Journal of Residuals Science & Technology, Vol.13(2)en_US
dc.descriptionStaff publicationsen_US
dc.description.abstractFissured strong expansive soil swelling behavior is complicated. In this paper, considering the typical filling fissures of strong expansive soils, fissure rate K r was given as a fissure content quantitative indicator. A prediction model was developed for the prediction of swelling effect on a fissured strong expansive soil using BP neural net- work approach, the gradient descent and the conjugate gradient algorithm methods were adopted. The actual test and predicted results of the two algorithms network showed high degree of similarity. The BP neural network model described by fissure rate, dry density, initial moisture content and overlying load can meet the precision requirements. The conjugate gradient method when compared with the gradient descent method, has a significantly improved calculation efficiency, the convergence rate is about 30 times less- er than the latter, therefore, conjugate gradient algorithm BP network prediction model for swelling in the actual engineering calculation has obvious advantages.en_US
dc.description.sponsorshipChinese Academy of Sciencesen_US
dc.relation.ispartofseriesJournal of Residuals Science & Technology;Vol.13(2)
dc.subjectBP neural network;Fissured strong expansive soil;conjugate gradient method;gradient descenten_US
dc.subjectBP neural networken_US
dc.subjectFissured strong expansive soilen_US
dc.subjectConjugate gradient methoden_US
dc.subjectGradient descenten_US
dc.subjectResearch Subject Categories::NATURAL SCIENCESen_US
dc.titleNeural Net Expansion Model for Fissured Strong Expansive Soilen_US

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