Prediction of cyclic resistance ratio for silty sands and its applications in the simplified liquefaction analysis

Jafarian, Yaser; Vakili, Rouzbeh; Abdollahi, A. Sadeghi

doi:10.1016/j.compgeo.2013.04.001

Cited by 10 publications

(1 citation statement)

References 24 publications

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“…In case of complicated problems, experimentalists prefer these trial approaches rather than analytical optimization. Numerous researchers applied artificial intelligence approaches in the various fields of geotechnical engineering such as stress-strain modeling of soil (Penumadu and Zhao, 1999), slope stability (McCombie and Wilkinson, 2002), shallow foundations (Shahin et al, 2002), soil liquefaction Jafarian, 2007, Baziar et al, 2011;Ghorbani et al, 2012;Jafarian et al, 2013), and earthquake engineering (Jafarian et al, 2010).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive model for normalized shear modulus of cohesive soils

Jafarian¹

2013

Acta Geodynamica Et Geomaterialia

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called maximum shear modulus, G max . It can be determined by in-situ methods from the measurements of the shear wave velocity, V s , by means of crosshole, down-hole, up-hole, or surface wave testing. The relationship between maximum shear modulus and shear wave velocity is rigorous:ABSTRACT Evaluating dynamic properties of geomaterials is an essential step for solving geotechnical earthquake engineering problems. The shear stiffness-reduction curves of soils are commonly presented in normalized form and have many applications in equivalent-linear and nonlinear dynamic analyses. In this study, a radial basis function (RBF) neural network model was developed to predict normalized shear modulus of cohesive soils. The most important factors that affect this parameter include effective confining pressure, shear strain, and plasticity index. The comprehensive database used for the development of the model was obtained from previously published experimental results. Validation of the model performance was carried out by using centrifuge tests results. A parametric analysis was then performed to evaluate sensitivity of the proposed model to variations of the influencing parameters. The results indicate that the neural network model could provide predictions more accurate than those obtained by the previous models.

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