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Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 18 | Page No.: 3068-3082
DOI: 10.3923/jas.2008.3068.3082
 
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Shear Modulus and Damping Ratio in Aggregate-Clay Mixtures: An Experimental Study Versus ANNs Prediction

A. Shafiee and R. Ghate

Abstract:
An extensive test program was conducted on kaolin-gravel and kaolin-sand mixtures to evaluate shear modulus, damping ratio and pore pressure of the mixtures. Test results reveal that shear modulus increases with aggregate content and confining stress and decreases with shear strain amplitude and loading cycles. Meanwhile, damping ratio depends mainly on aggregate content and shear strain amplitude and is less affected by loading cycles. Pore pressure also plays an important role in aggregate-kaolin mixtures and increases with aggregate content. An attempt has also been made to implement Artificial Neural Networks (ANNs) for modeling shear modulus, damping ratio and pore pressure build-up in the mixtures. The study indicates that ANNs can successfully model the complex relationship between cyclic deformation properties and input parameters including current state of stress, aggregate content, shear strain amplitude, confining stress, aggregate size and loading cycles.
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How to cite this article:

A. Shafiee and R. Ghate, 2008. Shear Modulus and Damping Ratio in Aggregate-Clay Mixtures: An Experimental Study Versus ANNs Prediction. Journal of Applied Sciences, 8: 3068-3082.

DOI: 10.3923/jas.2008.3068.3082

URL: https://scialert.net/abstract/?doi=jas.2008.3068.3082

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