Assessment of Cyclic Resistance Ratio (CRR) in Silty Sands Using Artificial Neural Networks

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ISSN Print: 2164-3164
ISSN Online: 2164-3172
www.scirp.org/journal/ojce
E-mail: ojce@scirp.org

"Assessment of Cyclic Resistance Ratio (CRR) in Silty Sands Using Artificial Neural Networks"
written by Hassan Sharafi, Sahar Jalili,
published by Open Journal of Civil Engineering, Vol.4 No.3, 2014

has been cited by the following article(s):

[1]	Analysis of potential liquefaction of sandy soils using effective confining pressure
	2020

[2]	Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands
	2020

[3]	THE USE OF ARTIFICIAL NEURAL NETWORKS (ANNS) IN GEOTECHNICS
	2019

[1]	Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands IOP Conference Series: Earth and Environmental Science, 2020 DOI:10.1088/1755-1315/491/1/012048

[2]	Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands IOP Conference Series: Earth and Environmental Science, 2020 DOI:10.1088/1755-1315/491/1/012048

[3]	Application of artificial intelligence techniques in prediction of cyclic resistance ratio (CRR) of clean sands IOP Conference Series: Earth and Environmental Science, 2020 DOI:10.1088/1755-1315/491/1/012048

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