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Paper Information

Journal:   INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)   SUMMER 2007 , Volume 18 , Number 2; Page(s) 11 To 20.
 
Paper: 

UTILIZING ARTIFICIAL NEURAL NETWORKS IN DETERMINATION OF UN-DRAINED COHESION VALUE OF CLAY USING NUMBER OF SPT, OVERBURDEN PRESSURE, MOISTURE CONTENT AND ATTERBERG LIMITS

 
 
Author(s):  ARABANI M., VEYS KARAMI M.
 
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Abstract: 

Utilizing artificial neural networks (ANN) simplify data processing operations in a broad range area in engineering. ANN is an oversimplified simulation of the human brain and composed of simple processing units referred to as neurons.
This method was successfully applied to develop prediction of the patterns, functions approximation, optimization, future value prediction, automotive control and information recovery. Un-drained cohesion value plays an important role in geotechnical earthquake design of earth fill structures.
In addition to laboratory test methods, in-situ testing methods as CPT and SPT provide reliable results. Different equations, charts and methods were provided by many researchers to describe the contingency between in situ test results and geotechnical parameters in recent decades.

The present study investigates the relation between overburden pressure (s°), number of SPT (NSPT), plasticity index (PI), liquid limit (LL) and moisture content with un-rained cohesion value of clay (Cu). On the basis of VU triaxial test and unconfined compression test results presented in geotechnical studies from different sites of Iran, an artificial neural network model was developed to comprise the laboratory test results and the results provide by the model. The artificial neural network model results presented in this study exhibit a good agreement with those of laboratory test.

 
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References: 
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