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

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ARABANI M.; VEYS KARAMI M.

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Journal: INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN) Year:2007 | Volume:18 | Issue:2 Page(s): 11-20

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Title

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

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ARABANI M. | VEYS KARAMI M. | Issue Writer Certificate

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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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APA: Copy

ARABANI, M., & VEYS KARAMI, M.. (2007). UTILIZING ARTIFICIAL NEURAL NETWORKS IN DETERMINATION OF UN-DRAINED COHESION VALUE OF CLAY USING NUMBER OF SPT, OVERBURDEN PRESSURE, MOISTURE CONTENT AND ATTERBERG LIMITS. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), 18(2), 11-20. SID. https://sid.ir/paper/65494/en

Vancouver: Copy

ARABANI M., VEYS KARAMI M.. UTILIZING ARTIFICIAL NEURAL NETWORKS IN DETERMINATION OF UN-DRAINED COHESION VALUE OF CLAY USING NUMBER OF SPT, OVERBURDEN PRESSURE, MOISTURE CONTENT AND ATTERBERG LIMITS. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)[Internet]. 2007;18(2):11-20. Available from: https://sid.ir/paper/65494/en

IEEE: Copy

M. ARABANI, and M. VEYS KARAMI, “UTILIZING ARTIFICIAL NEURAL NETWORKS IN DETERMINATION OF UN-DRAINED COHESION VALUE OF CLAY USING NUMBER OF SPT, OVERBURDEN PRESSURE, MOISTURE CONTENT AND ATTERBERG LIMITS,” INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), vol. 18, no. 2, pp. 11–20, 2007, [Online]. Available: https://sid.ir/paper/65494/en

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