Prediction of Shear Strength for Subgrade Soil under Freezing-Thawing Based on Back Propagation Neural Network

Jing Long Chen; Pei Feng Cheng; Chuan Jun Yin

doi:10.4028/www.scientific.net/AMR.926-930.610

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Prediction of Shear Strength for Subgrade Soil...

Prediction of Shear Strength for Subgrade Soil under Freezing-Thawing Based on Back Propagation Neural Network

Abstract:

Soil samples are taken from two experimental roads in Heilongjiang province for the test. Then a prediction of shear strength is carried out, basing on a three-layer BP (back propagation) network in Matlab, the hidden layer, output layer and training function of which adopt non-linear transfer function tansig, linear transfer function purelin, and trainbfg function respectively. It is found workable to predict factors influencing shearing strength using BP neural network with given soil properties. Prediction results of cohesion strength for clay show a better performance than those for sandy soil, while results of friction angle for sandy soil are better than those for clay. It is indicated that BP neural network does a better work in predicting the friction angle than that of cohesion.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

610-614

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.610

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Online since:

May 2014

Authors:

Jing Long Chen*, Pei Feng Cheng, Chuan Jun Yin

Keywords:

BP Neural Network (BPNN), Cohesion, Freeze-Thaw, Internal Friction Angle, Shear Strength, Subgrade Soil

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References

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