PREDICTION OF INDUCED STRESSES WITHIN SOIL MASS USING ARTIFICIAL NEURAL NETWORK

Main Article Content

Zainal Abdul Kareem Esmat

Abstract

In this paper, an Artificial Neural Network (ANN) is applied to predict the soil stress within a soil mass for a variety of depths and displacements under applied loading. The neural network model is created and applied for two cases, point load, and Uniform rectangular load.
Theses two cases were selected among many other cases of loading as a representation of the capabilities of ANN in finding proper solutions. The first case needs one input to get one output, where in the second case we need two inputs, to get one output only.
Results revealed that function approximation using neural network can be applied easily and can give accurate results by choosing the appropriate learning algorithm, number of layers, and number of neurons to solve the problem. ANN model can provide reasonable accuracy for civil engineering problems, and a more effective tool for engineering applications.

Article Details

How to Cite
“PREDICTION OF INDUCED STRESSES WITHIN SOIL MASS USING ARTIFICIAL NEURAL NETWORK” (2008) Journal of Engineering, 14(04), pp. 3179–3197. doi:10.31026/j.eng.2008.04.24.
Section
Articles

How to Cite

“PREDICTION OF INDUCED STRESSES WITHIN SOIL MASS USING ARTIFICIAL NEURAL NETWORK” (2008) Journal of Engineering, 14(04), pp. 3179–3197. doi:10.31026/j.eng.2008.04.24.

Publication Dates

References

Das, B.M., 1998, “Principles of Geotechnical Engineering”, PWS Publishing Company, Boston.

Demuth, H., Mark B., and Martin H., 2008, ” Neural Network Toolbox™ 6 User’s Guide”, The MathWorks, Inc.

Jeng, D.S., Cha D. H. and Blumenstein M., 2003, “Application of Neural Network in Civil Engineering Problems“, Proceedings of the International Conference on Advances in the Internet, Processing, Systems and Interdisciplinary Research (IPSI-2003).

Zurada, J. M., 1992, “Introduction to Artificial Neural Systems“, PWS Publishing Company.

Similar Articles

You may also start an advanced similarity search for this article.