Neural identification of compaction characteristics for granular soils

• Autorzy: Kłos M. , Waszczyszyn Z. , Sulewska M.
• Języki publikacji: EN

Słowa kluczowe

EN

granular soils; optimum water content; maximum dry density; neural network; multi-layered perception; semi-bayesian neural network; principal component analysis

PL

grunty gruboziarniste; charakterystyka zagęszczenia; optymalna zawartość wody; sieci neuronowe; sieci bayesowskie

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2011
• Tom: Vol. 18, no. 4
• Strony: 265--273
• Opis fizyczny: Bibliogr. 11 poz., rys., tab., wykr.

Twórcy

autor

Kłos M.

autor

Waszczyszyn Z.

autor

Sulewska M.

• Rzeszów University of Technology, Chair of Structural Mechanics W. Pola 2, 35-959 Rzeszów, Poland,

Bibliografia

• [1] H. Demuth, M. Beale. Neural Network Toolbox for Use with MATLAB, User’s Guide, Version 3, The Mathworks Inc., 1998.
• [2] S.S. Haykin. Neural Networks – A Comprehensive Introduction, 2nd Ed. Prentice Hall, 1999.
• [3] I.T. Nabney. Netlab – Algorithms for Pattern Recognition. Springer, London, 2004.
• [4] Y.M. Najjar. On the identification of compaction characteristics by neuronets. Computers and Geotechnics, 18: 167–187, 1996.
• [5] R.R. Proctor. Fundamental principles of soil compaction. Eng. News Records, 111: 245–248, 1993.
• [6] R.R. Proctor. Description of field and laboratory methods. Eng. News Records, 111: 286–289, 1993.
• [7] S.K. Sinha, M.C. Wang. Artificial neural network prediction models for soil compaction and permeability. Geotech. Geol. Eng., 26: 47–64, 2008.
• [8] M. Sulewska. Artificial Neural Networks for Evaluation of Compaction Parameters of Cohesionless Soils (in Polish). Committee Civil Eng. of the Polish Acad. Sci., Warsaw – Białystok, 2009.
• [9] M. Sulewska. Artificial Neural Modelling of Compaction Characteristics of Cohesionless Soil. Comp. Aided Mech. Eng. Sci., 17: 27–40, 2010.
• [10] Z. Waszczyszyn, M. Słoński. Maximum of marginal likelihood criterion instead of cross-validation for designing of artificial neural networks, In: L. Rutkowski et al., [Eds.], 9th Artificial Intelligence and Soft Computing ICAISC 2008, LNAI 5097, pp. 186–194. Springer, Berlin – Heidelberg – New York, 2008.
• [11] Z. Waszczyszyn, M. Słoński. Selected problems of artificial neural network development, Ch. 5 in: Z. Waszczyszyn [Ed.], Advances of Soft Computing in Engineering, CISM Courses and Lectures, vol. 512, pp. 237–316. Springer, Wien – New York, 2010.