Data compression by Principal Component Analysis (PCA) in modelling of soil density parameters based on soil granulation

• Autorzy: Sulewska M. J. , Zabielska-Adamska K.

Identyfikatory

DOI

10.7306/gq.1169

• Języki publikacji: EN

Abstrakty

EN

The parameter for the density specification of naturally compacted non-cohesive soils and soils in embankments of hydraulic structures is the density index (I_D). The parameter used to control the quality of compaction of cohesive and non-cohesive soils artificially thickened, embedded in a variety of embankments is the degree of compaction (I_S). In order to determine the parameters of density (I_D or I_S), compaction parameters ( or should be examined in a laboratory, which often is a long and difficult procedure to carry out. Therefore, there is a need for methods of improving and shortening the test of compaction parameters based on the development and application of useful correlations. Since compaction parameters are dependent on the soil granulation, a method based on regression and artificial neural networks was applied to develop required correlations. Due to the large number of input variables of neural networks in relation to the number of case studies, a PCA method was used to reduce the number of input variables, which resulted in reduction in the size of neural networks.

Słowa kluczowe

EN

Artificial Neural Networks; principal component analysis; compaction parameters; minimum and maximum dry density of solid particles; graining parameters

• Wydawca: Państwowy Instytut Geologiczny - Państwowy Instytut Badawczy
• Czasopismo: Geological Quarterly
• Rocznik: 2015
• Tom: Vol. 59, No. 2
• Strony: 400--407
• Opis fizyczny: Bibliogr. 22 poz., tab., wykr.

Twórcy

autor

Sulewska M. J.

• Bialystok University of Technology, Faculty of Civil and Environmental Engineering, Wiejska 45A, 15-351 Białystok, Poland

autor

Zabielska-Adamska K.

• Bialystok University of Technology, Faculty of Civil and Environmental Engineering, Wiejska 45A, 15-351 Białystok, Poland

Bibliografia

• 1. Ahangar-Asr, A., Faramarzi, A., Mottaghifard, N., Javasi, A.A., 2011. Modelling of permeability and compaction characteristics of soils using evolutionary polynomial regression. Computer & Geosciences, 37: 1860-1869.
• 2. Alavi, A.H., Gandomi, A.H., Mollahassani, A., Heshmati, A., Rashed A., 2010. Modelling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. Journal of Plant Nutrition and Soil Science, 173: 368-379.
• 3. Barton, M.E., Cresswell, A., Brown, R., 2001. Measuring the effect of mixed grading on the maximum dry density of sands. Geotechnical Testing Journal, 24: 121-127.
• 4. Dąbska, A., Pisarczyk, S., 2012. Wyznaczenie parametrów zagęszczalności na podstawie innych parametrów geotechnicznych (in Polish). Inżynieria Morska i Geotechnika, 33: 320-324.
• 5. Gurtug, Y., Sridharan, A., 2004. Compaction behaviour and prediction of its characteristics of fine grained soils with particular reference to compaction energy. Soil Foundation, 44: 27-36.
• 6. Haykin, S., 1999. Neural Networks. A Comprehensive Foundation. Prentice Hall Inc., Upper Saddle River.
• 7. Kłos, M., Waszczyszyn, Z., Sulewska, M.J., 2011. Neural identification of compaction characteristics for granular soils. Computer Assisted Mechanics and Engineering Sciences, 18: 265-273.
• 8. Kuźniar, K., Waszczyszyn, Z., 2006. Neural networks and Principal Component Analysis for identification of building natural periods. Journal of Computing in Civil Engineering, 20: 431-436.
• 9. Lade, P.V., Liggio, C.D., Yamamuro, J.A., 1998. Effects of non-plastic fines on minimum and maximum void ratios of sand. Geotechnical Testing Journal, 21: 336-347.
• 10. Osowski, S., 2006. Sieci neuronowe do przetwarzania informacji (in Polish). Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa.
• 11. Pisarczyk, S., 1977. Zagęszczalność gruntów gruboziarnistych i kamienistych (in Polish). Politechnika Warszawska Wydział Inżynierii Lądowej Instytut Dróg i Mostów, Warszawa.
• 12. PN-88/B-04481, 1988. Grunty budowlane (in Polish). Badania próbek gruntu.
• 13. Proctor, R.R., 1933. Fundamental principles of soil compaction. Engineering News-Record, 111: 245-248.
• 14. Singha, S.K., Wang, M.C., 2008. Artificial neural network prediction models for soil compaction and permeability. Geotechnical and Geological Engineering, 26: 47-64.
• 15. Sivrikaya, O., 2008. Models of compacted fine grained soils used as mineral liner for solid waste. Envi ionmenial Geology, 53: 1585-1595.
• 16. Sivrikaya, O., Togrol, E., Kayadelen, C., 2008. Estimating compaction behavior of fine-grained soils based on compaction energy. Canadian Geotechnical Journal, 45: 877-887.
• 17. Stanisz, A., 2007. Przystępny kurs statystyki z wykorzystaniem programu STATISTICA PL na przykładach z medycyny (in Polish). Vol. 2 and 3. StatSoft Polska Sp. z o.o., Kraków.
• 18. Sulewska, M.J. 2012. Zastosowanie sztucznych sieci neuronowych w badaniach wybranych parametrów geotechnicznych (in Polish). Inżynieria Morska i Geotechnika, 33: 388-392.
• 19. Sulewska, M.J., 2010a. Neural modelling of compactibility characteristics of cohesionless soil. Computer Assisted Mechanics and Engineering Sciences, 17: 27-40.
• 20. Sulewska, M.J., 2010b. Prediction models for mini mum and maximum dry density of non-cohesive soils. Polish Journal of Environmental Studies, 19: 797-804.
• 21. Waszczyszyn, Z., 1999. Neural Networks in the Analysis and Design of Structures. CISM Courses and Lectures, No 404, International Centre for Mechanical Sciences, Springer, Wien-New York.
• 22. Zabielska-Adamska, K., Sulewska, M.J., 2012. ANN-based modelling of fly ash compaction curve. Archives of Civil Engineering, 58: 57-69.