Application of the ANFIS to analysis of results from soil testings
Ewa Daniszewska
Department of Geotechnics and Road Construction; The Faculty of Technical Sciences; University of Warmia and Mazury in Olsztyn (Poland)
Abstract
The article was analyzed in order to test applicability and capability of the ANFIS tool used for interpretation of results of triaxial shear tests on loamy soils sampled near Olsztyn. The ANFIS system in the Matlab software programme was used to model and determine relationships between the shear stress and soil resistance parameters in a triaxial shear test apparatus. It has been demonstrated that the achieved shear strength parameters are significantly affected by the variables tested during the triaxial experiments and physical parameters of a given soil sample, but also by the loading increment rate during the tests. It is extremely important to adjust the rate of loading during a test according to the preliminary characterization of a tested ground sample so as to have some control over the obtained ground strength parameters. The neuro-fuzzy model has been constructed based on a set of values obtained after a series of experimental tests, including values of ground shear strength parameters. The database used for the neuro-fuzzy modelling consisted of 6 different ground parameters for each of the 12 shear stress rates applied during the triaxial tests. The learnability was verified on a database composed of the test results – a neuro-fuzzy model was built from learning sets and its accuracy was verified by sets of tests to which the model was applied for the first time. The results obtained from the ANFIS model did not diverge substantially from the ones obtained directly by performing the physical tests. The ANFIS proved to be highly universal and easy to operate. It accounted for the multi-faceted nature of interrelationships between ground parameters.
Keywords:
adaptive neuro-fuzzy inference system, fuzzy logic, soil triaxial testing, shear speedReferences
Suchnicka H., Wytrzymałość gruntów – opis i badania. XI Krajowa Konferencja Mechaniki Gruntów i Fundamentowania – Gdańsk, 25-27 czerwca 1997, s. 47-74.
Google Scholar
Rutkowski, L. Flexible neuro-fuzzy systems: structures, learning and performance evaluation. Kluwer Academic Publishers, 2004.
Google Scholar
Akgun A., Sezer E.A., Nefeslioglu H.A., Gokceoglu C., Pradhan B. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers and Geosciences, Volume 38, Issue 1, s. 23-34.
DOI: https://doi.org/10.1016/j.cageo.2011.04.012
Google Scholar
Gokceoglu C., Zorlu K. A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence 2004, Vol. 17(1), s. 61–72.
DOI: https://doi.org/10.1016/j.engappai.2003.11.006
Google Scholar
Gokceoglu, C. A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. Engineering Geology 2002, Vol. 66, s. 39–51.
DOI: https://doi.org/10.1016/S0013-7952(02)00023-6
Google Scholar
den Hartog M.H., Babuska R., Deketh H.J.R., Alvarez Grima M., Verhoef P.N.W., Verbruggen H.B. Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. International Journal of Approximate Reasoning 1997, Vol.16, s. 43–66.
DOI: https://doi.org/10.1016/S0888-613X(96)00118-1
Google Scholar
Cabalar A.F., Cevik A., Gokceoglu C. Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering. Computers and Geotechnics Vol. 40, March 2012, s. 14–33.
DOI: https://doi.org/10.1016/j.compgeo.2011.09.008
Google Scholar
Provenzano P., Ferlisi S., Musso A. Interpretation of a model footing response through an adaptive neural fuzzy inference system. Computers and Geotechnics 2004, Vol.31, s.251–66.
DOI: https://doi.org/10.1016/j.compgeo.2004.03.001
Google Scholar
Kayadelen C., Gunaydin O., Fener M., Demir A., Ozvan A. Modeling of the angle of shearing resistance of soils using soft computing systems. Expert Systems with Applications 2009, Vol.36,s.11814–26.
DOI: https://doi.org/10.1016/j.eswa.2009.04.008
Google Scholar
Gokceoglu C., Yesilnacar E., Sonmez H., Kayabasi A.A. Neuro-fuzzy model for modulus of deformation of jointed rock masses. Computers and Geotechnics 2004, vol. 31, s.375–83.
DOI: https://doi.org/10.1016/j.compgeo.2004.05.001
Google Scholar
Rangel J.L., Iturraran-Viveros U., Ayala A.G., Cervantes F. Tunnel stability analysis during construction using a neuro-fuzzy system. International Journal for Numerical and Analytical Methods in Geomechanics 2005, Vol. 29, s.1433–56.
DOI: https://doi.org/10.1002/nag.463
Google Scholar
Zounemat-Kermani M., Beheshti A.A., Ataie-Ashtiani B, Sabbagh-Yazdi S.R. Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Applied Soft Computing 2009, Vol.9, s.746–55.
DOI: https://doi.org/10.1016/j.asoc.2008.09.006
Google Scholar
Kalkan E., Akbulut S., Tortum A., Celik S. Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environmental Geology 2008, Vol.58, s.1429–40.
DOI: https://doi.org/10.1007/s00254-008-1645-x
Google Scholar
Kayadelen C., Taskiran T., Gunaydin O., Fener M. Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils. Environmental Earth Sciences 2009, Vol. 59, s.109–15.
DOI: https://doi.org/10.1007/s12665-009-0009-5
Google Scholar
Pradhan B., Sezer E.A., Gokceoglu C., Buchroithner M.F. Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Transactions on Geoscience and Remote Sensing 2010,Vol.48[12], s. 4164–77.
DOI: https://doi.org/10.1109/TGRS.2010.2050328
Google Scholar
Jang, J.S.R. ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 1993, vol. 23, s. 665-685.
DOI: https://doi.org/10.1109/21.256541
Google Scholar
Daniszewska E. Zastosowanie adaptacyjnego, neuronowo-rozmytego systemu wnioskowania ANFIS w analizie wyników badania trójosiowego ściskania gruntów. Praca doktorska, Olsztyn 2012.
Google Scholar
Authors
Ewa DaniszewskaDepartment of Geotechnics and Road Construction; The Faculty of Technical Sciences; University of Warmia and Mazury in Olsztyn Poland
Statistics
Abstract views: 174PDF downloads: 102
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Budownictwo i Architektura supports the open science program. The journal enables Open Access to their publications. Everyone can view, download and forward articles, provided that the terms of the license are respected.
Publishing of articles is possible after submitting a signed statement on the transfer of a license to the Journal.