Numerical analysis of tailing dam with calibration based on genetic algorithm and geotechnical monitoring data

• Autorzy: Grosel Szczepan

Identyfikatory

DOI

10.2478/sgem-2020-0008

• Języki publikacji: EN

Abstrakty

EN

The article presents a method of calibration of material parameters of a numerical model based on a genetic algorithm, which allows to match the calculation results with measurements from the geotechnical monitoring network. This method can be used for the maintenance of objects managed by the observation method, which requires continuous monitoring and design alterations. The correctness of the calibration method has been verified on the basis of artificially generated data in order to eliminate inaccuracies related to approximations resulting from the numerical model generation. Using the example of the tailing dam model the quality of prediction of the selected measurement points was verified. Moreover, changes of factor of safety values, which is an important indicator for designing this type of construction, were analyzed. It was decided to exploit the case of dam of reservoir, which is under continuous construction, that is dam height is increasing constantly, because in this situation the use of the observation method is relevant.

Słowa kluczowe

EN

soil parameters; optimization; slope stability; genetic algorithm; observational method; monitoring

• Wydawca: De Gruyter
• Czasopismo: Studia Geotechnica et Mechanica
• Rocznik: 2021
• Tom: Vol. 43, nr 1
• Strony: 34--47
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Grosel Szczepan

• Wroclaw University of Science and Technology, Wroclaw, Poland

Bibliografia

• [1] Cała M, Flisiak J. Slope stability analysis with numerical and limit equilibrium methods. Burczynski, Fedelinski i Majchrzak (eds) Computermethods in mechanics, CMM-2003.
• [2] Gioda G, Sakurai S. Back analysis procedures for the interpretation of field measurements in geomechanics. International Journal for Numerical and Analytical Methods in Geomechanics, 1987, 11(6), 555–583. doi:10.1002/ nag.1610110604
• [3] Holland J. Adaptation In Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975.
• [4] Hunter JD. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9,2007, 90-95.
• [5] Jamiolkowski M. Soil mechanics and the observational method: challenges at the Zelazny Most copper tailings disposal facility. Géotechnique, 2014, 64(8), 590–618. doi:10.1680/geot.14.rl.002.
• [6] McKinney W. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 2010, 51-56.
• [7] Oliphant TE. A guide to NumPy, USA: Trelgol Publishing 2006.
• [8] Papon A., Riou Y., Dano C., Hicher PY. Single-and multi-objective genetic algorithm optimization for identifying soil parameters. International Journal for Numerical and Analytical Methods in Geomechanics, 2011, 36(5), 597–618. doi:10.1002/nag.1019.
• [9] Peck RB. Advantages and limitations of the observational method in applied soil mechanics. Geotechnique 1969, 19, No. 2, 171-187.
• [10] Rokonuzzaman M., Sakai T. Calibration of the parameters for a hardening–softening constitutive model using genetic algorithms. Computers and Geotechnics, 2010, 37(4), 573–579. doi:10.1016/j.compgeo.2010.02.007.
• [11] Simo, J. C, Rifai, M. S. A class of mixed assumed strain methods and the method of incompatible modes. International Journal for Numerical Methods in Engineering, 1990, 29(8), 1595–1638. doi:10.1002/nme.1620290802
• [12] Vahdati P, Levasseur S, Mattsson H, Knutsson S. Inverse Mohr-Coulomb soil parameter identification of an earth and rockfill dam by genetic algorithm optimization. Electronic Journal of Geotechnical Engineering, 2013, 18. 5419-5440.
• [13] Vahdati P, Levasseur S, Mattsson H, Knutsson S. Inverse hardening soil parameter identification of an earth and rockfill dam by genetic algorithm optimization. Electronic Journal of Geotechnical Engineering, 2014, 19. 3327-3349.
• [14] Van Genuchten M. A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Science Society of America Journal, 1980, 44.
• [15] Van Rossum G, Drake FL. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace, 2009.
• [16] Whitley, D. A Genetic Algorithm Tutorial. Statistics and Computing. 1998
• [17] Whitley D, Starkweather T. Genitor II: a distributed genetic algorithm. Journal of Experimental and Theoretical Artificial Intelligence, 1990, 6, 367-388
• [18] Zentar R, Hicher PY, Moulin G. Identification of soil parameters by inverse analysis. Computers and Geotechnics, 2001, 28(2), 129–144. doi:10.1016/s0266-352x(00)00020-3.
• [19] Zimmermann T, Truty A, Urbański A, Podleś K. ZSoil user manual. Zace Services, Switzerland 2016.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).