آمار

تعداد نشریات8
تعداد شماره‌ها423
تعداد مقالات5,540
تعداد مشاهده مقاله6,444,010
تعداد دریافت فایل اصل مقاله5,523,640
Mousavi, Maryam, Jiryaei Sharahi, Morteza. (1400). Estimating the Sand Shear Strength from Its Grain Characteristics Using an Artificial Neural Network Model and Multiple Regression Analysis. نشریه مهندسی عمران امیرکبیر, 5(3), 403-420. doi: 10.22060/ajce.2021.19213.5721
Maryam Mousavi; Morteza Jiryaei Sharahi. "Estimating the Sand Shear Strength from Its Grain Characteristics Using an Artificial Neural Network Model and Multiple Regression Analysis". نشریه مهندسی عمران امیرکبیر, 5, 3, 1400, 403-420. doi: 10.22060/ajce.2021.19213.5721
Mousavi, Maryam, Jiryaei Sharahi, Morteza. (1400). 'Estimating the Sand Shear Strength from Its Grain Characteristics Using an Artificial Neural Network Model and Multiple Regression Analysis', نشریه مهندسی عمران امیرکبیر, 5(3), pp. 403-420. doi: 10.22060/ajce.2021.19213.5721
Mousavi, Maryam, Jiryaei Sharahi, Morteza. Estimating the Sand Shear Strength from Its Grain Characteristics Using an Artificial Neural Network Model and Multiple Regression Analysis. نشریه مهندسی عمران امیرکبیر, 1400; 5(3): 403-420. doi: 10.22060/ajce.2021.19213.5721

Estimating the Sand Shear Strength from Its Grain Characteristics Using an Artificial Neural Network Model and Multiple Regression Analysis

AUT Journal of Civil Engineering
مقاله 4، دوره 5، شماره 3، آذر 2021، صفحه 403-420    اصل مقاله (1.62 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/ajce.2021.19213.5721
نویسندگان
Maryam Mousavi؛ Morteza Jiryaei Sharahi*
Civil Engineering Department, Qom University of Technology, Iran
چکیده
Determination of soil shear strength is always among the most important issues in geotechnical problems. In this research, various neural network models and multiple regression are developed to obtain shear strength parameter of the sandy soil from physical parameters of roundness (R), maximum and minimum dry densities (γdmax, γdmin), relative density (Dr), and grain sizes, D10, D30, D50, and D60. Firstly, the effect of these physical parameters on the shear strength of sands is examined by soil laboratory tests. For this purpose, laboratory tests of the direct shear, maximum and minimum dry densities, and sieve analysis are conducted. Subsequently, the laboratory results are used as a data set to develop an artificial neural network and multiple regression models to predict shear strength parameters. Finally, the efficiency and appropriateness of each approach are discussed. Results showed that both neural network and regression are precise, appropriate, and inexpensive methods to predict soil shear strength parameters.
کلیدواژه‌ها
Neural network؛ Multiple regression؛ Sand؛ Shear strength؛ Grain characteristics
مراجع
  1. Kolbuszewski, M.R. Frederick, The significance of particle shape and size on the mechanical behavior of granular materials, European Conference on Soil Mechanics and Foundation Engineering (Wiesbaden) Sec.4 Paper 9, 1963.
  2. Zolkov, G. Wiseman, Engineering Properties of Dune and Beach Sands and the Influence of Stress History, Proc. of Sixth Int. Conf. on SMFE, vol. I, 1965.
  3. A. Charles, K.S. Watts, The influence of confining pressure on the shear strength of compacted rockfill, Géotechnique, 30(4) (1980) 353-367.
  4. Nakao, S. Fityus, Direct shear testing of a marginal material using a large shear box, Geotechnical Testing Journal, 31(5) (2008) 1-11.
  5. N. Islam, A. Siddika, M.B. Hossain, A. Rahman, M.A. Asad, Effect of Particle Size on the Shear Strength Behavior of Granular Materials, Journal of Australian Geomechanics, 46 (3) (2011) 75-86.
  6. M. Kara, M. Meghachou, N. Aboubekr, Contribution of particle size ranges to sand fraction, ETASR- Engineering Technology & Applied Science Research, 3(4) (2013) 497-501.
  7. Wang, H.-P. Zhang, S.-C. Tang, Y. Liang, Effects of Particle Size Distribution on Shear Strength of Accumulation Soil, Journal of Geotechnical and Geoenvironmental Engineering, 139(11) (2013) 1994-1997.
  8. M. Kirkpatric, Effects of Grain Size and Grading on the Shearing Behaviour of Granular Materials, Proc. 6th Int. Conf. Soil. Mech. and Foundation Engineering, Canada, Vol. I, 1965.
  9. J. Marsal, Mechanical properties of rockfill, Embankment-Dam Engineering, R.C. Hirschfeld and S. J. Poulos, Eds. A Wiley Interscience Publication, 1973.
  10. D. Marschi, C.K. Chan, H.B. Seed, Evaluation of Properties of Rockfill Materials, Journal of the Soil Mechanics and Foundations Division, 98(1) (1972) 95-114.
  11. Zelasko, R.J. Krizek, T.B. Edil, Shear behavior of sand as a function of grain characteristics, Proc. Conference on Soil Mechanics and Foundation Engineering, Istanbul, 1975.
  12. Vangla, G.M. Latha, Influence of Particle Size on the Friction and Interfacial Shear Strength of Sands of Similar Morphology, International Journal of Geosynthetics and Ground Engineering, 1(1) (2015) 6.
  13. M. Koerner, Effect of Particle Characteristics on Soil Strength, Journal of the Soil Mechanics and Foundations Division, 96(4) (1970) 1221-1234.
  14. Shang, L. Sun, S. Li, X. Liu, W. Chen, Experimental study of the shear strength of carbonate gravel, Bulletin of Engineering Geology and the Environment, 79(5) (2020) 2381-2394.
  15. Alias, A. Kasa, M.R. Taha, Particle size effect on shear strength of granular materials in direct shear test, International Journal of Civil and Environmental Engineering, 8(11) (2014) 1144-1147.
  16. Zhang, P. Tahmasebi, Effects of Grain Size on Deformation in Porous Media, Transport in Porous Media, 129(1) (2019) 321-341.
  17. Boudia, A. Berga, Effect of Grain Size and Distribution on Mechanical Behavior of Dune Sand, Civil Engineering Journal, 7(8) (2021) 1355-1377.
  18. -C. Cho, J. Dodds, J.C. Santamarina, Particle Shape Effects on Packing Density, Stiffness, and Strength: Natural and Crushed Sands, Journal of Geotechnical and Geoenvironmental Engineering, 132(5) (2006) 591-602.
  19. A. Bareither, T.B. Edil, C.H. Benson, D.M. Mickelson, Geological and Physical Factors Affecting the Friction Angle of Compacted Sands, Journal of Geotechnical and Geoenvironmental Engineering, 134(10) (2008) 1476-1489.
  20. S. Zelasko, An investigation of the influences of particle size, size gradation and particle shape of the shear strength and packing behavior of quartziferous sands, Ph.D. thesis, Northwestern Univ., Evanston, Ill, 1966.
  21. B. Edil, R.J. Krizek, J.S. Zelasko, Effect of grain characteristics on packing of sands, in: Proceedings of Istanbul Conf. on Soil Mechanics and Foundation Engineering, Balkema, Rotterdam, 1975, pp.46– 54.
  22. H. Juang, P.C. Lu, Predicting Geotechnical Parameters of Sands from CPT Measurements Using Neural Networks, Computer-Aided Civil and Infrastructure Engineering, 17 (2002) 31–42.
  23. W. Ellis, C. Yao, R. Zhao, D. Penumadu, Stress-Strain Modeling of Sands Using Artificial Neural Networks, Journal of Geotechnical Engineering, 121(5) (1995) 429-435.
  24. Ghaboussi, D.E. Sidarta, New nested adaptive neural networks (NANN) for constitutive modeling, Computers and Geotechnics, 22(1) (1998) 29-52.
  25. -H. Zhu, M.M. Zaman, S.A. Anderson, Modeling of soil behavior with a recurrent neural network, Canadian Geotechnical Journal, 35(5) (1998) 858-872.
  26. C. Krumbein, Measurement and geological significance of shape and roundness of sedimentary particles, Journal of Sedimentary Research, 11(2) (1941) 64-72.
  27. ASTM D2487-11, Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System), ASTM International, West Conshohocken, PA, 2011.
  28. ASTM D4253-16, Standard Test Methods for Maximum Index Density and Unit Weight of Soils Using a Vibratory Table, ASTM International, West Conshohocken, PA, 2016.
  29. ASTM D4254-16, Standard Test Methods for Minimum Index Density and Unit Weight of Soils and Calculation of Relative Density, ASTM International, West Conshohocken, PA, 2016.
  30. ASTM D3080 / D3080M-11, Standard Test Method for Direct Shear Test of Soils Under Consolidated Drained Conditions, ASTM International, West Conshohocken, PA, 2011.
  31. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, in: IEEE International Conference on Neural Networks, 1993, pp. 586-591 vol.581
  32. T. Hagan, M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5(6) (1994) 989–993.
  33. J.C. MacKay, A Practical Bayesian Framework for Backpropagation Networks, Neural Computation, 4(3) (1992) 448-472.
  34. D. Foresee, M.T. Hagan, Gauss-Newton approximation to Bayesian learning, in: Proceedings of International Conference on Neural Networks (ICNN'97), 1997, pp. 1930-1935 vol.1933.
  35. F. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6(4) (1993) 525-533.
  36. Gander, M.J. Gander, Scientific Computing - An Introduction using Maple and MATLAB, Texts in Computational Science and Engineering, 2014.
  37. A.F. Seber, C.J. Wild, Nonlinear regression, Hoboken, NJ: Wiley-Interscience, 2003.                                                                    
آمار
تعداد مشاهده مقاله: 393
تعداد دریافت فایل اصل مقاله: 593


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب