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Clone granular soils with mixed particle morphological characteristics by integrating spherical harmonics with Gaussian mixture model, expectation–maximization, and Dirichlet process

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Abstract

Computers have been taught to clone granular soil particles for discrete element method simulations to alleviate difficulties of using three-dimensional imaging techniques for scanning a large number of particles. In this process, computers analyze a few scanned particles to extract morphological characteristics of the target soil, which are used to clone as many particles as necessary. However, many natural granular soils contain a wide range of particle shapes mixing more than one type of morphological characteristics, causing difficulties in cloning. This research aims to address this challenge by integrating spherical harmonics with Gaussian mixture model, expectation–maximization, and Dirichlet process. Spherical harmonics coefficients are used to characterize morphological information of the granular soil. Gaussian mixture model is used to fit a function to the mixed morphological characteristics. The expectation–maximization and Dirichlet process are used to estimate the fitting parameters in Gaussian mixture model. Then, Gaussian mixture model is used to generate new spherical harmonics coefficients and then generate new particles. The effectiveness and accuracy of the proposed methodology are verified using a Griffin sand. Although this approach is developed for granular soils, the proposed technique can also be used to clone other particulate materials.

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References

  1. Alshibli KA, Alsaleh MI (2004) Characterizing surface roughness and shape of sands using digital microscopy. J Comput Civ Eng 18:36–45. https://doi.org/10.1061/~ASCE!0887-3801~2004!18:1~36!

    Article  Google Scholar 

  2. Alshibli KA, Cil MB (2018) Influence of particle morphology on the friction and dilatancy of sand. J Geotech Geoenviron Eng 144:04017118. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001841

    Article  Google Scholar 

  3. Altuhafi FN, Coop MR, Georgiannou VN (2016) Effect of particle shape on the mechanical properties of natural sands. J Geotech Geoenviron Eng 142:1–15. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001569

    Article  Google Scholar 

  4. Amendola C, Faugere JC, Sturmfels B (2016) Moment varieties of gaussian mixtures. J Algebr Stat 7:14–28. https://doi.org/10.18409/jas.v7i1.42

    Article  MathSciNet  MATH  Google Scholar 

  5. Anochie-Boateng JK, Komba JJ, Mvelase GM (2013) Three-dimensional laser scanning technique to quantify aggregate and ballast shape properties. Constr Build Mater 43:389–398. https://doi.org/10.1016/j.conbuildmat.2013.02.062

    Article  Google Scholar 

  6. Aschenbrenner BC (1956) A new method of expressing particle sphericity. J Sediment Res 26:15–31. https://doi.org/10.1306/74D704A7-2B21-11D7-8648000102C1865D

    Article  Google Scholar 

  7. Bareither CA, Edil TB, Benson CH, Mickelson DM (2008) Geological and physical factors affecting the friction angle of compacted sands. J Geotech Geoenviron Eng 134:1476–1489. https://doi.org/10.1061/(asce)1090-0241(2008)134:10(1476)

    Article  Google Scholar 

  8. Biernacki C, Celeux G, Govaert G (2003) Choosing starting values for the EM algorithm for getting the highest likehood in multivariate Gaussian mixture models. Comput Stat Data Anal 41:561–575. https://doi.org/10.1016/S0167-9473(02)00163-9

    Article  MATH  Google Scholar 

  9. Cavarretta I, O’Sullivan C, Coop MR (2010) The influence of particle characteristics on the behaviour of coarse grained soils. Geotechnique 60:413–423. https://doi.org/10.1680/geot.2010.60.6.413

    Article  Google Scholar 

  10. Cho G-C, Dodds J, Santamarina JC (2006) Particle shape effects on packing density, stiffness, and strength: natural and crushed sands. J Geotech Geoenviron Eng 132:591–602. https://doi.org/10.1061/(asce)1090-0241(2006)132:5(591)

    Article  Google Scholar 

  11. Cil MB, Alshibli KA, Kenesei P (2017) 3D experimental measurement of lattice strain and fracture behavior of sand particles using synchrotron X-ray diffraction and tomography. J Geotech Geoenvirom Eng 143:1–18. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001737

    Article  Google Scholar 

  12. Druckrey AM, Alshibli KA, Al-Raoush RI (2016) 3D characterization of sand particle-to-particle contact and morphology. Comput Geotech 74:26–35. https://doi.org/10.1016/j.compgeo.2015.12.014

    Article  Google Scholar 

  13. Galindo-Torres SA, Muñoz JD, Alonso-Marroquín F (2010) Minkowski-Voronoi diagrams as a method to generate random packings of spheropolygons for the simulation of soils. Phys Rev E Stat Nonlinear Soft Matter Phys 82:1–12. https://doi.org/10.1103/PhysRevE.82.056713

    Article  Google Scholar 

  14. Galindo-Torres SA, Pedroso DM, Muñoz JD, Alonso-Marroquín F (2010) Molecular dynamics simulations of complex-shaped particles using Voronoi-based spheropolyhedra. Phys Rev E. https://doi.org/10.1103/physreve.81.061303

    Article  Google Scholar 

  15. Grigoriu M, Garboczi E, Kafali C (2006) Spherical harmonic-based random fields for aggregates used in concrete. Powder Technol 166:123–138. https://doi.org/10.1016/j.powtec.2006.03.026

    Article  Google Scholar 

  16. Guida G, Viggiani GMB, Casini F (2009) Multi-scale morphological descriptors from the fractal analysis of particle contour. Acta Geotech. https://doi.org/10.1007/s11440-019-00772-3

    Article  Google Scholar 

  17. Hayakawa Y, Oguchi T (2005) Evaluation of gravel sphericity and roundness based on surface-area measurement with a laser scanner. Comput Geosci 31:735–741. https://doi.org/10.1016/j.cageo.2005.01.004

    Article  Google Scholar 

  18. Hryciw RD, Zheng J, Shetler K (2016) Particle roundness and sphericity from images of assemblies by chart estimates and computer methods. J Geotech Geoenviron Eng. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001485

    Article  Google Scholar 

  19. Jerves AX, Kawamoto RY, Andrade JE (2016) Effects of grain morphology on critical state: a computational analysis. Acta Geotech 11:493–503. https://doi.org/10.1007/s11440-015-0422-8

    Article  Google Scholar 

  20. Kandasami R, Murthy T (2014) Effect of particle shape on the mechanical response of a granular ensemble. 3rd International symposium on geomechanics from micro to macro. Univ Cambridge, Cambridge, pp 1093–1098

    Chapter  Google Scholar 

  21. Kim H, Haas CT, Rauch AF, Browne C (2002) Dimensional ratios for stone aggregates from three-dimensional laser scans. J Comput Civ Eng 16:175–183. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(175)

    Article  Google Scholar 

  22. Krumbein WC, Sloss LL (1951) Stratigraphy and sedimentation. W.H Freeman and Company, San Francisco

    Book  Google Scholar 

  23. Kuo C-Y, Freeman R (2000) Imaging indices for quantification of shape, angularity, and surface texture of aggregates. Transp Res Rec J Transp Res Board 1721:57–65. https://doi.org/10.3141/1721-07

    Article  Google Scholar 

  24. Lai Z, Chen Q (2019) Reconstructing granular particles from X-ray computed tomography using the TWS machine learning tool and the level set method. Acta Geotech 14:1–18. https://doi.org/10.1007/s11440-018-0759-x

    Article  Google Scholar 

  25. Li C, Zheng J, Zhang Z et al (2020) Morphology-based indices and recommended sampling sizes for using image-based methods to quantify degradations of compacted aggregate materials. Constr Build Mater 230:116970. https://doi.org/10.1016/j.conbuildmat.2019.116970

    Article  Google Scholar 

  26. Liu X, Garboczi EJ, Grigoriu M et al (2011) Spherical harmonic-based random fields based on real particle 3D data: improved numerical algorithm and quantitative comparison to real particles. Powder Technol 207:78–86. https://doi.org/10.1016/j.powtec.2010.10.012

    Article  Google Scholar 

  27. Liu X, Yang J (2018) Shear wave velocity in sand: effect of grain shape. Géotechnique 68:742–748. https://doi.org/10.1680/jgeot.17.t.011

    Article  Google Scholar 

  28. Melnykov V, Melnykov I (2012) Initializing the em algorithm in Gaussian mixture models with an unknown number of components. Comput Stat Data Anal 56:1381–1395. https://doi.org/10.1016/j.csda.2011.11.002

    Article  MathSciNet  MATH  Google Scholar 

  29. Mollon G, Zhao J (2012) Fourier–Voronoi-based generation of realistic samples for discrete modelling of granular materials. Granul Matter 14:621–638. https://doi.org/10.1007/s10035-012-0356-x

    Article  Google Scholar 

  30. Mollon G, Zhao J (2013) Generating realistic 3D sand particles using Fourier descriptors. Granul Matter 15:95–108. https://doi.org/10.1007/s10035-012-0380-x

    Article  Google Scholar 

  31. Mollon G, Zhao J (2014) 3D generation of realistic granular samples based on random fields theory and Fourier shape descriptors. Comput Methods Appl Mech Eng 279:46–65. https://doi.org/10.1016/j.cma.2014.06.022

    Article  MATH  Google Scholar 

  32. Mora CF, Kwan AKH (2000) Sphericity, shape factor, and convexity measurement of coarse aggregate for concrete using digital image processing. Cem Concr Res 30:351–358. https://doi.org/10.1016/S0008-8846(99)00259-8

    Article  Google Scholar 

  33. Müller C (2006) Spherical harmonics. Springer, Berlin

    MATH  Google Scholar 

  34. Nie Z, Liang Z, Wang X (2018) A three-dimensional particle roundness evaluation method. Granul Matter 20:1–11. https://doi.org/10.1007/s10035-018-0802-5

    Article  Google Scholar 

  35. Nouguier-Lehon C, Cambou B, Vincens E (2003) Influence of particle shape and angularity on the behaviour of granular materials: a numerical analysis. Int J Numer Anal Methods Geomech 27:1207–1226. https://doi.org/10.1002/nag.314

    Article  MATH  Google Scholar 

  36. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  37. Otsubo M, O’Sullivan C, Sim WW, Ibraim E (2015) Quantitative assessment of the influence of surface roughness on soil stiffness. Géotechnique 65:694–700. https://doi.org/10.1680/geot.14.T.028

    Article  Google Scholar 

  38. Pernkopf F, Bouchaffra D (2005) Genetic-based EM algorithm for learning Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 27:1344–1348. https://doi.org/10.1109/TPAMI.2005.162

    Article  Google Scholar 

  39. Quevedo R, Carlos LG, Aguilera JM, Cadoche L (2002) Description of food surfaces and microstructural changes using fractal image texture analysis. J Food Eng 53:361–371. https://doi.org/10.1016/S0260-8774(01)00177-7

    Article  Google Scholar 

  40. Riley NA (1941) Projection sphericity. SEPM J Sediment Res. https://doi.org/10.1306/d426910c-2b26-11d7-8648000102c1865d

    Article  Google Scholar 

  41. Semnani SJ, Borja RI (2017) Quantifying the heterogeneity of shale through statistical combination of imaging across scales. Acta Geotech 12:1193–1205

    Article  Google Scholar 

  42. Shin H, Santamarina JC (2013) Role of particle angularity on the mechanical behavior of granular mixtures. J Geotech Geoenviron Eng 139:353–355. https://doi.org/10.1061/(asce)gt.1943-5606.0000768

    Article  Google Scholar 

  43. Su D, Yan WM (2018) 3D characterization of general-shape sand particles using microfocus X-ray computed tomography and spherical harmonic functions, and particle regeneration using multivariate random vector. Powder Technol 323:8–23. https://doi.org/10.1016/j.powtec.2017.09.030

    Article  Google Scholar 

  44. Su D, Yan WM (2019) Prediction of 3D size and shape descriptors of irregular granular particles from projected 2D images. Acta Geotech. https://doi.org/10.1007/s11440-019-00845-3

    Article  Google Scholar 

  45. Sun Q, Zheng J (2019) Two-dimensional and three-dimensional inherent fabric in cross-anisotropic granular soils. Comput Geotech 116:103197. https://doi.org/10.1016/j.compgeo.2019.103197

    Article  Google Scholar 

  46. Sun Q, Zheng J (2019) Realistic soil particles generation based on limited morphological information by probability-based spherical harmonics. Comput Part Mech 6:1–21. https://doi.org/10.1007/s40571-020-00325-6

    Article  MathSciNet  Google Scholar 

  47. Sun Q, Zheng J, He H, Li Z (2019) Particulate material fabric characterization from volumetric images by computational geometry. Powder Technol 344:804–813. https://doi.org/10.1016/j.powtec.2018.12.070

    Article  Google Scholar 

  48. Sun Q, Zheng J, Li C (2019) Improved watershed analysis for segmenting contacting particles of coarse granular soils in volumetric images. Powder Technol 356:295–303. https://doi.org/10.1016/j.powtec.2019.08.028

    Article  Google Scholar 

  49. Sun Q, Zheng Y, Li B et al (2019) Three-dimensional particle size and shape characterisation using structural light. Géotechnique Lett 9:72–78

    Article  Google Scholar 

  50. Teh YW (2010) Encyclopedia of machine learning. Springer, Berlin

    Google Scholar 

  51. Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical Dirichlet processes. J Am Stat Assoc 101:1566–1581. https://doi.org/10.1198/016214506000000302

    Article  MathSciNet  MATH  Google Scholar 

  52. Vangla P, Roy N, Gali ML (2017) Image based shape characterization of granular materials and its effect on kinematics of particle motion. Granul Matter. https://doi.org/10.1007/s10035-017-0776-8

    Article  Google Scholar 

  53. Wadell H (1933) Sphericity and roundness of rock particles. J Geol 41:310–331. https://doi.org/10.1086/624040

    Article  Google Scholar 

  54. Wadell H (1932) Volume, shape, and roundness of rock particles. J Geol 40:443–451. https://doi.org/10.1086/623964

    Article  Google Scholar 

  55. Wadell H (1935) Volume, shape, and roundness of quartz particles. J Geol 43:250–280. https://doi.org/10.1086/624298

    Article  Google Scholar 

  56. Wei D, Wang J, Nie J, Zhou B (2018) Generation of realistic sand particles with fractal nature using an improved spherical harmonic analysis. Comput Geotech 104:1–12. https://doi.org/10.1016/j.powtec.2018.02.006

    Article  Google Scholar 

  57. Wei D, Wang J, Zhao B (2018) A simple method for particle shape generation with spherical harmonics. Powder Technol 330:284–291

    Article  Google Scholar 

  58. Zhao B, Wang J (2016) 3D quantitative shape analysis on form, roundness, and compactness with μCT. Powder Technol. https://doi.org/10.1016/j.powtec.2015.12.029

    Article  Google Scholar 

  59. Zhao S, Zhao J (2019) A poly-superellipsoid-based approach on particle morphology for DEM modeling of granular media. Int J Numer Anal Methods Geomech 43:2147–2169

    Article  Google Scholar 

  60. Zheng J, Hryciw RD (2016) Index void ratios of sands from their intrinsic properties. J Geotech Geoenviron Eng 142:1–10. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001575

    Article  Google Scholar 

  61. Zheng J, Hryciw RD (2017) Particulate material fabric characterization by rotational haar wavelet transform. Comput Geotech 88:46–60. https://doi.org/10.1016/j.compgeo.2017.02.021

    Article  Google Scholar 

  62. Zheng J, Hryciw RD (2015) Traditional soil particle sphericity, roundness and surface roughness by computational geometry. Géotechnique. https://doi.org/10.1680/geot.14.P.192

    Article  Google Scholar 

  63. Zheng J, Hryciw RD (2017) An image based clump library for DEM simulations. Granul Matter 19:1–15. https://doi.org/10.1007/s10035-017-0713-x

    Article  Google Scholar 

  64. Zheng J, Hryciw RD (2014) Soil particle size characterization by stereophotography. Geotechnical Special Publication, En, pp 64–73

    Google Scholar 

  65. Zheng J, Hryciw RD (2017) Soil particle size and shape distributions by stereophotography and image analysis. Geotech Test J 40:317–328. https://doi.org/10.1520/GTJ20160165

    Article  Google Scholar 

  66. Zheng J, Hryciw RD (2016) A corner preserving algorithm for realistic DEM soil particle generation. Granul Matter 18:1–18. https://doi.org/10.1007/s10035-016-0679-0

    Article  Google Scholar 

  67. Zheng J, Hryciw RD, Ohm H-S (2014) Three-dimensional translucent segregation Table (3D-TST) test for soil particle size and shape distribution. 3rd International symposium on geomechanics from micro to macro. Univ Cambridge, Cambridge, pp 1037–1042

    Chapter  Google Scholar 

  68. Zheng J, Hryciw RD, Ventola A (2017) Compressibility of sands of various geologic origins at pre-crushing stress levels. Geol Geotech Eng. https://doi.org/10.1007/s10706-017-0225-9

    Article  Google Scholar 

  69. Zheng J, Sun Q, Zheng H, Wei D, Li Z, Gao L (2020) Three-dimensional particle shape characterizations from half particle geometries. Powder Technol 367:122–132. https://doi.org/10.1016/j.powtec.2020.03.046

    Article  Google Scholar 

  70. Zhou B, Wang J (2016) Generation of a realistic 3D sand assembly using X-ray micro-computed tomography and spherical harmonic-based principal component analysis. Int J Numer Anal Methods Geomech 41:93–109. https://doi.org/10.1002/nag.2548

    Article  Google Scholar 

  71. Zhou B, Wang J (2015) Random generation of natural sand assembly using micro x-ray tomography and spherical harmonics. Géotechn Lett 5:6–11. https://doi.org/10.1680/geolett.14.00082

    Article  Google Scholar 

  72. Zhou B, Wang J, Zhao B (2015) Micromorphology characterization and reconstruction of sand particles using micro X-ray tomography and spherical harmonics. Eng Geol 184:126–137. https://doi.org/10.1016/j.enggeo.2014.11.009

    Article  Google Scholar 

  73. Zhou W, Yuan W, Ma G, Chang XL (2016) Combined finite-discrete element method modeling of rockslides. Eng Comput 33(5):1530–1559. https://doi.org/10.1108/EC-04-2015-0082

    Article  Google Scholar 

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Acknowledgements

This material is based upon work supported by the US National Science Foundation under Grant No. CMMI 1917332. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Sun, Q., Zheng, J. Clone granular soils with mixed particle morphological characteristics by integrating spherical harmonics with Gaussian mixture model, expectation–maximization, and Dirichlet process. Acta Geotech. 15, 2779–2796 (2020). https://doi.org/10.1007/s11440-020-00963-3

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