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Open-pit overburden dump characterization using digital image processing technique

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The safety of the open-pit overburden dump slope largely depends on the geomaterial size and shape. The shape of these geomaterials contributes to their shear resistance against sliding. The present investigation proposed a method to characterize the geomaterial using the digital image processing technique. The resources invested in this work are a simple digital camera and a computational toolbox. The system estimates the size distribution of geomaterial. The study also proposed a methodology for recon-structing the 3D geometry of the mine dump from the images. The advantage of the method is a low-cost, quick assessment of the dump geomaterial, and outcomes can easily be used in a numerical toolbox. The study was conducted in Barakar Valley Coalfields, West Bengal, India. The geomaterials above 4 mm sizes are considered in this work. The results matched the mechanical sieving output of the particle size distribution curve.
Czasopismo
Rocznik
Tom
Strony
81--101
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
  • Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India
  • Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India
autor
  • Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India
Bibliografia
  • ABRÀMOFF M.D., MAGALHÃES P.J., and RAM S.J., 2004, Image processing with ImageJ, Biophotonics International, Vol. 11, No. 7, pp. 36–42.
  • AKBULUT S., ARASAN S., and HASILOGLU A.S., 2011, Effect of particle size and shape on the grain- -size distribution using image analysis, International Journal of Civil and Structural Engineering, Vol. 1, No. 4, pp. 968–985, doi: 10.6088/ijcser.00202010083.
  • ATTENE M. and SPAGNUOLO M., 2000, Automatic surface reconstruction from point sets in space, Computer Graphics Forum, Vol. 19, No. 3, pp. 457–465.
  • BAY H., TUYTELAARS T., and VAN GOOL L., 2006, SURF: Speeded Up Robust Features, ECCV 2006, Lecture Notes in Computer Science, Vol. 3951, pp. 404–417.
  • BERGER M. et al., 2014, State of the Art in Surface Reconstruction from Point Clouds, Annual Conference of the European Association for Computer Graphics, Eurographics.
  • CHAND K. and KONER R., 2024, Mine Active Internal Dump Susceptible Zone Identification using MMO Technique, Journal of Mines, Metals and Fuels, pp. 165–177, Apr., doi: 10.18311/jmmf/2024/35719.
  • CIGNONI P., CALLIERI M., CORSINI M., DELLEPIANE M., GANOVELLI F., and RANZUGLIA G., 2008, MeshLab: an Open-Source Mesh Processing Tool, Eurographics Italian chapter conference, pp. 129–136.
  • FERNLUND J.M.R., 2005a, Image analysis method for determining 3-D size distribution of coarse aggregates, Bulletin of Engineering Geology and the Environment, Vol. 64, No. 2, pp. 159–166, Jun., doi: 10.1007/s10064-004-0251-8.
  • FERNLUND J.M.R., 2005b, Image analysis method for determining 3-D shape of coarse aggregate, Cem. Concr. Res., Vol. 35, No. 8, pp. 1629–1637, Aug., doi: 10.1016/j.cemconres.2004.11.017.
  • FERNLUND J.M.R., 2005c, 3-D image analysis size and shape method applied to the evaluation of the Los Angeles test, Eng. Geol., Vol. 77, No. 1–2, pp. 57–67, Feb., doi: 10.1016/j.enggeo.2004.08.002.
  • FISCHLER M.A. and BOLLES R.C., 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM, Vol. 24, No. 6, pp. 381–395.
  • GEE G.W. and OR D., 2002, Particle-Size Analysis, [in:] Methods of soil analysis: Part 4. Physical methods, Vol. 5, pp. 255–293.
  • GHALIB A.M. and HRYCIW R.D., 1999, Soil particle size distribution by mosaic imaging and water-shed analysis, Journal of Computing in Civil Engineering, Vol. 13, No. 2, pp. 80–87.
  • HUNT B.R., LIPSMAN R.L., and ROSENBERG J.M., 2001, A Guide to MATLAB, doi: 10.1017/ cbo9781139164801.
  • JIE XU W., QI YUE Z., and LIN HU R., 2008, Study on the mesostructure and mesomechanical characteristics of the soil-rock mixture using digital image processing based finite element method, International Journal of Rock Mechanics and Mining Sciences, Vol. 45, No. 5, pp. 749–762, doi: 10.1016/ j.ijrmms.2007.09.003.
  • KARAKUS D., ONUR A.H., DELIORMANLI A.H., and KONAK G., 2010, Size and shape analysis of mineral particles using image processing technique, Journal of Ore Dressing, Vol. 12, No. 23.
  • KAZHDAN M. and HOPPE H., 2013, Screened poisson surface reconstruction, ACM Trans. Graph., Vol. 32, No. 3, Jun., doi: 10.1145/2487228.2487237.
  • KAZHDAN M., BOLITHO M., and HOPPE H., 2006, Poisson Surface Reconstruction, [in:] K. Polthier, A. Sheffer (Eds.), Eurographics Symposium on Geometry Processing.
  • KONER R. and CHAKRAVARTY D., 2016, Characterisation of overburden dump materials: a case study from the Wardha valley coal field, Bulletin of Engineering Geology and the Environment, Vol. 75, No. 3, pp. 1311–1323, Aug., doi: 10.1007/s10064-015-0830-x.
  • KONER R., 2021, Estimation of Optimum Geometric Configuration of Mine Dumps in Wardha Valley Coalfields in India: A Case Study, Journal of Mining and Environment, Vol. 12, No. 4, pp. 907–927, Sep., doi: 10.22044/jme.2021.10979.2074.
  • KWAN A.K.H., MORA C.F., and CHAN H.C., 1999, Particle shape analysis of coarse aggregate using digital image processing, Cem. Concr. Res., Vol. 29, No. 9, pp. 1403–1410.
  • LIRA C. and PINA P., 2007, Sedimentological analysis of sands, [in:] Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer-Verlag, pp. 388–396. doi: 10.1007/978-3-540-72849-8_49.
  • LU F., ZHOU X., and HE Y.B., 1988, Image Segmentation Technique Used in Estimation of the Size Distribution of Rock Fragments in Mining, MVA, pp. 351–354.
  • MAERZ N.H. and LUSHER M., 2001, Measurement of flat and elongation of coarse aggregate using digital image processing, [in:] 80th Annual Meeting, Transportation Research Board, Washington DC, pp. 2–14. [Online]. Available: https://www.researchgate.net/publication/242107361
  • MAERZ N.H., 2004, Technical and Computational Aspects of the Measurement of Aggregate Shape by Digital Image Analysis, Journal of Computing in Civil Engineering, Vol. 18, No. 1, pp. 10–18, doi: 10.1061/ASCE0887-3801200418:110.
  • MIR B.A. and ASHRAF S., 2019, Evaluation of load-settlement behaviour of square model footings resting on geogrid reinforced granular soils, [in:] Advanced Research on Shallow Foundations, pp. 103–126, doi: 10.1007/978-3-030-01923-5.
  • MISHRA A., DAS S.K., and REDDY K.R., 2024, Potential Use of Coal Mine Overburden Waste Rock as Sustainable Geomaterial: Review of Properties and Research Challenges, J. Hazard Toxic Radio-act. Waste, Vol. 28, No. 1, Jan., doi: 10.1061/jhtrbp.hzeng-1258.
  • MORA C.F. and KWAN A.K.H., 2000, Sphericity, shape factor, and convexity measurement of coarse aggregate for concrete using digital image processing, Cem. Concr. Res., Vol. 30, Issue 3, 351–358.
  • MORA C.F., KWAN A.K.H., and CHAN H.C., 1998, Particle size distribution analysis of coarse aggregate using digital image processing, Cem. Concr. Res., Vol. 28, No. 6, pp. 921–932.
  • NEUBECK A. and VAN GOOL L., 2006, Efficient Non-Maximum Suppression, ETH Zurich.
  • RUBIN D.M., 2004, A Simple Autocorrelation Algorithm for Determining Grain Size from Digital Images of Sediment,” JOURNAL OF SEDIMENTARY RESEARCH, vol. 74, no. 1, pp. 160–165, [Online]. Avail-able: http://pubs.geoscienceworld.org/sepm/jsedres/article-pdf/74/1/160/2819088/160.pdf
  • SERESHKI F., HOSEINI S.M., and ATAEI M., 2016, Blast fragmentation analysis using image pro-cessing, International Journal of Mining and Geo-Engineering, Vol. 50, No. 2, pp. 211–218, doi: 10.22059/ijmge.2016.59831.
  • TAYLOR M.A., 2002, Quantitative measures for shape and size of particles, Powder Technol., Vol. 124, No. 1–2, pp. 94–100. [Online]. Available: www.elsevier.com/locate/powtec
  • TRIGGS B., MCLAUCHLAN P., HARTLEY R., and FITZGIBBON A., 2000, Bundle Adjustment – A Mod-ern Synthesis, Vision Algorithms: Theory and Practice: International Workshop on Vision Algorithms Corfu, Greece, pp. 298–372, doi: 10.1007/3-540-44480-7_21ï.
  • TUTUMLUER E., PAN T., and CARPENTER S.H., 2005. Investigation of aggregate shape effects on hot mix performance using an image analysis approach, UILU-ENG. [Online]. Available: http://www.pooledfund.org
  • VANGLA P., ROY N., MENDU K., and LATHA G.M., 2014, Digital Image Analysis for the Determination of Size and Shape Parameters of Sand Grains, [in:] Golden Jubilee Conference of the IGS Bangalore Chapter, Geo Innovations, Bangalore India. [Online]. Available: https://www.researchgate.net/ publication/273261114
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-04bb286b-ebc6-4250-b08a-39bcdc1be8a3
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