Advertisement

A new quality factor for the building stone industry: a case study of stone blocks, slabs, and tiles

  • Reza Yarahmadi
  • Raheb BagherpourEmail author
  • Sayed-Ghahreman Taherian
  • Luis M. O. Sousa
Original Article

Abstract

In building stones, discontinuities, non-uniformity, and irregular shape are among the problems that reduce the quality of products. Stone blocks, slabs, and tiles are the main products of quarries and stone processing plants. Quantifying the quality is a key factor in valuation of these products. This paper proposes a quality factor formula to determine the quality of stone blocks (three-dimensional) and products from stone processing plants (two-dimensional). This factor, which shows the uniformity and esthetic index of a piece of stone, is determined based on the shape quality (α), number, size, and esthetic quality (β) of the pieces and the discontinuity factor (γ) that separates the pieces. The quality factor is defined in a way that each previously mentioned characteristic of a piece is involved in the quality factor formula calculation. The quality factor ranges between 0 and 1 and can be expressed as a percentage. The proposed quality factor was calculated for a number of different stone blocks, slabs, and tiles to examine the validity of the quality factor formula. The results showed that the shape quality is the most significant parameter in determining the quality of stone blocks in quarries. Moreover, in processing plant products the esthetic quality and the number of stone pieces in a product are the key factors for determination of their quality. The discontinuity factor is a parameter that reduces the quality of stone products significantly. Furthermore, the quality factor formula can be used as a convenient tool for classifying the products of quarries and stone processing plants.

Keywords

Building stone Quality factor Block Tile Slab 

References

  1. Akkoyun O, Toprak ZF (2012) Fuzzy-based quality classification model for natural building stone blocks. Eng Geol 133:66–75CrossRefGoogle Scholar
  2. Álvarez-Fernández MI, González-Nicieza C, Álvarez-Vigil AE, Alejano L (2012) Geometrical design of ornamental stone slabs cutting using the neutral region concept. Int J Rock Mech Min Sci 52:31–39CrossRefGoogle Scholar
  3. Ar I, Akgul YS (2008) A generic system for the classification of marble tiles using Gabor filters. In: Computer and information sciences, 2008. ISCIS’08. 23rd International Symposium on. IEEE, pp 1–6Google Scholar
  4. Ashmole I, Motloung M (2008) Dimension stone: the latest trends in exploration and production technology. In: Proceedings of the International Conference on Surface Mining, pp 5–8Google Scholar
  5. Carvalho JF, Henriques P, Falé P, Luís G (2008) Decision criteria for the exploration of ornamental-stone deposits: application to the marbles of the Portuguese Estremoz Anticline. Int J Rock Mech Min Sci 45(8):1306–1319CrossRefGoogle Scholar
  6. Deviren M, Balci MK, Leloglu UM, Severcan M (2000) A feature extraction method for marble tile classification. In: International conference on computer vision, pattern recognition and image processing, Atlantic City, NJ, pp 25–28Google Scholar
  7. Egesi N, Tse C (2011) Dimension stone: exploration, evaluation and exploitation in southwest parts of Oban Massif Southeastern Nigeria. J Geol Min Res 3(4):114–122Google Scholar
  8. Elmouttie M, Krähenbühl G, Poropat G (2013) Robust algorithms for polyhedral modelling of fractured rock mass structure. Comput Geotech 53:83–94CrossRefGoogle Scholar
  9. Fernández-de Arriba M, Díaz-Fernández ME, González-Nicieza C, Álvarez-Fernández MI, Álvarez-Vigil AE (2013) A computational algorithm for rock cutting optimisation from primary blocks. Comput Geotech 50:29–40CrossRefGoogle Scholar
  10. Fort R, de Buergo MA, Perez-Monserrat E, Varas MJ (2010) Characterisation of monzogranitic batholiths as a supply source for heritage construction in the northwest of Madrid. Eng Geol 115(3):149–157CrossRefGoogle Scholar
  11. Goodman RE, Shi G-H (1985) Block theory and its application to rock engineering. Prentice-Hall, Englewood Cliffs, pp 9–98Google Scholar
  12. Jing L, Stephansson O (2007) Fundamentals of discrete element methods for rock engineering: theory and applications. Elsevier, Amsterdam, pp 199–232CrossRefGoogle Scholar
  13. Latham J-P, Van Meulen J, Dupray S (2006) Prediction of in situ block size distributions with reference to armourstone for breakwaters. Eng Geol 86(1):18–36CrossRefGoogle Scholar
  14. Lu J (2002) Systematic identification of polyhedral rock blocks with arbitrary joints and faults. Comput Geotech 29(1):49–72CrossRefGoogle Scholar
  15. Luis-Delgado J, Martinez-Alajarin J, Tomas-Balibrea L (2003) Classification of marble surfaces using wavelets. Electron Lett 39(9):1CrossRefGoogle Scholar
  16. Luodes H, Selonen O, Pääkkönen K (2000) Evaluation of dimension stone in gneissic rocks—a case history from southern Finland. Eng Geol 58(2):209–223CrossRefGoogle Scholar
  17. Martinez-Alajarin J (2004) Supervised classification of marble textures using support vector machines. Electron Lett 40(11):1CrossRefGoogle Scholar
  18. Morales Demarco M, Oyhantçabal P, Stein K-J, Siegesmund S (2011) Black dimensional stones: geology, technical properties and deposit characterization of the dolerites from Uruguay. Environ Earth Sci 63(7–8):1879–1909CrossRefGoogle Scholar
  19. Morote-Martínez V, Pascual-Sánchez V, Martín-Martínez JM (2008) Improvement in mechanical and structural integrity of natural stone by applying unsaturated polyester resin-nanosilica hybrid thin coating. Eur Polymer J 44(10):3146–3155CrossRefGoogle Scholar
  20. Morote-Martínez V, Torregrosa-Coque R, Martín-Martínez JM (2011) Addition of unmodified nanoclay to improve the performance of unsaturated polyester resin coating on natural stone. Int J Adhes Adhes 31(3):154–163CrossRefGoogle Scholar
  21. Mosch S, Nikolayew D, Ewiak O, Siegesmund S (2011) Optimized extraction of dimension stone blocks. Environ Earth Sci 63(7–8):1911–1924CrossRefGoogle Scholar
  22. Mustafa S, Khan MA, Khan MR, Hameed F, Mughal MS, Asghar A, Niaz A (2015) Geotechnical study of marble, schist, and granite as dimension stone: a case study from parts of Lesser Himalaya, Neelum Valley Area, Azad Kashmir, Pakistan. Bull Eng Geol Env 74(4):1475–1487CrossRefGoogle Scholar
  23. Mustafa S, Khan MA, Khan MR, Sousa LM, Hameed F, Mughal MS, Niaz A (2016) Building stone evaluation—a case study of the sub-Himalayas, Muzaffarabad region, Azad Kashmir, Pakistan. Eng Geol 209:56–69CrossRefGoogle Scholar
  24. Mutlutürk M (2007) Determining the amount of marketable blocks of dimensional stone before actual extraction. J Min Sci 43(1):67–72CrossRefGoogle Scholar
  25. Ozcelik Y (2011) Determination of the regions used as facing and building stone according to the material characteristics in an andesite quarry. Eng Geol 118(3):104–109CrossRefGoogle Scholar
  26. Palmström A, Sharma VI, Saxena K (2001) In-Situ Characterization of rocks. AA Balkema Publishers, Lise/Abingdon/Exton (PA)/TokioGoogle Scholar
  27. Pinto JC, Sousa JM, Alexandre H (2003) New distance measures applied to marble classification. In: Sanfeliu A, Ruiz-Shulcloper J (eds) Progress in pattern recognition, speech and image analysis: 8th Iberoamerican Congress on Pattern Recognition, CIARP 2003, Havana, Cuba, November 26–29, 2003 Proceedings. Springer, Berlin Heidelberg, pp 383–390CrossRefGoogle Scholar
  28. Reddy DV (2002) Evaluation of natural defects in commercial decorative rock deposits in Karnataka, India. Gondwana Res 5(2):557–560CrossRefGoogle Scholar
  29. Sambuelli L, Calzoni C (2010) Estimation of thin fracture aperture in a marble block by GPR sounding. Boll di Geofis Teor ed Appl 51(2–3):239–252Google Scholar
  30. Selonen O, Luodes H, Ehlers C (2000) Exploration for dimensional stone—implications and examples from the Precambrian of southern Finland. Eng Geol 56(3):275–291CrossRefGoogle Scholar
  31. Selver MA, Akay O, Alim F, Bardakçı S, Ölmez M (2011) An automated industrial conveyor belt system using image processing and hierarchical clustering for classifying marble slabs. Robot Comput Integr Manuf 27(1):164–176CrossRefGoogle Scholar
  32. Shakas A, Linde N (2015) Effective modeling of ground penetrating radar in fractured media using analytic solutions for propagation, thin-bed interaction and dipolar scattering. J Appl Geophys 116:206–214CrossRefGoogle Scholar
  33. Siegesmund S, Nikolayev D, Hoffmann A, Mosch S (2007) 3D-block-expert. Naturstein 5(2007):102–107Google Scholar
  34. Smith J (2004) Determining the size and shape of blocks from linear sampling for geotechnical rock mass classification and assessment. J Struct Geol 26(6):1317–1339CrossRefGoogle Scholar
  35. Sohrabian B, Ozcelik Y (2012) Joint simulation of a building stone deposit using minimum/maximum autocorrelation factors. Constr Build Mater 37:257–268CrossRefGoogle Scholar
  36. Sousa LM (2007) Granite fracture index to check suitability of granite outcrops for quarrying. Eng Geol 92(3):146–159CrossRefGoogle Scholar
  37. Sousa L (2010) Evaluation of joints in granitic outcrops for dimension stone exploitation. Q J Eng GeolHydrogeol 43(1):85–94CrossRefGoogle Scholar
  38. Sousa JM, Pinto JRC (2004) Comparison of intelligent classification techniques applied to marble classification. International Conference Image Analysis and Recognition. Springer, Berlin, pp 802–809CrossRefGoogle Scholar
  39. Taboada J, Vaamonde A, Saavedra A (1999) Evaluation of the quality of a granite quarry. Eng Geol 53(1):1–11CrossRefGoogle Scholar
  40. Turanboy A (2010) A geometric approach for natural rock blocks in engineering structures. Comput Geosci 14(4):565–577CrossRefGoogle Scholar
  41. Vázquez P, Alonso F, Carrizo L, Molina E, Cultrone G, Blanco M, Zamora I (2013) Evaluation of the petrophysical properties of sedimentary building stones in order to establish quality criteria. Constr Build Mater 41:868–878CrossRefGoogle Scholar
  42. Wang L, Yamashita S, Sugimoto F, Pan C, Tan G (2003) A methodology for predicting the in situ size and shape distribution of rock blocks. Rock Mech Rock Eng 36(2):121–142CrossRefGoogle Scholar
  43. Yarahmadi R, Bagherpour R, Kakaie R, Mirzaie NH, Yari M (2014a) Development of 2D computer program to determine geometry of rock mass blocks. Int J Min Sci Technol 24(2):191–194CrossRefGoogle Scholar
  44. Yarahmadi R, Bagherpour R, Khademian A (2014b) Safety risk assessment of Iran’s dimension stone quarries (exploited by diamond wire cutting method). Saf Sci 63:146–150CrossRefGoogle Scholar
  45. Yarahmadi R, Bagherpour R, Sousa LM, Taherian S-G (2015) How to determine the appropriate methods to identify the geometry of in situ rock blocks in dimension stones. Environ Earth Sci 74(9):6779–6790CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Reza Yarahmadi
    • 1
  • Raheb Bagherpour
    • 1
    Email author
  • Sayed-Ghahreman Taherian
    • 2
  • Luis M. O. Sousa
    • 3
  1. 1.Department of Mining EngineeringIsfahan University of TechnologyIsfahanIran
  2. 2.Department of Mathematical SciencesIsfahan University of TechnologyIsfahanIran
  3. 3.Department of GeologyUniversity of Trás-os-Montes e Alto DouroVila RealPortugal

Personalised recommendations