Advertisement

Estimating compressive and flexural strength of travertines with respect to laminae-orientation by geomechanical properties

  • G. R. Khanlari
  • F. Naseri
  • D. M. Freire-Lista
Original Paper

Abstract

Travertine is an anisotropic rock considered as one of the most popular stone materials in the building industry. Due to its bedding and lamination planes, the determination of rock strength properties regarding anisotropic orientation is a critical issue. In this research, different techniques were applied to estimate the compressive and flexural strengths of four types of travertines, selected from Hamedan and Markazi Provinces located in the west and central parts of Iran. For this purpose, after sample preparation and assessment of mineral composition, fabric (texture and structure) and pore characteristics (pore shape and pore sizes), the selected samples were characterized using physical and mechanical tests. These properties were evaluated with respect to two major anisotropic orientations (perpendicular and parallel to the bedding/lamination axis). Statistical analyses, including simple and multiple linear regressions, were utilized to correlate physical and mechanical parameters with compressive and flexural strength, and to establish some new equations. Based on the test results, it can be concluded that the percentage/type of matrix and porosity have a more important effect on the physical and mechanical properties than the rock structure. Data analysis in simple regression shows that bulk specific gravity (saturated surface dry) and Brazilian tensile strength are the most and least influential factors on compressive strength at perpendicular and parallel directions, respectively. In addition, effective porosity and Brazilian tensile strength are the most and least influential factors on flexural strength at both directions, respectively. Based on best subset multiple regression method, one or two equations were extracted for calculating compressive and flexural strength in the perpendicular and parallel directions. Also, pore shape factor and pore radius were used as independent parameters in multiple regression to establish some new equations for predicting compressive and flexural strength considering cutting directions. These parameters have more influence on flexural strength than compressive strength, because the parameters show significant correlation with flexural strength. Consequently, the results of statistical analyses show that the proposed equations are not necessarily composed of parameters with the higher/stronger determination coefficient in simple regression. Therefore, prediction studies not only offer some rational approaches, they also give a better insight into the main factors determining rock strength.

Keywords

Travertine Bedding plane Cutting direction Petrographical characteristics Geomechanical properties Multiple regression 

Notes

Acknowledgments

This study was financed by the University of Bu-Ali Sina, department of geology. Our special thanks are offered to Mr. H.R. Mirzaiee (the chief of Sajad Stone Factory) who provided and cut the studied travertine blocks in the desired sizes. We would like to thank Dr. H. Mohsseni (Associated Professor in Sedimentary Geology) and S. Rahmani (PhD candidate in sedimentary geology) for their valuable suggestions on the petrography of the studied rocks.

References

  1. Ajalloeian R, Lashkaripour G (2000) Strength anisotropies in mudrocks. Bull Eng Geol Environ 59:195–199CrossRefGoogle Scholar
  2. Akin M (2010) A quantitative weathering classification system for yellow travertines. Environ Earth Sci 61:47–61CrossRefGoogle Scholar
  3. Akin M, Özsan A (2011) Evaluation of the long-term durability of yellow travertine using accelerated weathering tests. Bull Eng Geol Environ 70:101–114CrossRefGoogle Scholar
  4. Akin M, Özsan A, Akin M (2009) Investigation of the macro pore geometry of yellow travertines using the shape parameter approach. Environ Eng Geosci 15(3):197–209CrossRefGoogle Scholar
  5. Akyol E, Yagiz S, Ozkul M, Sen G, Kato S (2005) Physical properties of hot spring travertines related to lithotypes at Pamukkale region in Denizli, Turkey. In: International Symposium on Travertine, 21–25 September 2005, Denizli Turkey, pp 286–290Google Scholar
  6. ASTM (2001) Standard Test method for specific gravity and absorption of rock for erosion control ASTM Standards on Disc 0409 Designation D6473–15Google Scholar
  7. ASTM D 4543–85 (Reapproved 1991) Standard practice for preparing rock core specimens and determining dimensional and shape tolerances Annual book of ASTM standards, Section 4Google Scholar
  8. Basu A, Kamran M (2010) Point load test on schistose rocks and its applicability in predicting uniaxial compressive strength. Int J Rock Mech Min Sci 47:823–828CrossRefGoogle Scholar
  9. Bayram F (2012) Predicting mechanical strength loss of natural stones after freeze–thaw in cold regions. Cold Reg Sci Technol 83:98–102CrossRefGoogle Scholar
  10. Brown E (1981a) ISRM suggested methods. Rock characterization testing and monitoring. Pergamon, OxfordGoogle Scholar
  11. Brown E (1981b) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials Rock characterization, testing and monitoring—ISRM suggested methods. Pergamon, Oxford, pp 113–116Google Scholar
  12. Cargill JS, Shakoor A (1990) Evaluation of empirical methods for measuring the uniaxial compressive strength of rock. In: International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, vol 6. Elsevier, pp 495–503Google Scholar
  13. Chentout M, Alloul B, Rezouk A, Belhai D (2015) Experimental study to evaluate the effect of travertine structure on the physical and mechanical properties of the material. Arab J Geosci 8:8975–8985CrossRefGoogle Scholar
  14. Çobanoğlu İ, Çelik SB (2012) Determination of strength parameters and quality assessment of Denizli travertines (SW Turkey). Eng Geol 129:38–47CrossRefGoogle Scholar
  15. D’Andrea DV, Fischer R, Fogelson D (1965) Prediction of compressive strength from other rock properties vol 6702. United States Department of the Interior, Bureau of Mines, Washington DCGoogle Scholar
  16. Dehghan S, Sattari G, Chelgani SC, Aliabadi M (2010) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Technol (China) 20:41–46CrossRefGoogle Scholar
  17. British Standards Institution (2013) Natural stone test methods. Determination of flexural strength under concentrated load. BS EN 12372:2006, BSI, LondonGoogle Scholar
  18. Erdoğan O, Özvan A (2015) Evaluation of strength parameters and quality assessment of different lithotype levels of Edremit (Van) Travertine (Eastern Turkey). J Afr Earth Sci 106:108–117CrossRefGoogle Scholar
  19. Ersoy A, Atıcı U, Büyüksağiş I (2005) The assessment of the specific cutting energy in travertine. In: Proceedings of 1st International Symposium on Travertine, 21–25 September 2005, Pamukkale University, Denizli, Turkey, pp 217–223Google Scholar
  20. Folk RL (1962) Spectral subdivision of limestone types. In: Ham WE (ed) Classification of carbonate rocks—a symposium. American Association of Petroleum Geologists Memoir 1. AAPG, Washington DC, pp 62–84Google Scholar
  21. Franklin J (1985) Suggested method for determining point load strength. In: International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, vol 2. Elsevier, Amsterdam, pp 51–60Google Scholar
  22. García-del-Cura MÁ, Benavente D, Martínez-Martínez J, Cueto N (2012) Sedimentary structures and physical properties of travertine and carbonate tufa building stone. Constr Build Mater 28:456–467CrossRefGoogle Scholar
  23. Geological Society (1999) Appendix C: Stone and rock properties, vol 16. London, Engineering Geology Special Publications, pp 451–470Google Scholar
  24. Gokce MV (2015) The effects of bedding directions on abrasion resistance in travertine rocks. Turk J Earth Sci 24:196CrossRefGoogle Scholar
  25. Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng Geol 66:39–51CrossRefGoogle Scholar
  26. Griffith AA (1924) The theory of rupture. In: Biezeno CB, Burgers JM (eds) First International Congress of Applied Mechanics. Delft, The Netherlands, pp 55–63Google Scholar
  27. Hocking RR (1976) A Biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics 32:1–49CrossRefGoogle Scholar
  28. Irfan T (1996) Mineralogy, fabric properties and classification of weathered granites in Hong Kong. Q J Eng Geol Hydrogeol 29:5–35CrossRefGoogle Scholar
  29. ISRM (1978) Suggested methods for determining tensile strength of rock materials. Int J Rock Mech Min Sci Geomech Abstr 15 doi: 10.1016/0148-9062(78)90003-7
  30. Jafari E, Nikudel M, Ahmadi M (2010) Evaluation of strength characteristics of rocks using cylindrical punch test results. J Sci Univ Tehran 36:169–183Google Scholar
  31. Kadane JB, Lazar NA (2004) Methods and criteria for model selection. J Am Stat Assoc 99:279–290CrossRefGoogle Scholar
  32. Karakul H, Ulusay R, Isik N (2010) Empirical models and numerical analysis for assessing strength anisotropy based on block punch index and uniaxial compression tests. Int J Rock Mech Min Sci 47:657–665CrossRefGoogle Scholar
  33. Karakus M, Kumral M, Kilic O (2005) Predicting elastic properties of intact rocks from index tests using multiple regression modeling. Int J Rock Mech Min Sci 42:323–330CrossRefGoogle Scholar
  34. Khanlari G-R, Heidari M, Sepahigero A-A, Fereidooni D (2014) Quantification of strength anisotropy of metamorphic rocks of the Hamedan province, Iran, as determined from cylindrical punch, point load and Brazilian tests. Eng Geol 169:80–90CrossRefGoogle Scholar
  35. Khanlari G, Rafiei B, Abdilor Y (2015) Evaluation of strength anisotropy and failure modes of laminated sandstones. Arab J Geosci 8:3089–3102CrossRefGoogle Scholar
  36. Kohno M, Maeda H (2012) Relationship between point load strength index and uniaxial compressive strength of hydrothermally altered soft rocks. Int J Rock Mech Min Sci 50:147–157CrossRefGoogle Scholar
  37. Korkanç M (2016) The characterization of building stones from the ancient Tyana aqueducts, Central Anatolia, Turkey: implications on the factors of deterioration processes. Bull Eng Geol Environ. doi: 10.1007/s10064-016-0930-2
  38. Montgomery DC, Peck EA, Vining GG (2015) Introduction to linear regression analysis. Wiley, New YorkGoogle Scholar
  39. Ozcelik Y, Yilmazkaya E (2011) The effect of the rock anisotropy on the efficiency of diamond wire cutting machines. Int J Rock Mech Min Sci 48:626–636CrossRefGoogle Scholar
  40. Pentecost A (2005) Travertine. Springer, BerlinGoogle Scholar
  41. Rilem T 25-PEM (1980) Recommended tests to measure the deterioration of stone and to assess the effectiveness of treatment methods. Mater Struct 13:175–253Google Scholar
  42. Sengun N, Demirdag S, Ugur I, Akbay D, Altindag R (2015) Assessment of the physical and mechanical variations of some travertines depend on the bedding plane orientation under physical weathering conditions. Constr Build Mater 98:641–648CrossRefGoogle Scholar
  43. Shakoor A, Bonelli RE (1991) Relationship between petrographic characteristics, engineering index properties, and mechanical properties of selected sandstones. Environ Eng Geosci 28:55–71CrossRefGoogle Scholar
  44. Shalabi FI, Cording EJ, Al-Hattamleh OH (2007) Estimation of rock engineering properties using hardness tests. Eng Geol 90:138–147CrossRefGoogle Scholar
  45. Sharma P, Singh T (2008) A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bull Eng Geol Environ 67:17–22CrossRefGoogle Scholar
  46. Singh V, Singh D, Singh T (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38:269–284CrossRefGoogle Scholar
  47. Sulukcu S, Ulusay R (2001) Evaluation of the block punch index test with particular reference to the size effect, failure mechanism and its effectiveness in predicting rock strength. Int J Rock Mech Min Sci 38:1091–1111CrossRefGoogle Scholar
  48. Tokashiki N, Kyoya T, Aydan O (1995) A research on modeling porous rocks. In: Symposium on Numerical Methods in the Design and Development of Agricultural Engineering. Machinery Society of Japan, Okinawa, Japan, pp 65–79Google Scholar
  49. Torabi-Kaveh M, Naseri F, Saneie S, Sarshari B (2015) Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arab J Geosci 8:2889–2897CrossRefGoogle Scholar
  50. Török Á, Vásárhelyi B (2010) The influence of fabric and water content on selected rock mechanical parameters of travertine, examples from Hungary. Eng Geol 115:237–245CrossRefGoogle Scholar
  51. TS EN 12372 (2013) Natural stone test methods—determination of flexural strength under concentrated load. Institute of Turkish Standards. pp 15Google Scholar
  52. Ulusay R, Türeli K, Ider M (1994) Prediction of engineering properties of a selected litharenite sandstone from its petrographic characteristics using correlation and multivariate statistical techniques. Eng Geol 38:135–157CrossRefGoogle Scholar
  53. Ulusay R, Gokceoglu C, Sulukcu S (2001) Draft ISRM suggested method for determining block punch strength index (BPI). Int J Rock Mech Min Sci 38:1113–1119CrossRefGoogle Scholar
  54. Vernik L, Bruno M, Bovberg C (1993) Empirical relations between compressive strength and porosity of siliciclastic rocks. In: International journal of rock mechanics and mining sciences and geomechanics abstracts, vol 7. Pergamon, Oxford, pp 677–680Google Scholar
  55. Yagiz S (2009) Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer. Bull Eng Geol Environ 68:55–63CrossRefGoogle Scholar
  56. Yagiz S (2012) Comments on “Determination of strength parameters and quality assessment of Denizli travertines (SW Turkey)” Ibrahim Cobanoglu and Sefer Beran Celik, 129–130 (2012) 38–47. Eng Geol 147:149–150CrossRefGoogle Scholar
  57. Yesiloglu-Gultekin N, Sezer EA, Gokceoglu C, Bayhan H (2013) An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents. Expert Syst Appl 40:921–928CrossRefGoogle Scholar
  58. Yilmaz I, Yucel Ö (2014) Use of the core strangle test for determining strength anisotropy of rocks. Int J Rock Mech Min Sci 66:57–63Google Scholar
  59. Zarif I, Tuğrul A (2003) Aggregate properties of Devonian limestones for use in concrete in Istanbul, Turkey. Bull Eng Geol Environ 62:379–388CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • G. R. Khanlari
    • 1
  • F. Naseri
    • 1
  • D. M. Freire-Lista
    • 2
  1. 1.Department of Geology, Faculty of SciencesBu-Ali Sina UniversityHamedanIran
  2. 2.Instituto de Geociencias IGEO (CSIC, UCM) Spanish Research Council CSICComplutense University of MadridMadridSpain

Personalised recommendations