Rock Brittleness Prediction Using Geomechanical Properties of Hamekasi Limestone: Regression and Artificial Neural Networks Analysis

Document Type : Research Paper

Authors

1 Bu_ Ali Sina university, Hamedan, Iran

2 Bu-Ali Sina university

Abstract

The cold climate is a favorable parameter for the development of tension cracks and decrease of rock brittleness. Therefore, this paper attempts to investigate the Hamekasi porous limestone in order to predict the brittleness indices during freeze-thaw cycles. The freeze–thaw test was executed for one cycle including 16 h of freezing, and 8 h of thawing. The geo mechanical properties and brittleness indices (B1, B2, B3) of limestones were measured across freeze-thaw cycles from cycle 0 (fresh rock) to cycle 40. Statistical analyses, including simple and multiple regressions, were applied to identify those geomechanical parameters that are most influenced by the progression of freeze-thaw cycles and more appropriate for the brittleness prediction. Based on simple regression, all geomechanical properties including tensile strength (), uniaxial compressive strength (), P-wave velocity (Vp), porosity (n), and quick absorption index (QAI) (except dry density ()) demonstrated good correlations with brittleness index (B3). The integrated prediction of brittleness is put forward to develop some models by multiple regression (MR) and artificial neural network (ANN) with some statistic parameters (R, RMSE, VAF and ME), based on all geomechanical properties examined in this research. It is concluded that models based on n, Vp and  exhibited high performance according to the obtained statistic parameters. In spite of the fact that Vp has good correlation coefficient (R) with freeze-thaw cycles, and B3 (R2= 0.74, and 0.55, respectively) in simple regression, it does not have a prominent effect on B3 in MR models. Also, parameters with low correlation coefficient in simple regression (=0.15) cannot improve the model performance in ANNmethods

Keywords

Article Title [Persian]

پیش بینی شکنندگی سنگ با استفاده از ویژگی های ژئومکانیکی سنگ آهک همه کسی: آنالیز رگرسیون و شبکه عصبی مصنوعی

Author [Persian]

  • فاطمه ناصری 2
2 دانشگاه بوعلی سینا
Abstract [Persian]

آب و هوای سرد، پارامتر مناسبی برای توسعه ترک های کششی و کاهش شکنندگی سنگ است. بنابراین در این مقاله سعی بر بررسی سنگ آهک های متخلخل همه کسی و پیش بینی شکنندگی آنها در طی چرخه های انجماد و ذوب شدن شده است. هر چرخه از آزمایش انجماد و ذوب شدن شامل 16 ساعت انجماد و 8 ساعت ذوب شدن است. ویژگی های ژئومکانیکی و شاخص های شکنندگی (B1, B2, B3)  سنگ آهک ها در طی چرخه های انجماد و آب شدن از چرخه (سنگ غیر هوازده) تا چرخه 40 اندازه گیری شده اند. آنالیز آماری شامل رگرسیون ساده و چندمتغیره به منظور شناسایی پارامترهای ژئومکانیکی که نسبت به سایر پارامترها تحت تاثیر پیشرفت چرخه های انجماد و ذوب شدن قرار گرفته و برای پیش بینی شکنندگی مناسبتر هستند، بکار رفته اند. از دیدگاه آنالیز رگرسیون ساده، تمامی پارامترهای ژئومکانیکی شامل مقاومت کششی، مقاومت فشاری تک محوری، سرعت موج p، تخلخل و جذب آب (به استثنای دانسیته خشک) ارتباط خوبی با شاخص شکنندگی نشان داده اند. در این تحقیق؛ پیش بینی یکپارچه شکنندگی به منظور توسعه مدل های چند متغیره (MR) و شبکه عصبی مصنوعی (ANN) با تعدادی پارامتر آماری (R ،  RMSE، VAF و ME) و بر اساس خصوصیات ژئومکانیکی بررسی شده است. بر طبق آماره های بدست آمده، مدل­هایی که بر اساس n، Vp و  می باشند کارایی بیشتری نشان می دهند. علیرغم این حقیقت که Vp در رگرسیون ساده ضریب انطباق خوبی با چرخه های انجماد و آب شدن و B3  (R= 0.74  و R= 0.55) دارد، اما اثر آشکاری بر B3 در مدل های MR ندارد. همچنین پارامترهایی با ضریب تعیین کم در رگرسیون ساده (=0.15) نمی توانند سبب بهبود مدل ها در رگرسیون چند متغیره شوند

Keywords [Persian]

  • سنگ آهک های متخلخل چرخه های انجماد
  • مدل های چند متغیره (MR)
  • شبکه عصبی مصنوعی (ANN

References

Alavi, M., 1994. Tectonics of Zagros orogenic belt of Iran: new data and interpretation. Tectonophys. 229: 211–238.
Al-Harthi, A.A., Al-Amri, R.M., Shehata, W.M., 1999. The porosity and engineering properties of vesicular basalt in Saudi Arabia. Engineering Geology. 54: 313–320.
Altindag, R., 2002. The evaluation of rock brittleness concept on rotary blast hole drills. Journal of The South African Institute of Mining and Metallurgy. 102 (1): 61- 66.
Altindag, R., 2003. Correlation of specific energy with rock brittleness concepts on rock cutting. Journal of The South African Institute of Mining and Metallurgy. 103: 163-173.
Altindag, R., 2010. Reply to the discussion by Yagiz on ‘‘Assessment of some brittleness indexes in rock-drilling efficiency’’ by Altindag. Rock Mechanics and Rock Engineering, 43: 375–376.
Altindag, R., Guney, A., 2010. Predicting the relationships between brittleness and mechanical properties (UCS, TS and SH) of rocks. Scientific Research and Essays. 5 (16): 2107–2118.
Amiri, M., 2005 The effect of bedrock dissolution and pumping on Hamadan sinkholes occurrences. Proceeding of the conference on hazards of sinkholes in karst terrains, Kermanshah, Iran (in Persian), pp. 43–67.
Aqil, M., Kit, I., Yano, A., Nishiyama, S., 2007. A comparative study of  ANNand neuro-fuzzy in continuous modeling of the daily and hourly behavior of runoff. Journal of Hydrology. 337: 22–34.
ASTM, 2004. Standard test method for evaluation of durability of rock erosion control  under freezing and thawing conditions, D, 5312-92.
Bayram, F., 2012. Predicting mechanical strength loss of natural stones after freeze–thaw in cold regions. Cold Regions Science and Technology. 83- 84, 98–102.
Berberian, M., Alavi-Tehrani, N., 1977. Structural analyses of Hamadan metamorphic tectonites: A Paleotectonic Discussion. Geological Survey of Iran, Report no. 40: 263–279.
Blindheim, O.T., Bruland, A., 1998. Boreability testing. Norwegian TBM tunnelling 30 years of Experience with TBMs in Norwegian Tunnelling, Norwegian Soil and Rock Engineering Association. 11: 29–34.
Chen, C.H., Yeung, M.R., Mori, N., 2004. Effect of water saturation on deterioration of welded tuff due to freeze–thaw action. Cold Regions Science and Technology. 38: 127–136.
Bolourchi, M.H., 1979. Explanatory text of Kabudar Ahang Quadrangle Map, scale 1:250,000. Geological and Mineral Survey of Iran.
George, E. A., 1995. Brittle failure of rock material – Test results and constitutive models. A.A. Balkema/Rotterdam/Brolkfield. pp. 123-128.
Gharahbagh, A.E., Fakhimi, A., Socorro, N.M., 2011. The effect of pore size on tensile and compressive strengths of rock: a bonded particle simulation. 45th US rock mechanics geomechanic Symposium, San Francisco, CA.
Goktan, R.M., Yilmaz, N.G., 2005. A new methodology for the analysis of the relationship between rock brittleness index and drag pick cutting efficiency. Journal of The South African Institute of Mining and Metallurgy. 105: 727-733.
Gong, Q.M., Zhao, J., 2007. Influence of rock brittleness on TBM penetration rate in Singapore granite. Tunnelling and Underground Space Technology. 22: 317- 324.
Hajiabdolmajid, V., Kaiser, P.K., Martin, C.D., 2002. Modelling brittle failure of rock. International Journal of Rock Mechanics and Mining Sciences. 39: 731- 741.
Heidari, M., Khanlari, G.R., Torabi-Kaveh, M., Kargarian, S., Saneie, S., 2013. Effect of Porosity on Rock Brittleness. Rock mechanics and rock engineering. 47:785-790.
Hori, M., Morihiro, H., 1998. Micromechanical analysis on deterioration due to freezing and thawing in porous brittle materials. International Journal of Engineering Science. 4: 511- 522.
Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks. 2: 359- 366.
Hucka, V., Das, B., 1974. Brittleness determination of rocks by different methods. International Journal of Rock Mechanics and Mining Sciences 11: 389- 392.
ISRM., 1978. Suggested methods for determining tensile strength of rock materials. International Journal of Rock Mechanics and Mining Sciences. 15: 99- 103.
ISRM., 1979. Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. International Journal of Rock Mechanics and Mining Sciences. 18: 85- 110.
ISRM., 1981. ISRM Suggested Methods: Rock Characterization, Testing and Monitoring: International Society of Rock Mechanics Suggest Methods. Pergamon Press, London.
ISRM., 1985. Suggested method for determining point load strength. International Journal of Rock Mechanics and Mining Sciences. 2: 51- 60.
Kahraman, S., 2002. Correlation of TBM and drilling machine performances with rock brittleness. Engineering Geology. 4: 269- 283.
Kaiser, P.K., Diederichs, M.S., Martin, C.D., Sharp, J., Steiner, W., 2000. Underground works in hard rock tunneling and mining. Keynote lecture at GeoEngineering 2000. Melbourne, Australia: Technomic Publishing Co. 1: 841–926.
Karakus, M., Kumra, M., Kilic, O., 2005. Predicting elastic properties of intact rocks from index tests using multiple regression modelling. International Journal of Rock Mechanics and Mining Sciences. 2: 323-330.
Karimi, H., Taheri, K., 2010. Hazard and mechanism of sinkholes on Kabudar Ahang and Famenin plains of Hamadan, Iran. Natural Hazards. 55: 481- 499.
Khanlari, G.R., Heidari, M., Momeni, A.A., Ahmadi, M., Taleb Beydokhti, A., 2012. The effect of groundwater overexploitation on land subsidence and sinkhole occurrences, western Iran. Quarterly JournalofEngineering GeologyandHydrogeology. 45: 447- 456.
Lawrence, J., 1991. Introduction to neural networks, 3rd edn. California Scientific Software, Grass Valley.
Lu, M., AbouRizk, S.M., Hermann, U.H., 2001. Sensitivity analysis of neural networks in spool fabrication productivity studies. Journal of Computing in Civil Engineering. 15: 299–308.
Malinova, T., Guo, Z.X., 2004. Artificial neural network modeling of hydrogen storage. properties of Mg-based Alloys. Materials Science and Engineering A. 365: 219- 227.
Martinez-Martinez, J., Benavente, D., Gomez-Heras, M., Marco-Castao, L., Garcia-del-Cura, M.A., 2013. Non-linear decay of building stones during freeze–thaw weathering processes. Construction and Building Materials. 38: 443- 454.
Matlab 7.1., 2005. Software for technical computing and model-based design. The Math Works Inc.
Moh’d, B.K., 2009. Compressive strength of vuggy oolitic limestones as a function of their porosity and sound propagation. Jordan Journal of Earth and Environmental Sciences. 1: 18- 25.
Nourani, V., Sayyah Fard, M., 2012. Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software. 47:127–146.
Palchik, V., Hatzor, Y.H., 2004. The influence of porosity on tensile and compressive strength of porous chalks. Rock Mechanics and Rock Engineering. 37(4): 331- 341.
Protodyakonov, M.M., 1963. Mechanical properties and drillability of rocks. In Proc. 5th symp. Rock mech, University of Minnesota USA.
Rajabzadeh, M.A., Moosavinasab, Z., Rakhshandehroo, G., 2012. Effects of rock classes and porosity on the relation between uniaxial compressive strength and some rock properties for carbonate rocks. Rock Mechanics and Rock Engineering. 1: 113- 122.
Sabziparvar, A.A., 2003. The analysis of aridity and meteorological drought indices in west of Iran. Research report. Bu-Ali Sina University, Hamadan, Iran.
Sepahi, A.A., 1999. Petrology of Alvand plutonic complex with special reference on granitoids. Thesis (PhD). In Persian; Tarbiat Moallem University of Tehran, Iran.
Suorineni, F.T., Chinnasane, D.R., Kaiser, P.K., 2009. A procedure for determining rock-type specific Hoek–Brown brittle parameters. Rock Mechanics and Rock Engineering. 42: 849–881.
Takarli, M., Prince, W., Siddique, R., 2008. Damage in granite under heating/cooling cycles and water freeze–thaw condition. International Journal of Rock Mechanics and Mining Sciences. 45: 1164–1175.
Tan, X., Chen, W., Tian, H., Cao, J., 2011. Laboratory investigations on the mechanical properties degradation of granite under freeze–thaw cycles. Cold Regions Science and Technology. 68:130–138.
Torabi-Kaveh, M., Naseri, F., Saneie, S., Sarshari, B., 2014. Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arabian journal of Geosciences. 8: 2889-2897.
Vihtuk, A.A., 1998. Determination of strength of solid porous body. Acta Physica Polonica A. 93 (Suppl), S71.
Yagiz, S., 2004. Correlation between uniaxial compressive strength and brittleness of selected rock types. In: 57th Geological Congress of Turkey, MTA General Directory, Ankara, Turkey, Abstract, p. 160.
Yagiz, S., 2006. An investigation on the relationship between rock strength and brittleness. In: 59th Geological Congress of Turkey, MTA General Directory, Ankara, Turkey, Abstract, p. 352.
Yagiz, S., 2009. Assessment of brittleness using rock strength and density with punch penetration test. Tunnelling and Underground Space Technology. 24: 66-74.
Yagiz, S., Gokceoglu, C., 2010. Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Systems With Applications. 37: 2265- 2272.
Yarali, O., Kahraman, S., 2011. The drillability assessment of rocks using the different brittleness values. Tunnelling and Underground Space Technology. 26: 406- 414.
Yarali O, Soyer E (2011) The effect of mechanical rock properties and brittleness on drillability. Science Research Essays 6(5): 1077-1088.
Zamiran Consulting Engineers., 2003. Sinkhole and subsidence studies in Kabudar Ahang and Famenin plain. Report, West Water Authority Iran.
Zurada, J.M., 1992. Introduction to Artificial Neural Systems. St. Paul.: West Publishing.
 
Geopersia
Volume 6, Issue 1 - Serial Number 22287825
Winter and Spring
March 2016
Pages 19-33

Files

History

  • Receive Date: 04 July 2015
  • Revise Date: 29 May 2016
  • Accept Date: 26 June 2016

Share

How to cite

Statistics