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Application of soft computing methods in predicting uniaxial compressive strength of the volcanic rocks with different weathering degree

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Abstract

Uniaxial compressive strength (UCS) of rock material is very important parameter for rock engineering applications such as rock mass classification, numerical modelling bearing capacity, mechanical excavation, slope stability and supporting with respect to the engineering behaviors’ of rock. UCS is obtained directly or can be predicted by different methods including using existing tables and diagrams, regression, Bayesian approach and soft computing methods. The main purpose of this study is to examine the applicability and capability of the Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) for prediction of UCS of the volcanic rocks and to compare its performance with Least Square Support Vector Machine (LS-SVM). The samples tested were taken from the volcanic rock masses exposed at the eastern Pontides (NE Turkey). In the soft computing model to estimate UCS of the samples investigated, porosity and slake durability index were used as input parameters. In this study, the root mean square error (RMSE), variance account factor (VAF), maximum determination coefficient value (R2), adjusted determination coefficient (Adj. R2) and performance index (PI), regression error characteristic (REC) curve and Taylor diagram were used to determine the accuracy of the ELM, MPMR and LS-SVM models developed.

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References

  • Aboutaleb S, Mahmoud Behnia M, Bagherpour R, Bluekian B (2018) Using non-destructive tests for estimating uniaxial compressive strength and static Young’s modulus of carbonate rocks via some modeling techniques. Bull Eng Geol Environ 77(4):1717–1728

    Google Scholar 

  • Acarlioglu S, Kadir S, Abdioglu E, Arslan M (2013) Epithermal-alteration geology, mineralogy and geochemistry of Eocene volcanic rocks in the Hasandağ (Giresun) area, eastern Pontides, NE Turkey. N Jb Miner Abh (J.Min. Geochem.) 190(1):79–99

    Google Scholar 

  • Ali M, Guan W, Ibrahim A (2014) Empirical relations between compressive strength and microfabric properties of amphibolites using multivariate regression, fuzzy inference and neural networks: a comparative study. Eng Geol 183:230–240

    Google Scholar 

  • Alvarez-Grima M, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349

    Google Scholar 

  • Ashtaria M, Mousavib SE, Cheshomib A, Khamechiana M (2019) Evaluation of the single compressive strength test in estimating uniaxial compressive and Brazilian tensile strengths and elastic modulus of marlstone. Eng Geol 248:256–266

    Google Scholar 

  • Baud P, Wong T-F, Zhu W (2014) Effects of porosity and crack density on the compressive strength of rocks. Int J Rock Mech Min Sci 64:202–211

    Google Scholar 

  • Baykasoglu A, Gullu H, Canakci H (2008) Predicting of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35(1–2):111–123

    Google Scholar 

  • Beiki M, Majdi A, Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159–169

    Google Scholar 

  • Bi J, Bennett KP (2003) Regression error characteristic curves, proceedings of the twentieth international conference on machine learning (ICML-2003), Washington, pp 43–50

  • BS 5930 (1981) Code of practice for site investigations. British Standards Institute, London

    Google Scholar 

  • Bubeck A, Walker RJ, Healy D, Dobbs M, Holwell DA (2017) Pore geometry as a control on rock strength. Earth Planet Sci Lett 457:38–48

    Google Scholar 

  • Burges C (1998) A tutorial on support vector machines for pattern recognition”, in “data mining and knowledge discovery. Kluwer Academic Publishers, Boston

  • Canakci H, Baykasoglu A, Gullu H (2009) Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput & Applic 18:1031–1041

    Google Scholar 

  • Cargill JS, Shakoor A (1990) Evaluation of empirical methods for measuring the uniaxial compressive strength. Int J Rock Mech Min Sci 27:495–503

    Google Scholar 

  • Celik SB (2019) Prediction of uniaxial compressive strength of carbonate rocks from nondestructive tests using multivariate regression and LS-SVM methods. Arab J Geosci 12:193

    Google Scholar 

  • Cengiz K, Sertcelik F, Sertcelik I (2018) Estimation of unconfined uniaxial compressive strength using schmidt hardness ultrasonic pulse velocity. Tehnıckı VjesnıkTechnıcal Gaz 25(5):1669–1574

    Google Scholar 

  • Ceryan S (2008) New chemical weathering indices for estimating engineering properties of rocks: a case study from Kürtün Granodiorite, NE Turkey. Turkish J Earth Sci 17:187–207

    Google Scholar 

  • Ceryan S (2012) Weathering indices for assessment of weathering effect and classification of weathered rocks: a case study from ne Turkey. In: Dar, Imran A. (Ed.), Earth Sciences. InTech Publication, pp. 19–44, ISBN 978-953-307-861-8

  • Ceryan N (2014) Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. J Afr Earth Sci 100:634–644

    Google Scholar 

  • Ceryan S (2015) New weathering indices for evaluating durability and weathering characterization of crystalline rock material: a case study from NE Turkey. J Afr Earth Sci 103:54–64

    Google Scholar 

  • Ceryan N, Korkmaz Can N (2018) Prediction of the uniaxial compressive strength of rocks materials. In: Ceryan N (ed) Handbook of Research on Trends and Digital Advances in Engineering Geology. IGI Global United States of America, pp 39–76

  • Ceryan S, Tudes S, Ceryan N (2008) A new quantitative weathering classification for igneous rocks. Environ Geol 55:1319–1336

    Google Scholar 

  • Ceryan N, Okkan U, Kesimal A (2013) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ Earth Sci 68:807–819

    Google Scholar 

  • Ceryan N, Berber S, Ceryan S (2016) Prediction of unconfined compressive strength of weathered rocks using p-durability index and porosity in soft computing models. ICENS 2016, 2nd International Conference on Engineering and Natural Sciences, 24-28

  • Cevik A, Sezer EA, Cabalar AF, Gokceoglu C (2011) Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput 11:2587–2594

    Google Scholar 

  • Chang C, Zoback MD, Khaksar A (2006) Empirical relations between rock strength and physical properties in sedimentary rocks. J Pet Sci Eng 51:223–237

    Google Scholar 

  • Chaudhary B (2004). Correlation between UCS and penetration strength on weak calcarenite of the roman mine pillar by Tu Delft hand penetrometer. Personal communication. ITC, Delft

  • Cheshom A, Ahmadi-Sheshde E (2013) Determination of uniaxial compressive strength of microcrystalline limestone using single particles load test. J Pet Sci Eng 111:121–126

    Google Scholar 

  • Cheshomi A, Ahmadi-Seshde E, Galandarzade A (2012) Introducing single particle loading apparatus and repeatability of the results. Iran J Eng Geol 5:17–32 (Persian Language)

    Google Scholar 

  • Cheshomi A, Mousavi E, Ahmadi-Sheshde E (2015) Evaluation of single particle loading test to estimate the uniaxial compressive strength of sandstone. J Pet Sci Eng 135:421–428

    Google Scholar 

  • Cheshomi A, Hajipour G, Hassanpour J, Dashtaki BB, Firouzei Y, Sheshde EA (2017) Estimation of uniaxial compressive strength of shale using indentation testing. J Pet Sci Eng 151:24–30

    Google Scholar 

  • Deere DU, Miller R P(1966) Engineering classification and index properties for intact rock. Technical Report AFWL-TR-65-116. Air Force Weapons Lab., Kirtland Air Force Base, New Mexico pp. 308

  • Dehghan S, Sattari GH, Chehreh Chelgani S, Aliabadi MA (2010) Prediction of uniaxial compressive strength and modulus of elasticity for travertine samples using regression and artificial neural networks. Min Sci Technol 20:41–46

    Google Scholar 

  • Demirdag S, Sengun N, Ugur I, Altindag R (2018) Estimating the uniaxial compressive strength of rocks with schmidt rebound hardness by considering the sample size. Arab J Geosci 11:502

    Google Scholar 

  • Deo RC, Samui P (2017) Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, Gaussian process, and minimax probability machine regression: case study of Brisbane City. J Hydrol Eng 22(6):05017003. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001506

  • Dincer I, Acar A, Ural S (2008) Estimation of strength and deformation properties of quaternary caliche deposits. Bull Eng Geol Environ 67:353–366

    Google Scholar 

  • Fattahi H (2017) Applying soft computing methods to predict the uniaxial compressive strength rocks from schmidt hammer rebound values. Comput Geosci 21(4):665–681

    Google Scholar 

  • Fener M, Kahraman S, Bilgil A, Gunaydin O (2005) A comparative evaluation of indirect methods to estimate the compressive strength of rocks. Rock Mech Rock Eng 38(4):329–334

    Google Scholar 

  • Feng X (2015) Application of Bayesian approach in geotechnical engineering Ph.D. Thesis Polytechnic University of Madrid, 198p

  • Fereidooni D (2016) Determination of the geotechnical characteristics of hornfelsic rocks with a particular emphasis on the correlation between physical and mechanical properties. Rock Mech Rock Eng 49:2595–2608

    Google Scholar 

  • Gedik N (2018) Least squares support vector mechanics to predict the stability number of rubble-mound breakwaters. Waterm 10(10):1452. https://doi.org/10.3390/w10101452

    Article  Google Scholar 

  • Ghasemi E, Kalhori H, Bagherpour R, Yagiz S (2018) Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks. Bull Eng Geol Environ 77:331–343

    Google Scholar 

  • Gokceoglu C (1997) Approach to eliminate the difficulties encountered in the engineering classification of clay masses containing dense discontinuities PhD Thesis, Hacette University, Department of Geological Engineering, 214p. (in Turkish)

  • Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of the agglomerates from their petrographic composition. Eng Geol 66(1–2):39–51

    Google Scholar 

  • Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17:61–72

    Google Scholar 

  • Gokceoglu C, Ulusay R, Sonmez H (2000) Factors affecting the durability of selected weak and clay bearing rocks from Turkey. With particular emphasis on the influence of the number of drying and wetting cycles. Eng Geol 57:215–237

    Google Scholar 

  • Gokceoglu C, Sonmez H, Zorlu K (2009) Estimating the uniaxial compressive strength of some clay bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models. Expert Syst 26(2):176–190

    Google Scholar 

  • Gopinath KGS, Pal S, Tambe P (2018) Prediction of weight percentage alumina and pore volume fraction in bio-ceramics using gaussian process regression and minimax probability machine regression material today. Proceedings 5(5):12233–12239

    Google Scholar 

  • Griffiths L, Heap MJ, Xu T, Chen CF, Baud P (2017) The influence of pore geometry and orientation on the strength and stiffness of porous rock. J Struct Geol 96:149–160

    Google Scholar 

  • Haftani M, Bohloli B, Moosavi M, Nouri A, Moradi M, Javan MRM (2013) A new method for correlating rock strength to indentation tests. J Pet Sci Eng 112:24–31

    Google Scholar 

  • Haramy KY, De Marco MJ (1985) Use of schmidt hammer for rock and coal testing. In: Proceedings 26th US symposium rock mechanics, 26–28 June, Rapid City, SD. Rotterdam: Balkema, 549–555

  • Heidari M, Mohseni H, Jalali SH (2018) Prediction of uniaxial compressive strength of some sedimentary rocks by fuzzy and regression models. Geotech Geol Eng 36:401–412

    Google Scholar 

  • Heidarian M, Jalalifar H, Rafati A (2016) Prediction of rock strength parameters for an Iranian oil field using neurofuzzy method. J AI Data Min 4:2

    Google Scholar 

  • Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Google Scholar 

  • Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE (2010) Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agric 71(2):107–127

    Google Scholar 

  • Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Google Scholar 

  • International Society for Rock Mechanics (1981) In: Brown ET (ed) Rock characterization, testing and monitoring-ISRM suggested methods. Oxford Pergamon, p 211

  • International Society for Rock Mechanics (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay H (ed) Suggested methods prepared by the commission on testing methods. International Society for Rock Mechanics. ISRM Turkish National Group, Ankara, p 628

    Google Scholar 

  • Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Monjezi M, Narayanasamy MS, Amin MFM (2015) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main range granite. Bull Eng Geol Environ 74:1301–1319

    Google Scholar 

  • Jahed Armaghani D, Amin MFM, Yagiz S, Faradonbeh RS, Abdullah RA (2016a) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186

    Google Scholar 

  • Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Monjezi M, Narayanasamy MS (2016b) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:48

    Google Scholar 

  • Jamshidi A, Zamanian H, Sahamieh RZ (2018) The effect of density and porosity on the correlation between uniaxial compressive strength and p-wave velocity. Rock Mech Rock Eng 51(4):1279–1286

    Google Scholar 

  • Kahraman S, Fener M, Kilic CO (2016) A preliminary study on the conversion factor used in the prediction of the UCS from the BPI for pyroclastic rocks. Bull Eng Geol Environ 75(2):771–780

    Google Scholar 

  • Karakus M, Tutmez B (2006) Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock Mech Rock Eng 39(1):45–57

    Google Scholar 

  • Kayabali K, Selcuk L (2010) Nail penetration test for determining the uniaxial compressive strength of rock. Int J Rock Mech Min Sci 47:265–275

    Google Scholar 

  • Koncagul CE, Santi PM (1999) Predicting the unconfined compressive strength of the Breathitt shale using durability, shore hardness and rock structural properties. Int J Rock Mech Min Sci 36:139–153

    Google Scholar 

  • Koolivand-Salooki M, Esfandyari M, Rabbani E, Koulivand M, Azarmehre A (2017) Application of genetic programing technique for predicting uniaxial compressive strength using reservoir formation properties. J Pet Sci Eng 159:35–48

    Google Scholar 

  • Kumar M, Samui P, Naithani AK (2013) Determination of uniaxial compressive strength and modulus of elasticity of travertine using machine learning techniques. Int J Adv Soft Comput Appl 5(3):1–15

    Google Scholar 

  • Lanckriet GRG, Ghaoui LE, Bhattacharyya C, Jordan MI (2002a) Minimax probability machine. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems 14. MA. MIT Press, Cambridge

    Google Scholar 

  • Lanckriet GRG, Ghaoui LE, Bhattacharyya C, Jordan MI (2002b) A robust minimax approach to classification. J Mach Learn Res 3:555–582

    Google Scholar 

  • Lee CJ, Lee GSW, Yoon ES (2008) A new estimation algorithm of physical proper-ties based on a group contribution and support vector machine. Korean J Chem Eng 25:568–574

    Google Scholar 

  • Li W, Tan Z (2017) Research on rock strength prediction based on least squares support vector machine. Geotech Geol Eng 35:385–393

    Google Scholar 

  • Liu Z, Shao J, Xu W, Wu Q (2015) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech 10:651–663

    Google Scholar 

  • Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213

    Google Scholar 

  • Maji VB, Sitharam TG (2008) Prediction of elastic modulus of jointed rock mass using artificial neural networks. Geotech Geol Eng 26(4):443–452

    Google Scholar 

  • Manouchehrian A, Sharifzadeh M, Hamidzadeh Moghadem R, Nouri T (2013) Selection of regression models for predicting strength and deformability properties of rocks using GA. Int J Min Sci Technol 23:495–501

    Google Scholar 

  • Manouchehrian A, Sharifzadeh M, Marji MF (2014) A bonded particle model for analysis of the flaw orientation effect on crack propagation mechanism in brittle materials under compression. Arch Civ Mech Eng 14:40–52

    Google Scholar 

  • Maruto Corporation (2006) Penetrom:eter for soft rock: model SH-70 instruction manual, Tokyo

  • Mateus J, Saavedra N, Carrillo ZC, Mateus D (2007) Correlation development between indentation parameters and unaxial compressive strength for Colombian sandstones. CT&F-Ciencia Tecnología y Futuro 3(3):125–136

    Google Scholar 

  • Matin SS, Farahzadi L, Makaremi S, Chehreh Chelgan S, Sattari GH (2018) Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Appl Soft Comput 70:980–987

    Google Scholar 

  • Mazidi MS, Haftani M, Bohloli B, Cheshomi A (2012) Measurement of uniaxial compressive strength of rocks using reconstructed cores from rock cuttings. J Pet Sci Eng 86-87:39–43

    Google Scholar 

  • Mercer J (1909) Functions of positive and negative type, and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

  • Meulenkamp F, Alvarez Grima M (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39

    Google Scholar 

  • Mishra DA, Basu A (2012) Use of the block punch test to predict the compressive and tensile strengths of rocks. Int J Rock Mech Min Sci 51:119–127

    Google Scholar 

  • Mishra DA, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 160:54–68

    Google Scholar 

  • Mohamad T, Jahed Armaghani D, Momeni E, Yazdavar AH, Ebrahim M (2018) Rock strength estimation: a PSO-based BP approach. Neural Comput and Applic 30:1635–1646

    Google Scholar 

  • Mokhtari M, Behnia M (2019) Comparison of LLNF, ANN, and COA-ANN techniques in modeling the uniaxial compressive strength and static Young’s modulus of limestone of the Dalan formation. Nat Resour Res 28(1):223–239

    Google Scholar 

  • Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Google Scholar 

  • Monjezi M, Amini Khoshalan H, Razifard M (2012) A neuro-genetic network for predicting uniaxial compressive strength of rocks. Geotech Geol Eng 30:1053–1062

    Google Scholar 

  • Moos D, Peska P, Finkbeiner T, Zoback MD (2003) Comprehensive wellbore stability analysis utilizing quantitative risk assessment. J Pet Sci Eng 38:97–110

    Google Scholar 

  • Moradian Z, Behnia M (2009) Predicting the uniaxial compressive strength and static Young's modulus of intact sedimentary rocks using the ultrasonic test. Int J Geomech 9(1):14–19

    Google Scholar 

  • Nazir R, Momeni E, Jahed Armaghani D, Mohd For M (2013) Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples. EJGE 18(1):1737–1746

    Google Scholar 

  • Nefeslioglu HA (2013) Evaluation of geo-mechanical properties of very weak and weak rock materials by using non-destructive techniques: ultrasonic pulse velocity measurements and reflectance spectroscopy. Eng Geol 160:8–20

    Google Scholar 

  • Ng K, Peiris S, Gerlach R (2014) Estimation and forecasting with logarithmic autoregressive conditional duration models: a comparative study with an application. Expert Syst Appl 41(7):3323–3332

    Google Scholar 

  • Ngan-Tillard DJM, Verwaa W, Maurenbrecher PM, Paassen LA (2009) Microstructural degradation of maastrichtian limestones. In: Proceedings of Eurock 2009, Engineering in difficult ground conditions soft rocks and karst Leiden, CRC Press, 321–326

  • Ojha VK, Mishra AA (2018) Neural tree for estimating the uniaxial compressive strength of rock materials. Springer International Publishing AG, part of Springer Nature 2018 A. Abraham et al. (Eds.): HIS 2017, AISC 734: 1–10

  • Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mech Geotech Eng 5(4):325–329

    Google Scholar 

  • Palassi M, Mojtaba Pirpanahi M (2013) Edge load strength test for indirect estimation of unconfined compressive strength. Int J Rock Mech Min Sci 61:111–117

    Google Scholar 

  • Pollak D, Gulam V, Bostjancic I (2017) A visual determination method for uniaxial compressive strength estimation based on Croatian carbonate rock materials. Eng Geol 231:68–80

    Google Scholar 

  • Rabbani E, Sharif F, Koolivand Salooki M, Moradzadeh A (2012) Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. Int J Rock Mech Min Sci 56:100–111

    Google Scholar 

  • Rasool D, Nasser ME, Zahra H (2014) Modeling of uniaxial compressive strength by genetic programming and neuro-fuzzy. J Biodivers Environ Sci (JBES) 5(2):604–611

    Google Scholar 

  • Rezaei M, Majdi A, Monjezi M (2014) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 24:233–241

    Google Scholar 

  • Rzhevsky V, Novick G (1971) The physics of rocks. Mir Publisher, Moscow, 320 p. (1971)

  • Saedi B, Mohammadi SD, Shahbazi H (2019) Prediction of uniaxial compressive strength and elastic modulus of migmatites using various modeling techniques. Arab J Geosci 11:574

    Google Scholar 

  • Samui P (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56:255–267

    Google Scholar 

  • Samui P, Dixon B (2012) Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrol Process 26(9):1361–1369

    Google Scholar 

  • Sattarib M, Gharagheizia F, Ilani-Kashkoulia P, Mohammadic AH, Ramjugernatha D (2014) Determination of the speed of sound in ionic liquids using a leastsquares support vector machine group contribution method. Fluid Phase Equilib 367:188–193

    Google Scholar 

  • Serre D (2002) Matrices theory and applications. Springer, Berlin

    Google Scholar 

  • Sharma PK, Singh TN (2008) A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bull Eng Geol Environ 67:17–22

    Google Scholar 

  • Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169

    Google Scholar 

  • Sheshde EA, Cheshomi A (2015) New method for estimating unconfined compressive strength (UCS) using small rock samples. J Pet Sci Eng 133:367–375

    Google Scholar 

  • Shorey PR, Barat D, Das MN, Mukherjee KP, Singh B (1984) Schmidt hammer rebound data for estimation of large scale in situ coal strength. Int J Rock Mech Min Sci Geomech Abstr 21:39–42

    Google Scholar 

  • Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regressio. Measurement 99:08–119

    Google Scholar 

  • Sonmez H, Tuncay E, Gokceoglu C (2004) Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara agglomerate. Int J Rock Mech Min Sci 41:717–729

    Google Scholar 

  • Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235

    Google Scholar 

  • Sridevi J (2000) Numerical modeling of jointed rocks. PhD Thesis, Department of Civil Engineering, IISc, Bangalore

    Google Scholar 

  • Strohmann T, Grudic GZ (2002) A formulation for minimax probability machine regression. Proc., Advances in Neural Information Processing System, MIT Press, Cambridge, 769–776

  • 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 38:1091–1111

    Google Scholar 

  • Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300

    Google Scholar 

  • Suykens JAK, De Brabanter J, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1–4):85–105

    Google Scholar 

  • Szwedzicki T (1998) ISRM suggested methods for determining the indentation hardness index of rock materials. Int J Rock Mech Min Sci Geomech Abstr 35:831–835

    Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192

    Google Scholar 

  • Tonnizam Mohamad E, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74:745–757

    Google Scholar 

  • 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. Arab J Geosci 8(5):2889–2897

    Google Scholar 

  • Tugrul A (2004) The effect of weathering on pore geometry and compressive strength of selected rock types from Turkey. Eng Geol 75(3–4):215–227

    Google Scholar 

  • Udagedara DT, Oguchi CT, Gunatilake JK (2017) Evaluation of geomechanical and geochemical properties in weathered metamorphic rocks in tropical environment: a case study from Samanalawewa hydropower project, Sri Lank. Geosci J 21(3):441–452

    Google Scholar 

  • Ulusay R, Gokceoglu C (1997) The modified block punch index test. Can Geotech J 34(6):991–1001

    Google Scholar 

  • Umra RK, Sharma LK, Singh R, Singh TN (2018) Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: an ANFIS predictive technique. Measurement 126:194–201

    Google Scholar 

  • Undul O, Tugrul A (2016) On the variations of geo-engineering properties of dunites and diorites related to weathering. Environ Earth Sci 75:1326

    Google Scholar 

  • Usturbelli Z (2008) Determination of weathering degree and weatherability of wall building stone constructed for environmental recreation in engineering time in Karadeniz Technical University central campus (Trabzon), Ms Thesis, Karadeniz Technical University, Department of Geological Engineering, 90p (in Turkish)

  • Van Gestel T, Suykens JAK, Baesens B, Viaene S, Vanthienen J, Dedene G, De Moor B, Vandewalle J (2004) Benchmarking least squares support vector machine classifiers. Mach Learn 54:5–32

    Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer

  • Verwaal W, Mulder A (1993) Estimating rock strength with the Equotip hardness tester. Int J Rock Mech Min Sci Geomech Abstr 30:659–662

    Google Scholar 

  • Wang Y, Aladejare AE (2015) Selection of site-specific regression model for characterization of uniaxial compressive strength of rock. Int J Rock Mech Min Sci 75:73–81

    Google Scholar 

  • Wang CW, Chau KW, Cheng CT, Qiu LA (2009) Comparison of performance of several artificial intelligience methods for forecasting monthly discharge time series. J Hydrol 374:294–306

    Google Scholar 

  • Wyering LD, Villeneuve MC, Wallis IC, Siratovich PA, Kennedy BM, Gravley DM, Cant JL (2014) Mechanical and physical properties of hydrothermally altered rocks, Taupo Volcanic Zone, New Zealand. J Volcanol Geotherm Res 288:76–93

  • Yagiz S (2009) Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using Schmidt hammer. Bull Eng Geol Environ 68:55–63

    Google Scholar 

  • Yagiz S (2011) Correlation between slake durability and rock properties for some carbonate rocks. Bull Eng Geol Environ 70:377–383

    Google Scholar 

  • Yagiz S, Sezer EA, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Methods Geomech 36:1636–1650

    Google Scholar 

  • Yamaguchi Y, Nakamura Y, Nakamura M, Hakoishi N, Yamaya M, Kato Y (2005) Verification of design strength of soft rock foundation by needle penetration testing. J Jpn Soc Eng Geol 46(1):20–27 (In Japanese)

    Google Scholar 

  • Yang L, Yang B, Jing S, Sun Q (2019) A minimax probability extreme machine framework and its application in pattern recognition. Eng Appl Artif Intell 81:260–269

    Google Scholar 

  • Yasar E, Erdogan Y (2004) Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks. Int J Rock Mech Min Sci 41:871–875

    Google Scholar 

  • 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(3):921–928

    Google Scholar 

  • Yilmaz I (2009) A new testing method for indirect determination of the unconfined compressive strength of rocks. Int J Rock Mech Min Sci 46:1349–1357

    Google Scholar 

  • Yilmaz NG (2013) The influence of testing procedures on uniaxial compressive strength prediction of carbonate rocks from equotip hardness tester (EHT) and proposal of a new testing methodology: hybrid dynamic hardness (HDH). Rock Mech Rock Eng 46:95–106

    Google Scholar 

  • Yilmaz I, Yuksek AG (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41:781–795

    Google Scholar 

  • Yilmaz I, Yuksek AG (2009) Prediction of the strength and elastic modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46:803–810

    Google Scholar 

  • Yuen KV (2010) Recent developments of Bayesian model class selection and applications in civil engineering. Struct Saf 32(5):338–346

    Google Scholar 

  • Zausa F, Civolani L (2001) Method for selecting drilling parameters. International Application Published under the Patent Cooperation Treaty, p 23

  • Zong WW, Huang G-B, Chen YQ (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242

    Google Scholar 

  • Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158

    Google Scholar 

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Authors and Affiliations

  1. Department of Mining and Mineral Extraction, Balıkesir Vocational School, Balikesir University, Balikesir, Turkey

    Nurcihan Ceryan

  2. Department of Civil Engineering, NIT Patna, Patna, Bihar, India

    Pijush Samui

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  1. Nurcihan Ceryan
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  2. Pijush Samui
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Correspondence to Nurcihan Ceryan.

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Ceryan, N., Samui, P. Application of soft computing methods in predicting uniaxial compressive strength of the volcanic rocks with different weathering degree. Arab J Geosci 13, 288 (2020). https://doi.org/10.1007/s12517-020-5273-4

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