Estimating Uniaxial Compressive Strength of Sedimentary Rocks with Leeb hardness Using SVM Regression Analysis and Artificial Neural Networks

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Gökhan Ekincioğlu , Deniz Akbay , Serkan Keser

https://doi.org/10.2339/politeknik.1475944

Öz

Uniaxial compressive strength (UCS) of rock materials is a rock property that should be determined for the design and stability of structures before underground and aboveground engineering projects. However, it is impossible to determine the properties of rocks such as UCS directly due to the lack of standardized sample preparation, necessary equipment, etc. In this case, the UCS of rocks is estimated by index test methods such as hardness, ultrasound velocity, etc. Determining the hardness of rocks is relatively more practical, fast, and inexpensive than other properties. In this study, the UCS of sedimentary rocks was estimated as a function of Leeb hardness using artificial neural networks (ANN) and SVM regression analysis. With the proposed neural network and SVM regression models, it is aimed to obtain more accurate and faster prediction values. To better train the models created in the study, the number of data was increased by compiling data from the studies in the literature. The UCS values predicted by the models obtained with two different methods and the measured UCS values were statistically compared. It was proved that the models created with ANN and SVM regression can be used reliably in predicting UCS values.

Anahtar Kelimeler

Leeb hardness, uniaxial compressive strength, sedimentary rocks, artificial neural network, SVM regression

Etik Beyan

The author of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission.

Sedimanter Kayaçların Tek Eksenli Basınç Dayanımının Leeb Sertliği Kullanılarak Yapay Sinir Ağları ve SVM Regresyon Analizi ile Tahmin Edilmesi

Öz

Kayaların tek eksenli basınç dayanımı (UCS), yeraltı ve yerüstü mühendislik projelerinden önce yapıların tasarımı ve stabilitesi için belirlenmesi gereken bir kaya özelliğidir. Bununla birlikte, standartlaştırılmış numune hazırlama, gerekli ekipman vb. eksikliklerden dolayı kayaların UCS gibi özelliklerine doğrudan belirlemem mümkün olmamaktadır. Bu durumda, kayaçların UCS'si sertlik, ultrases hızı gibi indeks test yöntemleri ile tahmin edilir. Kayaçların sertliğini belirlemek diğer özelliklere göre nispeten daha pratik, hızlı ve ucuzdur. Bu çalışmada, sedimanter kayaçların UCS'si yapay sinir ağları (ANN) ve SVM regresyon analizi kullanılarak Leeb sertliğinin bir fonksiyonu olarak tahmin edilmiştir. Önerilen yapay sinir ağı ve SVM regresyon modelleri ile daha doğru ve hızlı tahmin değerleri elde edilmesi amaçlanmıştır. Çalışmada oluşturulan modellerin daha iyi eğitilmesi için literatürdeki çalışmalardan veriler derlenerek veri sayısı artırılmıştır. İki farklı yöntemle elde edilen modellerin tahmin ettiği UCS değerleri ile ölçülen UCS değerleri istatistiksel olarak karşılaştırılmıştır. ANN ve SVM regresyonu ile oluşturulan modellerin UCS değerlerini tahmin etmede güvenilir bir şekilde kullanılabileceği ortaya konmuştur.

Anahtar Kelimeler

Leeb sertliği, tek eksenli basınç dayanımı, sedimanter kayaçlar, yapay sinir ağı, SVM regresyonu

Kaynakça

• [1] Shalabi F. I., Cording E. J. and Al-Hattamleh O.H., “Estimation of rock engineering properties using hardness tests”, Engineering Geology, 90(3–4): 138–147, (2007).
• [2] Çelik S. B., Çobanoğlu İ. and Koralay T., “Investigation of the use of LEEB HARDNESS in the estimation of some physical and mechanical properties of rock materials, Pamukkale University Journal of Engineering, 26(8): 1385-1392, (2020).
• [3] Siegesmund S. and Dürrast H., “Physical and mechanical properties of rocks”, Stone in architecture, properties, durability, Springer, Berlin, (2014).
• [4] Çelik M. Y., Yeşilkaya L., Ersoy M. and Turgut T., “Karbonat kökenlı̇ doğaltaşlarda tane boyu ı̇le knoop sertlı̇k değerı̇ arasindaki ı̇lı̇şkı̇nı̇n ı̇ncelenmesi̇”, Madencilik, 50(2): 29–40, (2011).
• [5] Atkinson R. H., “Hardness test for rock characterization”, Comprehensive rock engineering: principles, practice and projects. Rock testing and site characterization, Pergamon, Oxford, (1993).
• [6] Leeb D., “Dynamic hardness testing of metallic materials”, NDT International, 12(6): 274–278, (1979).
• [7] Wilhelm K., Viles H. and Burke O., “Low impact surface hardness testing (Equotip) on porous surfaces – advances in methodology with implications for rock weathering and stone deterioration research”, Earth Surface Processes and Landforms, 41:1027–1038, (2016).
• [8] Kompatscher M., “Equotip—rebound hardness testing after D.Leeb.” Conference on hardness measurements theory and application in laboratories and industries, Washington, DC, USA, 66–72, (2004).
• [9] Çelik S. B., Çobanoğlu İ., and Koralay T., “Investigation of the Use of LEEB HARDNESS in the Prediction of Uniaxial Compressive Strength of Rock Materials”, ENGGEO’2019: National Symposium on Engineering Geology and Geotechnics, Denizli, Türkiye, 47-55, (2019).
• [10] Proceq SA, “Equotip 3, Portable Hardness Tester, Operating instructions”, Proceq SA, Schwerzenbach, Switzerland, (2007).
• [11] Güneş Yılmaz N., “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 Mechanics and Rock Engineering, 46(1): 95-106, (2013).
• [12] Hack H. R. G. K., Hingira J. and Verwaal, W. “Determination of discontinuity wall strength by Equotip and ball rebound tests”, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 30: 151-151, (1993).
• [13] Verwaal W. and Mulder A. “Estimating rock strength with the Equotip hardness tester”, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 30(6): 659-662, (1993).
• [14] Meulenkamp F. and Grima M. A., “Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness”, International Journal of Rock Mechanics and Mining Sciences, 36(1): 29-39, (1999).
• [15] Kawasaki S., Tanimoto C., Koizumi K. and Ishikawa M., “An attempt to estimate mechanical properties of rocks using the Equotip hardness tester”, Journal of the Japanese Society of Engineering Geology, 43(4): 244-248, (2002).
• [16] Aoki and Matsukura, Estimating the unconfined compressive strength of intact rocks from Equotip hardness, Bulletin of Engineering Geology and the Environment, 67: 23-29., (2008).
• [17] Lee J. S., Smallwood L. and Morgan E., “New Application of Rebound Hardness Numbers to generate Logging of Unconfined Compressive Strength in Laminated Shale Formations”, 48th U.S. Rock Mechanics/Geomechanics Symposium, Minneapolis, Minnesota, 972–978, (2014).
• [18] Mol L., “Measuring rock hardness in the field”, Geomorphological Techniques, British Society for Geomorphology, London, (2015).
• [19] Asiri Y., Corkum A. G. and El Naggar H., “Leeb Hardness test for UCS estimation of sandstone”, The 69th Canadian Geotechnical Conference, Vancouver, British Columbia, Canada 1- 11, (2016).
• [20] Asiri Y., “Standardized process for field estimation of unconfined compressive strength using Leeb Hardness”, MSc Thesis, Dalhousie University, Applied Science, (2017).
• [21] Su O. and Momayez M., “Correlation between Equotip hardness index, mechanical properties and drillability of rocks”, Dokuz Eylul University Journal of Science and Engineering, 19(56): 519-531, (2017).
• [22] Corkum A. G., Asiri Y., El Naggar H. and Kinakin D., “The LEEB HARDNESS test for rock: An updated methodology and UCS correlation”, Rock Mechanics and Rock Engineering, 51: 665-675, (2018).
• [23] Güneş Yilmaz N. and Göktan, R. M., “Analysis of the LEEB HARDNESS test data obtained by using two different rock core holders”, Süleyman Demirel University Journal of Natural and Applied Sciences, 22(1): 24-31, (2018).
• [24] Güneş Yilmaz N. and Göktan, R. M., “Comparison and combination of two NDT methods with implications for compressive strength evaluation of selected masonry and building stones”, Bulletin of Engineering Geology and the Environment, 78(6): 4493-4503, (2019).
• [25] Çelik S. F. and Çobanoğlu İ., “Comparative investigation of HS, HSR, and LEEB HARDNESS tests in the characterization of rock materials”, Environmental Earth Sciences, 78(554): 1-16, (2019).
• [26] Çelik S. B., Çobanoğlu İ. and Koralay T. “Investigation of the use of LEEB HARDNESS in the estimation of some physical and mechanical properties of rock materials”, Pamukkale University Journal of Engineering Sciences, 26(8): 1385-1392, (2020).
• [27] Akbay D., Ekincioğlu G., Altındağ R. and Şengün N., “Farklı cihaz ve yöntemler ile belirlenen HS sertlik değerlerinin sedimanter kayaçların gevreklik değerlerinin tahmininde kullanılabilirliğinin incelenmesi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(3): 441-448, (2021).
• [28] Kriegeskorte N., “Deep neural networks: a new framework for modelling biological vision and brain information processing”, Annual Review of Vision Science, 1: 417-446, (2015).
• [29] Nguyen H. H. and Chan, C. W., “Multiple neural networks for a long term time series forecast”, Neural Computing & Applications, 13: 90-98, (2004).
• [30] Kanti K. M. and Rao P. S. “Prediction of bead geometry in pulsed GMA welding using back propagation neural network”, Journal of Materials Processing Technology, 200(1-3): 300-305, (2008).
• [31] Basheer I. A. and Hajmeer M., “Artificial neural networks: fundamentals, computing, design, and application”, Journal of Microbiological Methods, 43(1): 3-31, (2000).
• [32] Shehab M., Abualigah L., Shambour Q., Abu-Hashem M. A., Shambour M. K. Y., Alsalibi A. I. and Gandomi A. H., “Machine learning in medical applications: A review of state-of-the-art methods”, Computers in Biology and Medicine, 145: 105458, (2022).
• [33] Balabin R. M. and Lomakina E. I. “Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data”, Analyst, 136(8): 1703-1712, (2011).
• [34] Zhang X., Hu L. and Zhang, L., “An efficient multiple kernel computation method for regression analysis of economic data”, Neurocomputing, 118: 58-64, (2013).
• [35] Hindman M., “Building better models: Prediction, replication, and machine learning in the social sciences”, The Annals of the American Academy of Political and Social Science, 659(1): 48-62, (2015).
• [36] Vapnik V., “The Nature of Statistical Learning Theory”, Springer, New York, (1995).
• [37] Platt J., “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Technical Report MSR-TR-98–14, (1999).
• [38] Fan R. E., Chen P. H. and Lin C. J., "A Study on SMO-Type Decomposition Methods for Support Vector Machines", IEEE Transactions on Neural Networks, 17: 893–908, (2006).
• [39] Kecman V., “Support vector machines–an introduction”, Support vector machines: theory and applications, Springer, Heidelberg, (2005).