A comparative analysis data mining tools predicting strength parameters of rocks by point load index

Abdelhedi, M., Jabbar, R., Said, A. Ben, Fetais, N., Abbes, C., 2023. Machine learning for prediction of the uniaxial compressive strength within carbonate rocks. Earth Science Informatics, 16(2): 1473-1487.
Afolagboye, L. O., Ajayi, D. E., Afolabi, I. O., 2023. Machine learning models for predicting unconfined compressive strength: A case study for Precambrian basement complex rocks from Ado-Ekiti, Southwestern Nigeria. Scientific African, 20: e01715. https://doi.org/10.1016/J.SCIAF.2023. E01715
Akbay, D., 2018. Designing a new testing apparatus for preventing the errors in point load index test. Ph.D. thesis, Süleyman Demirel University.
Aksoy, C. O., Kantarci, O., Ozacar, V., 2010. An example of estimating rock mass deformation around an underground opening using numerical modeling. International Journal of Rock Mechanics and Mining Sciences, 47: 272-278. https://doi.org/10.1016/j.ijrmms.2009.12.001
Aladejare, A. E. 2020., Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests. Journal of Rock Mechanics and Geotechnical Engineering, 12(2): 256-268. https://doi.org/10.1016/J.JRMGE.2019.08.001
Altındağ, R., 2000. Kayaçlarda kırılma tokluğu ve diğer mekanik özellikler arasındaki ilişkiler. DEÜ Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 2(2): 39-47.
Andrea, D. V. D., Fischer, R. L., Fogelson, D. E., 1965. Prediction of Compressive Strength From Other Rock Properties (United States Department Interior, Bureau of Mines) (Issue 6702).
Aoki, H., Matsukura, Y., 2008. Estimating the unconfined compressive strength of intact rocks from Equotip hardness. Bulletin of Engineering Geology and the Environment, 67: 23-29.
Armaghani, D. J., For, M., Amin, M., Yagiz, S., Faradonbeh, R. S., Abdullah, R. A., 2016. Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. International Journal of Rock Mechanics and Mining Sciences, 85: 174-186. https://doi.org/10.1016/j.ijrmms.2016.03.018
Aston, T. R. C., MacIntyre, J. S., Kazi, H. A., 1991. The effect of worn and chipped points on point load indices. Mining Science and Technology, 13: 69-74.
Balcı, C., Bilgin, N., 2005. Mekanize kazı makinalarının seçiminde küçük ve tam boyutlu kazı deneylerinin karşılaştırılması. Itüdergisi/d Mühendislik, 4(3): 76-86.
Bansal, T., Talakokula, V., Saravanan, T. J., 2023. Comparative study of machine learning methods to predict compressive strength of high-performance concrete and model validation on experimental data. Asian Journal of Civil Engineering, 25(2): 1195-1206.
Barzegar, R., Sattarpour, M., Deo, R., Fijani, E., Adamowski, J., 2020. An ensemble tree-based machine learning model for predicting the uniaxial compressive strength of travertine rocks. Neural Computing and Applications, 32: 9065-9080.
Basarir, H., Karpuz, C., 2004. A rippability classification system for marls in lignite mines. Engineering Geology, 74(3-4): 303-318.
Basu, A., Kamran, M., 2010. Point load test on schistose rocks and its applicability in predicting uniaxial compressive strength. International Journal of Rock Mechanics and Mining Sciences, 47(5): 823-828.
Benavente, D., Fort, R., Gomez-Heras, M., 2021. Improving uniaxial compressive strength estimation of carbonate sedimentary rocks by combining minimally invasive and non-destructive techniques. International Journal of Rock Mechanics Mining Sciences, 147: 1365-1609.
Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B., 2008. KNIME: The Konstanz information miner. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 319-326). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-78246-9_38/COVER
Bieniawski, Z. T., 1975. The point-load test in geotechnical practice. Engineering Geology, 9(1): 1-11.
Broch, E., Franklin, J. A., 1972. The Point-Load Strength Test. International Journal of Rock Mechanics and Mining Sciences, 9: 669-697.
Capik, M., Yilmaz, A. O., Yasar, S., 2017. Relationships between the drilling rate index and physicomechanical rock properties. Bulletin of Engineering Geology and the Environment, 76(1): 253-261.
Cargill, J. S., Shakoor, A., 1990. Evaluation of empirical methods for measuring the uniaxial compressive strength of rock. International Journal of Rock Mechanics and Mining Sciences Geomechanics Abstracts, 27(6): 495-503.
Chai, T., Draxler, R. R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3): 1247-1250.
Dehghan, S., Sattari, G., Chehreh Chelgani, S., Aliabadi, M. A., 2010. Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Science and Technology (China), 20(1): 41-46.
Erdal, H. I., Karakurt, O., Namli, E., 2013. High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence, 26(4): 1246-1254.
Fadhil, A. I., Al-Adly, A. I. F., Fattah, M. Y., 2023. Estimation of uniaxial compressive and indirect tensile strengths of intact rock from Schmidt hammer rebound number. Journal of the Mechanical Behavior of Materials, 32: 20220255. https://doi.org/10.1515/JMBM-2022-0255
Fakir, M., Ferentinou, M., Misra, S., 2017. An Investigation into the Rock Properties Influencing the Strength in Some Granitoid Rocks of KwaZulu-Natal, South Africa. Geotechnical and Geological Engineering, 35(3): 1119-1140.
Fereidooni, D., Khajevand, R., 2018. Correlations Between Slake-Durability Index and Engineering Properties of Some Travertine Samples Under Wetting-Drying Cycles. Geotechnical and Geological Engineering, 36: 1071-1089.
Ferentinou, M., Fakir, M., 2017. An ANN Approach for the Prediction of Uniaxial Compressive Strength, of Some Sedimentary and Igneous Rocks in Eastern KwaZulu-Natal. Procedia Engineering, 191: 1117-1125.
Frank, E., Hall, M. A., Witten, I. H., 2016. The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques” (I. H. Witten, E. Frank, M. A. Hall, C. J. Pal, Eds.; Fourth Edition). Morgan Kaufmann, 558 pp.
Gao, H., Wang, Q., Jiang, B., Zhang, P., Jiang, Z., Wang, Y., 2021. Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method. International Journal of Coal Science and Technology, 8(4): 605-613.
Ghobadi, M. H., Babazadeh, R., 2015. Experimental Studies on the Effects of Cyclic Freezing-Thawing, Salt Crystallization, and Thermal Shock on the Physical and Mechanical Characteristics of Selected Sandstones. Rock Mechanics and Rock Engineering, 48(3): 1001-1016.
Gokceoglu, C., Zorlu, K., 2004. A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence, 17(1): 61-72.
Guan, S., Cao, R., Zhong, Y., Nan, H., Wu, F., 2024. Comparison and combination of Leeb hardness and point load strength for indirect measuring tensile and compressive strength of rocks. Bulletin of Engineering Geology and the Environment, 83(4): 1-13.
Gunsallus, K. L., Kulhawy, F. H., 1984. A comparative evaluation of rock strength measures. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 21(5): 233-248.
Hassan, M. Y., Arman, H., 2023. HYFIS vs FMR, LWR and Least squares regression methods in estimating uniaxial compressive strength of evaporitic rocks. Scientific Reports, 13: 14101. https://doi.org/10.1038/s41598-023-41349-1
Heidari, M., Khanlari, G. R., Kaveh, M. T., Kargarian, S., 2012. Predicting the uniaxial compressive and tensile strengths of gypsum rock by point load testing. Rock Mechanics and Rock Engineering, 45(2): 265-273.
Heidari, M., Mohseni, H., Jalali, S. H., 2018. Prediction of Uniaxial Compressive Strength of Some Sedimentary Rocks by Fuzzy and Regression Models. Geotechnical and Geological Engineering, 36(1): 401-412. https://doi.org/10.1007/S10706-017-0334-5/FIGURES/6
Heidari, M., Momeni, A. A., Naseri, F., 2013. New weathering classifications for granitic rocks based on geomechanical parameters. Engineering Geology, 166: 65-73.
Hemlata, Gulia, P., 2019. Experimental evaluation of open source data mining tools: R, rapid miner and knime. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4133-4144. https://doi.org/10.35940/IJITEE.A5341.119119
Hendrickx, K., Perini, L., Van der Plas, D., Meert, W., Davis, J., 2021. Machine Learning with a Reject Option: A survey. Machine Learning 2024, 1-38.
Hodson, T. O., 2022. Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14): 5481-5487. https://doi.org/10.5194/gmd-15- 5481-2022
Hofmann, M., Klinkenberg, R., 2013. RapidMiner: Data Mining Use Cases and Business Analytics Applications. In CRC Press. RC Press, 528 pp.
Huang, L., Asteris, P. G., Koopialipoor, M., Armaghani, D. J., Tahir, M. M., 2019. Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Applied Sciences (Switzerland), 9: 5372. https://doi.org/10.3390/APP9245372
Ibrahim, A. F., Hiba, M., Elkatatny, S., Ali, A., 2024. Estimation of tensile and uniaxial compressive strength of carbonate rocks from well-logging data: artificial intelligence approach. Journal of Petroleum Exploration and Production Technology, 14: 317-329.
İnce, İ., Bozdağ, A., Fener, M., Kahraman, S., 2019. Estimation of uniaxial compressive strength of pyroclastic rocks (Cappadocia, Turkey) by gene expression programming. Arabian Journal of Geosciences, 12: 756. https://doi.org/10.1007/s12517-019-4953-4
ISRM, 2007. The complete suggested methods for rock characterization, testing and monitoring: 1974-2006 (U. R. H. J.A., Eds.). Springer, 293 pp.
Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., Yagiz, S., Motaghedi, H., 2016. Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Engineering with Computers, 32(2): 189-206. https://doi.org/10.1007/S00366-015-0410-5/FIGURES/12
Jamshidi, A., Abdi, Y., Sarikhani, R., 2020. Prediction of Brittleness Indices of Sandstones Using a Novel Physico-Mechanical Parameter. Geotechnical and Geological Engineering, 38: 4651-4659. https://doi.org/10.1007/s10706-020-01316-3
Jamshidi, A., Nikudel, M. R., Khamehchiyan, M., Zarei Sahamieh, R., Abdi, Y., 2016. A correlation between P-wave velocity and Schmidt hardness with mechanical properties of travertine building stones. Arabian Journal of Geosciences, 9(10): 568. https://doi.org/10.1007/s12517-016-2542-3
Javadpour, L., 2022. Using RapidMiner for executing queries and visualization in a traditional database course. Journal of Education for Business, 97(4): 247-252. https://doi.org/10.1080/08832323.2021. 1924106
Jin, X., Zhao, R., Ma, Y., 2022. Application of a Hybrid Machine Learning Model for the Prediction of Compressive Strength and Elastic Modulus of Rocks. Minerals, 12(12): 1506. https://doi.org/10.3390/ min12121506
Jing, H., Nikafshan Rad, H., Hasanipanah, M., Jahed Armaghani, D., Qasem, S. N., 2021. Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS. Engineering with Computers, 37(4): 2717-2734.
Jović, A., Brkić, K., Bogunović, N., 2014. An overview of free software tools for general data mining, 26-30 May, Opatija, Croatia. 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1112-1117.
Kahraman, S., Balci, C., Yazici, S., Bilgin, N., 2000. Prediction of the penetration rate of rotary blast hole drills using a new drillability index. International Journal of Rock Mechanics and Mining Sciences, 37(5): 729-743.
Karaman, K., Kesimal, A., 2012. Kayaçların Tek Eksenli Basınç Dayanımı Tahmininde Nokta Yükü Deney Yöntemleri ve Porozitenin Değerlendirilmesi. Madencilik, 51(4): 3-14.
Karaman, K., Kesimal, A., 2015. A comparative study of Schmidt hammer test methods for estimating the uniaxial compressive strength of rocks. Bulletin of Engineering Geology and the Environment, 74: 507-520.
Kayabali, K., Selcuk, L., 2009. Nail penetration test for determining the uniaxial compressive strength of rock. International Journal of Rock Mechanics and Mining Sciences, 47: 265-271. https://doi.org/10.1016/j.ijrmms.2009.09.010
Khajevand, R., Fereidooni, D., 2018. Assessing the empirical correlations between engineering properties and P wave velocity of some sedimentary rock samples from Damghan, northern Iran. Arabian Journal of Geosciences, 11: 528. https://doi.org/10.1007/s12517-018-3810-1
Khanlari, G., Rafiei, B., Abdilor, Y., 2015. Evaluation of strength anisotropy and failure modes of laminated sandstones. Arabian Journal of Geosciences, 8(8): 3089-3102.
Kılıç, A., Teymen, A., 2008. Determination of mechanical properties of rocks using simple methods. Bulletin of Engineering Geology and the Environment, 67(2): 237-244. https://doi.org/10.1007/ s10064-008-0128-3
Kolapo, P., Munemo, P., 2021. Investigating the correlations between point load strength index, uniaxial compressive strength and Brazilian tensile strength of sandstones. A case study of QwaQwa sandstone deposit. International Journal of Mining and Mineral Engineering, 12(1): 67-83.
Lai, G. T., Rafek, A. G., Serasa, A. S., Hussin, A., Ern, L. K., 2016. Use of Ultrasonic Velocity Travel Time to Estimate Uniaxial Compressive Strength of Granite and Schist in Malaysia. Sains Malaysiana, 45(2): 185-193.
Lashkaripour, G. R., 2002. A statistical investigation on mudrocks. ISRM Regional Symposioum - Advancing Rock Mechanics Frontiers to Meet the Challenges of 21st Century, 1179-1184.
Lausch, A., Schmidt, A., Tischendorf, L., 2015. Data mining and linked open data - New perspectives for data analysis in environmental research. Ecological Modelling, 295: 5-17. https://doi.org/10.1016/j.ecolmodel.2014.09.018
Madhubabu, N., Singh, P. K., Kainthola, A., Mahanta, B., Tripathy, A., Singh, T. N., 2016. Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement: Journal of the International Measurement Confederation, 88: 202-213. https://doi.org/10.1016/j.measurement .2016.03.050
Mahdiabadi, N., Khanlari, G., 2019. Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis. Periodica Polytechnica Civil Engineering, 63(1): 104-114. https://doi.org/ 10.3311/PPCI.13035
Mahdiyar, A., Armaghani, D. J., Marto, A., Nilashi, M., Ismail, S., 2019. Rock tensile strength prediction using empirical and soft computing approaches. Bulletin of Engineering Geology and the Environment, 78(6): 4519-4531.
Mahmoodzadeh, A., Mohammadi, M., Hashim Ibrahim, H., Nariman Abdulhamid, S., Ghafoor Salim, S., Farid Hama Ali, H., Kamal Majeed, M., 2021. Artificial intelligence forecasting models of uniaxial compressive strength. Transportation Geotechnics, 27: 100499. https://doi.org/10.1016/ j.trgeo.2020.100499
Masoumi, H., Horne, J., Timms, W., 2017. Establishing Empirical Relationships for the Effects of Water Content on the Mechanical Behavior of Gosford Sandstone. Rock Mechanics and Rock Engineering, 50(8): 2235-2242.
Matin, S. S., Farahzadi, L., Makaremi, S., Chelgani, S. C., Sattari, G., 2018. Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Applied Soft Computing, 70: 980-987. https://doi.org/10.1016/J.ASOC.2017.06.030
Minaeian, B., Ahangari, K., 2013. Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arabian Journal of Geosciences, 6: 1925-1931. https://doi.org/10.1007/s12517-011-0460-y
Minaeian, B., Ahangari, K., 2017. Prediction of the uniaxial compressive strength and Brazilian tensile strength of weak conglomerate. International Journal of Geo-Engineering, 8: 19. https://doi.org/10.1186/s40703-017-0056-9
Mishra, D. A., Basu, A., 2012. Use of the block punch test to predict the compressive and tensile strengths of rocks. International Journal of Rock Mechanics and Mining Sciences, 51: 119-127. https://doi.org/10.1016/j.ijrmms.2012.01.016
Mishra, D. A., Basu, A., 2013. Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Engineering Geology, 160: 54-68. https://doi.org/10.1016/j.enggeo.2013.04.004
Mohamad, E. T., Jahed Armaghani, D., Momeni, E., Alavi Nezhad Khalil Abad, S. V., 2015. Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74(3): 745-757. https://doi.org/10.1007/S10064-014- 0638-0/TABLES/6
Momeni, E., Jahed Armaghani, D., Hajihassani, M., Mohd Amin, M. F., 2015. Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement, 60: 50-63. https://doi.org/10.1016/J.MEASUREMENT.2014.09.075 Moussas, V. C., Diamantis, K., 2021. Predicting uniaxial compressive strength of serpentinites through physical, dynamic and mechanical properties using neural networks. Journal of Rock Mechanics and Geotechnical Engineering, 13(1): 167-175. https://doi.org/10.1016/J.JRMGE.2020.10.001
Pyle, D., 1999. Data Preparation for Data Mining (Diane D. Cerra, Ed.). Morgan Kaufmann, 393 pp. Ristoski, P., Bizer, C., Paulheim, H., 2015. Mining the Web of Linked Data with RapidMiner. Web Semantics: Science, Services and Agents on the World Wide Web, 35: 142-151. https://doi.org/10.1016/j.websem.2015.06.004
Saedi, B., Mohammadi, S. D., Shahbazi, H., 2018. Prediction of uniaxial compressive strength and elastic modulus of migmatites using various modeling techniques. Arabian Journal for Science and Engineering, 11: 574. https://doi.org/10.1007/s12517-018-3912-9
Saedi, B., Seyed, D. M., Shahbazi, H., 2019. Application of fuzzy inference system to predict uniaxial compressive strength and elastic modulus of migmatites. Environmental Earth Sciences, 78. 208. https://doi.org/10.1007/s12665-019-8219-y
Sarkar, K., Tiwary, A., Singh, T. N., 2010. Estimation of strength parameters of rock using artificial neural networks. Bulletin of Engineering Geology and the Environment, 69: 599-606. https://doi.org/10.1007/s10064-010-0301-3
Singh, R., Umrao, R. K., Ahmad, M., Ansari, M. K., Sharma, L. K., Singh, T. N., 2017. Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement, 99: 108-119. https://doi.org/10.1016/j.measurement.2016.12.023
Tahir, M., Mohammad, N., Din, F., 2011. Strength parameters and their inter-relationship for limestone of Cherat and Kohat areas of Khyber Pakhtunkhwa. Journal of Himalayan Earth Sciences, 44(2): 45- 51.
Teymen, A., Mengüç, E. C., 2020. Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks. International Journal of Mining Science and Technology Volume 30(6): 785-797. https://doi.org/10.1016/j.ijmst.2020.06.008 The University of Waikato, 2024, March 14. https://www.cs.waikato.ac.nz/ml/weka/.
Török, Á., Czinder, B., 2017. Relationship between density, compressive strength, tensile strength and aggregate properties of andesites from Hungary. Environmental Earth Sciences, 76: 639. https://doi.org/10.1007/s12665-017-6977-y
Tripathy, A., Singh, T. N., Kundu, J., 2015. Prediction of abrasiveness index of some Indian rocks using soft computing methods. Measurement, 68: 302-309. https://doi.org/10.1016/j.measurement .2015.03.009
Tuğrul, A., Zarif, I. H., 1999. Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey. Engineering Geology, 51(4): 303-317. https://doi.org/10.1016/S0013-7952(98)00071-4
Ulusay, R., Tureli, K., Ider, M. H., 1994. Prediction of engineering properties of a selected litharenite sandstone from its pet- rographic characteristics using correlation and multivariate statistical techniques. Engineering Geology, 37, 135-157.
Villarroya, S., Baumann, P., 2023. A survey on machine learning in array databases. Applied Intelligence, 53(9): 9799-9822. https://doi.org/10.1007/S10489-022-03979-2/FIGURES/4
Wang, M., Wan, W., 2019. A new empirical formula for evaluating uniaxial compressive strength using the Schmidt hammer test. International Journal of Rock Mechanics and Mining Sciences, 123: 104094. https://doi.org/10.1016/j.ijrmms.2019.104094
Wang, M., Xu, W., Chen, D., Li, J., Mu, H., Mi, J., Wu, Y., 2022. Summary of the Transformational Relationship between Point Load Strength Index and Uniaxial Compressive Strength of Rocks. Sustainability 14(19): 12456. MDPI. https://doi.org/10.3390/su141912456
Willmott, C. J., Matsuura, K., 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1): 79-82. https://doi.org/10.3354/CR030079
Yarali, O., Soyer, E., 2013. Assessment of relationships between drilling rate index and mechanical properties of rocks. Tunnelling and Underground Space Technology, 33: 46-53. https://doi.org/ 10.1016/j.tust.2012.08.010
Yenice, H., 2002. Bazı Kayaçların Tek Eksenlı̇ Basınç Dayanımları ile Dı̇ğer Malzeme Özellı̇klerı̇ Arasındaki İlı̇şkı̇ler. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 4(2): 65-71.
Yesiloglu-Gultekin, N., Gokceoglu, C., Sezer, E. A., 2013. Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. International Journal of Rock Mechanics and Mining Sciences, 62: 113-122. https://doi.org/10.1016/ j.ijrmms.2013.05.005
Yilmaz, I., 2009. A new testing method for indirect determination of the unconfined compressive strength of rocks. International Journal of Rock Mechanics and Mining Sciences, 46(8): 1349-1357. https://doi.org/10.1016/j.ijrmms.2009.04.009
Yılmaz, I., Sendir, H., 2002. Correlation of Schmidt hardness with unconfined compressive strength and Young’s modulus in gypsum from Sivas (Turkey). Engineering Geology, 66(3-4): 211-219. https://doi.org/10.1016/S0013-7952(02)00041-8
Yılmaz, I., Yuksek, A. G., 2008. An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mechanics and Rock Engineering, 41(5): 781-795. https://doi.org/10.1007/s00603-007-0138-7
Yilmaz, I., Yuksek, G., 2009. Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. International Journal of Rock Mechanics and Mining Sciences, 46(4): 803-810. https://doi.org/10.1016/J.IJRMMS.2008.09.002
Yoon, J. H., Kim, D. J., Koo, Y. Y., 2023. Novel Fuzzy Correlation Coefficient and Variable Selection Method for Fuzzy Regression Analysis Based on Distance Approach. International Journal of Fuzzy Systems, 25(8): 2969-2985. https://doi.org/10.1007/s40815-023-01546-6