Skip to main content
SpringerLink
Log in

A Comparison Among Some Non-linear Prediction Tools on Indirect Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Basalt

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

Basalt is among the most used rocks as aggregate, ballast, ornamental stone and for other construction purposes. Therefore, the uniaxial compressive strength (UCS) and elasticity modulus (Ei) of intact rock are required to be known for several purposes. For this reason, the purpose of the present study is to develop various non-linear prediction Model s for UCS and Ei by employing simple and non-destructive test results. Here, a dataset including 137 cases was analyzed. Each case includes unit weight, porosity, sonic velocity, Ei and UCS. The non-linear multiple regression (NLMR), adaptive-neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN) were utilized as non-linear prediction algorithms. The performances of the developed Model s were assessed using various metrics such as coefficient of correlation (R2), values account for (VAF), root mean squared error (RMSE) and a20-index. To obtain these metrics, a ranking approach was employed. When the metrics were compared, the performance of ANFIS was found slightly higher for the Model s that predict UCS. The ANN was the most successful prediction tool for the Model s predicting Ei. Also, a series of Taylor diagrams were constructed to analyze the Model performances. According to the results, the Model s using porosity and sonic velocity as input parameters for predicting UCS exhibit the highest correlation with the observed data. Regarding the Ei prediction, the Model s with three inputs have the highest performance. The results show that the investigated algorithms reveal comparable performances and the Model s developed here can be used in feasibility assessment stages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Kayabasi, A., Gokceoglu, C., Ercanoglu, M.: Estimating the deformation modulus of rock masses: A comparative study. Int. J. Rock Mech. Min. Sci. 40, 55–63 (2003)

    Article  Google Scholar 

  2. Gokceoglu, C., Sonmez, H., Kayabasi, A.: Predicting the deformation moduli of rock masses. Int. J. Rock Mech. Min. Sci. 40, 701–710 (2003)

    Article  Google Scholar 

  3. Hoek, E., Diederichs, M.S.: Empirical estimation of rock mass modulus. Int. J. Rock Mech. Min. Sci. 43, 203–215 (2006)

    Article  Google Scholar 

  4. Zhang, L., Einstein, H.H.: Using RQD to estimate the deformation modulus of rock masses. Int. J. Rock Mech. Min. Sci. 41, 337–341 (2004)

    Article  Google Scholar 

  5. Ramamurthy, T.: A geo-engineering classification for rocks and rock masses. Int. J. Rock Mech. Min. Sci. 41, 89–101 (2004)

    Article  Google Scholar 

  6. Galera, J., Alvarez, Z., Bienawski, Z.: Evaluation of the Deformation Modulus of Rock Masses: Comparison Between Pressure Meter and Dilatometer Tests with RMR Predictions. In: Gambin, M., Mestat, B. (eds.) ISP5-PRESSIO. LCPC Publication Paris, Paris (2005)

    Google Scholar 

  7. Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A., Kayabasi, A.: 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, 224–235 (2006)

    Article  Google Scholar 

  8. Shen, J., Karakus, M., Xu, C.: A comparative study for empirical equations in estimating deformation modulus of rock masses. Tunn. Undergr. Sp. Technol. 32, 245–250 (2012)

    Article  Google Scholar 

  9. Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H.A., Acikalin, S.: Prediction of uniaxial compressive strength of sandstones using petrography-based Model s. Eng. Geol. 96, 141–158 (2008)

    Article  Google Scholar 

  10. Gokceoglu, C., Zorlu, K.: 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 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Yagiz, S., Sezer, E.A., Gokceoglu, C.: 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 (2012)

    Article  Google Scholar 

  13. Yesiloglu-Gultekin, N., Gokceoglu, C., Sezer, E.A.: Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int. J. Rock Mech. Min. Sci. 62, 113–122 (2013)

    Article  Google Scholar 

  14. Yesiloglu-Gultekin, N., Sezer, E.A., Gokceoglu, C., Bayhan, H.: 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, 921–928 (2013)

    Article  Google Scholar 

  15. Cevik, A., Sezer, E.A., Cabalar, A.F., Gokceoglu, C.: Model ing of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl. Soft Comput. 11, 2587–2594 (2011)

    Article  Google Scholar 

  16. Nefeslioglu, H.A.: 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 (2013)

    Article  Google Scholar 

  17. Gül, E., Ozdemir, E., Sarıcı, D.E.: Model ing uniaxial compressive strength of some rocks from turkey using soft computing techniques. Measurement 171, 108781 (2021)

    Article  Google Scholar 

  18. Li, D., Armaghani, D.J., Zhou, J., Lai, S.H., Hasanipanah, M.: A GMDH predictive Model to predict rock material strength using three non-destructive tests. J. Nondestruct. Eval. 39, 1–14 (2020)

    Article  Google Scholar 

  19. Armaghani, D.J., Mamou, A., Maraveas, C., Roussis, P.C., Siorikis, V.G., Skentou, A.D., Asteris, P.G.: Predicting the unconfined compressive strength of granite using only two non-destructive test indexes. Geomech. Eng. 25, 317–330 (2021)

    Google Scholar 

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

    Article  Google Scholar 

  21. Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.-T., Kardani, N., Armaghani, D.J.: Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp. Geotech. 29, 100588 (2021)

    Article  Google Scholar 

  22. Koopialipoor, M., Noorbakhsh, A., Noroozi Ghaleini, E., Jahed Armaghani, D., Yagiz, S.: A new approach for estimation of rock brittleness based on non-destructive tests. Nondestruct. Test. Eval. 34, 354–375 (2019)

    Article  Google Scholar 

  23. Al-Harthi, A.A., Al-Amri, R.M., Shehata, W.M.: The porosity and engineering properties of vesicular basalt in Saudi Arabia. Eng. Geol. 54, 313–320 (1999)

    Article  Google Scholar 

  24. Moon, V., Jayawardane, J.: Geomechanical and geochemical changes during early stages of weathering of Karamu Basalt. New Zealand. Eng. Geol. 74, 57–72 (2004)

    Google Scholar 

  25. Korkanç, M., Tuğrul, A.: Evaluation of selected basalts from Niğde, Turkey, as source of concrete aggregate. Eng. Geol. 75, 291–307 (2004)

    Article  Google Scholar 

  26. Korkanç, M., Tuğrul, A.: Evaluation of selected basalts from the point of alkali–silica reactivity. Cem. Concr. Res. 35, 505–512 (2005)

    Article  Google Scholar 

  27. Gurocak, Z., Kilic, R.: Effect of weathering on the geomechanical properties of the Miocene basalts in Malatya, Eastern Turkey. Bull. Eng. Geol. Environ. 64, 373–381 (2005)

    Article  Google Scholar 

  28. Çanakcı, H., Baykasoğlu, A., Güllü, H.: Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput. Appl. 18, 1031–1041 (2009)

    Article  Google Scholar 

  29. Karakuş, A., Akatay, M.: Determination of basic physical and mechanical properties of basaltic rocks from P-wave velocity. Nondestruct. Test. Eval. 28, 342–353 (2013)

    Article  Google Scholar 

  30. Dursun, F., Topal, T.: Durability assessment of the basalts used in the Diyarbakır City Walls, Turkey. Environ. Earth Sci. 78, 1–24 (2019)

    Article  Google Scholar 

  31. Aldeeky, H., Al Hattamleh, O., Rababah, S.: Assessing the uniaxial compressive strength and tangent Young’s modulus of basalt rock using the Leeb rebound hardness test. Mater. Construcción. 70, 230 (2020)

    Article  Google Scholar 

  32. Aldeeky, H., Al Hattamleh, O.: Prediction of engineering properties of basalt rock in Jordan using ultrasonic pulse velocity test. Geotech. Geol. Eng. 36, 3511–3525 (2018)

    Article  Google Scholar 

  33. Roghanchi, P., Kallu, R.R.: Block punch index (BPI) test—a new consideration on validity and correlations for basalt and rhyolite rock types. J. Min. Sci. 50, 475–483 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Engidasew, T.A., Barbieri, G.: Geo-engineering evaluation of Termaber basalt rock mass for crushed stone aggregate and building stone from Central Ethiopia. J. Afr. Earth Sci. 99, 581–594 (2014)

    Article  Google Scholar 

  36. Kolay, E., Baser, T.: The effect of the textural characteristics on the engineering properties of the basalts from Yozgat region, Turkey. J. Geol. Soc. India 90, 102–110 (2017)

    Article  Google Scholar 

  37. Endait, M., Juneja, A.: New correlations between uniaxial compressive strength and point load strength of basalt. Int. J. Geotech. Eng. 9, 348–353 (2015)

    Article  Google Scholar 

  38. Innocenti, F., Mazzuoli, R., Pasguare, G., Radicati, F., Villari, L.: Tertiary and quaternary volcanism of the Erzurum-Kars area (Eastern Turkey), geochronological data and geodynamic evolution. J. Volconogy Geoth. Res. 13, 223–240 (1982)

    Article  Google Scholar 

  39. Aktimur, H., Tekirli, M., Yurdakul, M., Ercan, T., Keçer, M., Aktimur, S., Ürgün, B., Gürbüz, M., Can, B., Yaşar, T.: Kars, Arpaçay ve Çıldır dolayının jeolojisi. MTA Rapor No:9257. Ankara (1991)

  40. Sümengen, M.: 1/100000 Ölçekli Türkiye Jeoloji Haritaları, Kars-H50 Paftası. MTA, Ankara (2009)

  41. ASTM: American Society for Testing and Material, D7012-14e1, Standard Test Methods for Compressive Strength and Elastic Moduli of Intact Rock Core Specimens under Varying States of Stress and Temperatures. ASTM International, West Conshohocken, PA (2014)

  42. ISRM: (International Society for Rock Mechanics) The Complete ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 1974–2006. (2007)

  43. Nelson, M.M., Illingworth, W.T.: A practical guide to neural nets. (1991)

  44. Swingler, K.: Applying neural networks: a practical guide. Morgan Kaufmann (1996)

  45. Looney, C.G.: Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans. Knowl. Data Eng. 8, 211–226 (1996)

    Article  Google Scholar 

  46. SPSS, 2021: IBM SPSS Statistics v23.0. https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-2, https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-23

  47. Davis, J.: Statistics and Data Analysis in Geology. Wiley, New York (1973)

    Google Scholar 

  48. Jang, J.-S.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man. Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  49. MATLAB: http://www.mathworks.com/products/matlab/?sec=apps., (2015)

  50. Hecht-Nielsen, R.: Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on Neural Networks. pp. 11–14. IEEE Press New York (1987)

  51. Baheer, I.: Selection of methodology for Model ing hysteresis behavior of soils using neural networks. J. Comput. Aided Civ. Infrastruct. Eng. 5, 445–463 (2000)

    Article  Google Scholar 

  52. Apostolopoulour, M., Douvika, M.G., Kanellopoulos, I.N., Moropoulou, A., Asteris, P.G.: Prediction of compressive strength of mortars using artificial neural networks. In: Proceedings of the 1st international conference TMM_CH, transdisciplinary multispectral Model ling and cooperation for the preservation of cultural heritage, Athens, Greece. pp. 10–13 (2018)

  53. Taylor, K.E.: Summarizing multiple aspects of Model performance in a single diagram. J. Geophys. Res. Atmos. 106, 7183–7192 (2001)

    Article  Google Scholar 

  54. Ullah, A., Salehnia, N., Kolsoumi, S., Ahmad, A., Khaliq, T.: Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan. Ecol. Indic. 90, 569–576 (2018)

    Article  Google Scholar 

  55. Salehnia, N., Salehnia, N., Torshizi, A.S., Kolsoumi, S.: Rainfed wheat (Triticum aestivum L) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling. Ecol. Indic. 111, 105991 (2020)

    Article  Google Scholar 

  56. Gholami, H., Mohamadifar, A., Sorooshian, A., Jansen, J.D.: Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran. Atmos. Pollut. Res. 11, 1303–1315 (2020)

    Article  Google Scholar 

  57. Norouzi, R., Arvanaghi, H., Salmasi, F., Farsadizadeh, D., Ghorbani, M.A.: A new approach for oblique weir discharge coefficient prediction based on hybrid inclusive multiple Model . Flow Meas. Instrum. 76, 101810 (2020)

    Article  Google Scholar 

  58. Peck, R.B., Hanson, W.E., Thornburn, T.H.: Foundation Engineering. Wiley, New York (1953)

    Google Scholar 

  59. Shabani, E., Hayati, B., Pishbahar, E., Ghorbani, M.A., Ghahremanzadeh, M.: A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model . J. Clean. Prod. 279, 123708 (2021)

    Article  Google Scholar 

  60. Pakalidou, N., Karacosta, P.: Study of very long-period extreme precipitation records in Thessaloniki, Greece. Atmos. Res. 208, 106–115 (2018)

    Article  Google Scholar 

  61. Dehghan, S., Sattari, G.H., Chelgani, S.C., Aliabadi, M.A.: Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min. Sci. Technol. 20, 41–46 (2010)

    Google Scholar 

  62. Teymen, A., Mengüç, E.C.: Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks. Int. J. Min. Sci. Technol. 30, 785–797 (2020)

    Article  Google Scholar 

  63. Yurdakul, M., Akdas, H.: Model ing uniaxial compressive strength of building stones using non-destructive test results as neural networks input parameters. Constr. Build. Mater. 47, 1010–1019 (2013)

    Article  Google Scholar 

  64. Alemdag, S., Gurocak, Z., Gokceoglu, C.: A simple regression based approach to estimate deformation modulus of rock masses. J. Afr. Earth Sci. 110, 75–80 (2015)

    Article  Google Scholar 

  65. Aladejare, A.E.: Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests. J. Rock Mech. Geotech. Eng. 12, 256–268 (2020)

    Article  Google Scholar 

  66. Aladejare, A.E., Akeju, V.O., Wang, Y.: Probabilistic characterization of uniaxial compressive strength of rock using test results from multiple types of punch tests. Georisk Assess. Manag. Risk Eng. Syst. Geohazards 591, 1–12 (2020)

    Google Scholar 

  67. Teymen, A.: Statistical Model s for estimating the uniaxial compressive strength and elastic modulus of rocks from different hardness test methods. Heliyon 7, e06891 (2021)

    Article  Google Scholar 

  68. Umrao, R.K., Sharma, L.K., Singh, R., Singh, T.N.: Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: An ANFIS predictive technique. Measurement 126, 194–201 (2018)

    Article  Google Scholar 

  69. Armaghani, D.J., Amin, M.F.M., Yagiz, S., Faradonbeh, R.S., Abdullah, R.A.: Prediction of the uniaxial compressive strength of sandstone using various Model ing techniques. Int. J. Rock Mech. Min. Sci. 85, 174–186 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  71. Asadi, A.: Application of artificial neural networks in prediction of uniaxial compressive strength of rocks using well logs and drilling data. Procedia Eng. 191, 279–286 (2017)

    Article  Google Scholar 

  72. Ferentinou, M., Fakir, M.: An ANN approach for the prediction of uniaxial compressive strength, of some sedimentary and igneous rocks in eastern KwaZulu-Natal. Procedia Eng. 191, 1117–1125 (2017)

    Article  Google Scholar 

  73. Hassanvand, M., Moradi, S., Fattahi, M., Zargar, G., Kamari, M.: Estimation of rock uniaxial compressive strength for an Iranian carbonate oil reservoir: Model ing vs. artificial neural network application. Pet. Res. 3, 336–345 (2018)

    Google Scholar 

  74. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Salim, S.G., Ali, H.F.H., Majeed, M.K.: Artificial intelligence forecasting Model s of uniaxial compressive strength. Transp. Geotech. 27, 100499 (2021)

    Article  Google Scholar 

  75. Moussas, V.C., Diamantis, K.: Predicting uniaxial compressive strength of serpentinites through physical, dynamic and mechanical properties using neural networks. J. Rock Mech. Geotech. Eng. 13, 167–175 (2021)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  78. Mishra, D.A., Srigyan, M., Basu, A., Rokade, P.J.: Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests. Int. J. Rock Mech. Min. Sci. 100, 418–424 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  80. Alemdag, S., Gurocak, Z., Cevik, A., Cabalar, A.F., Gokceoglu, C.: Model ing deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Eng. Geol. 203, 70–82 (2016)

    Article  Google Scholar 

  81. Kahraman, S.: Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Int. J. Rock Mech. Min. Sci. 38, 981–994 (2001)

    Article  Google Scholar 

  82. Çobanoğlu, İ, Çelik, S.B.: Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bull. Eng. Geol. Environ. 67, 491–498 (2008)

    Article  Google Scholar 

  83. Yilmaz, I., Yuksek, G.: Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS Model s. Int. J. Rock Mech. Min. Sci. 46, 803–810 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  85. Singh, R., Kainthola, A., Singh, T.N.: Estimation of elastic constant of rocks using an ANFIS approach. Appl. Soft Comput. 12, 40–45 (2012)

    Article  Google Scholar 

  86. Beiki, M., Majdi, A., Givshad, A.D.: 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 (2013)

    Article  Google Scholar 

  87. Armaghani, D.J., Mohamad, E.T., Momeni, E., Narayanasamy, M.S.: 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 (2015)

    Article  Google Scholar 

  88. Fang, Q., Yazdani Bejarbaneh, B., Vatandoust, M., Jahed Armaghani, D., Ramesh Murlidhar, B., Tonnizam Mohamad, E.: Strength evaluation of granite block samples with different predictive Model s. Eng. Comput. 37, 891–908 (2021)

    Article  Google Scholar 

  89. Google Earth: http://www.google.com/intl/tr/earth/index.html, (2021)

Download references

Acknowledgements

The authors gratefully thank H. Serkan Tezer for field and laboratory studies. The authors also would like to thank Zeynel Abidin Gök and Serkan Pişmiş for laboratory studies.

Author information

Authors and Affiliations

  1. Baskent Chamber of Industry Organized Industrial Zone Vocational School, Department of Construction, Construction Technology Program, Hacettepe University, Sincan, Ankara, Turkey

    N. Yesiloglu-Gultekin

  2. Department of Geological Engineering, Hacettepe University, 06800, Beytepe, Ankara, Turkey

    C. Gokceoglu

Authors
  1. N. Yesiloglu-Gultekin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Gokceoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Gokceoglu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yesiloglu-Gultekin, N., Gokceoglu, C. A Comparison Among Some Non-linear Prediction Tools on Indirect Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Basalt. J Nondestruct Eval 41, 10 (2022). https://doi.org/10.1007/s10921-021-00841-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-021-00841-2

Keywords

Search

Navigation