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Smart Estimation of Sandstones Mechanical Properties Based on Thin Section Image Processing Techniques

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Abstract

Rock strength parameters such as uniaxial compressive strength and modulus of elasticity are crucial parameters in designing rock engineering structures. Owing to the importance of the aforementioned parameters, in this paper, image processing technique is coupled with artificial neural network (ANN) method for assessing the uniaxial compressive strength and modulus of elasticity of sandstones. For this reason, 102 core sandstone samples were prepared. Subsequently petrographic analyses and imaging operation for 102 images were performed. Principal component analysis was then conducted for feature reduction purposes. At last, an ANN model, which received its input data from image processing technique, was constructed for assessing the UCS and E of sandstone samples. Overall, the best performance of the network was obtained when 10 hidden nodes were used. The correlation coefficient (R) values of 0.9722 and 0.97062 for UCS and E, respectively, suggest the feasibility of the proposed model.

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References

  1. 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 

  2. Minaeian, B., Ahangari, K.: Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arab. J. Geosci. 6, 1925–1931 (2013)

    Article  Google Scholar 

  3. 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 

  4. Lashkaripour, G.R.: Predicting mechanical properties of mudrock from index parameters. Bull. Eng. Geol. Environ. 61, 73–77 (2002)

    Article  Google Scholar 

  5. Fener, M., Kahraman, S., Bilgil, A., Gunaydin, O.: A comparative evaluation of indirect methods to estimate the compressive strength of rocks. Rock Mech. Rock Eng. 38, 329–343 (2005)

    Article  Google Scholar 

  6. Kilic, A., Teymen, A.: Determination of mechanical properties of rocks using simple methods. Bull. Eng. Geol. Environ. 67, 237–244 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Nazir, R., Momeni, E., Jahed Armaghani, D.: Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples. Electr J Geotech Eng 18, 1737–1746 (2013)

    Google Scholar 

  10. Kallu, R.R., Roghanchi, P.: Correlations between direct and indirect strength test methods. Int J Min Sci Tech 25, 355–360 (2015)

    Article  Google Scholar 

  11. Beiki, M., Majdi, A., Givshad, A.: Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int. J. Rock Mech. Min. Sci. 63, 159–163 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Jahed Armaghani, D., Tonnizam Mohamad, E., Momeni, E., Narayanasamy, M.S., Amin, M.F.M.: 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 

  14. Momeni, E., Jahed Armaghani, D., 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 

  15. Jahed 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 

  16. Abdi, Y., Garavand, A.T., Sahamieh, R.Z.: Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis. Arab. J. Geosci. 11, 587 (2018)

    Article  Google Scholar 

  17. Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.T., et al.: Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks. Transp. Geotech. 19, 100588 (2021)

    Article  Google Scholar 

  18. Acar, M.C., Kaya, B.: Models to estimate the elastic modulus of weak rocks based on least square support vector machine. Arab. J. Geosci. 13, 590 (2020)

    Article  Google Scholar 

  19. Armaghani, D.J., Harandizadeh, H., Momeni, E., et al.: An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity. Artif. Intell. Rev. 55, 2313–2350 (2022). https://doi.org/10.1007/s10462-021-10065-5

    Article  Google Scholar 

  20. Abdi, Y., Momeni, E., Armaghani, D.J.: Elastic modulus estimation of weak rock samples using random forest technique. Bull. Eng. Geol. Environ. 82, 176 (2023). https://doi.org/10.1007/s10064-023-03154-y

    Article  Google Scholar 

  21. Krynine, P.D.: The megascopic study and field classification of sedimentary rocks. J. Geol. 56, 130–165 (1948)

    Article  Google Scholar 

  22. Boggs, S.: Principles of Sedimentology and Stratigraphy, 2nd edn., p. 553p. Prentince-Hall Inc., Hoboken (1993)

    Google Scholar 

  23. Fahy, M.P., Guccione, M.J.: Estimating strength of sandstone using petrographic thin-section data. Bull. Assoc. Eng. Geol. 16, 467–485 (1979)

    Google Scholar 

  24. Dobereiner, L., DeFreitas, M.H.: Geotechnical properties of weak sandstones. Geotechnique 36, 79–94 (1986)

    Article  Google Scholar 

  25. Shakoor, A., Bonelli, R.E.: Relationship between petrophysical characteristics, engineering index properties and mechanical properties of selected sandstones. Bull. Assoc. Eng. Geol. 28, 55–71 (1991)

    Google Scholar 

  26. Ulusay, R., Tureli, K., Ider, M.H.: Prediction of engineering properties of a selected litharenite sandstone from its petrographic characteristics using correlation and multivariable statistical techniques. Eng. Geol. 37, 135–157 (1994)

    Article  Google Scholar 

  27. Bell, F.G., Culshaw, M.G.: Petrographic and engineering properties of sandstones from the Sneinton Formation, Nottinghamshire. Engl. Q. J. Eng. Geol. 31, 5–19 (1998)

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Heidari, M., Momeni, A.A., Rafiei, B., Khodabaksh, H., Torabi-Kaveh, M.: Relationship between petrographic characteristics and the engineering properties of Jurassic sandstones, Hamedan, Iran. Rock Mech. Rock Eng. 46, 1091–1101 (2013)

    Article  Google Scholar 

  30. Khanlari, G.R., Heidari, M., Noori, M., Momeni, A.: The effect of petrographic characteristics on engineering properties of conglomerates from Famenin region, Northeast of Hamedan, Iran. Rock Mech. Rock Eng. 49, 2609 (2016)

    Article  Google Scholar 

  31. Shi, Z., Zhang, W., Wang, Z.: Correlation of physical and mechanical properties of Jurassic sandstone in Jining, Shandong province. Arab. J. Geosci. 14, 1254 (2021). https://doi.org/10.1007/s12517-021-07655-6

    Article  Google Scholar 

  32. Gunsallus, K.L., Kulhawy, F.H.: A comparative evaluation of rock strength measures. Int. J. Rock Mech. Miner. Sci. Geomech. Abstr. 21, 233–248 (1984)

    Article  Google Scholar 

  33. Bell, F.G.: The physical and mechanical properties of the Fell Sandstones, Northumberland, England. Eng. Geol. 12, 1–29 (1978)

    Article  Google Scholar 

  34. Undul, O.: Assessment of mineralogical and petrographic factors affecting petro-physical properties, strength and cracking processes of volcanic rocks. Eng. Geol. 210, 10–22 (2016). https://doi.org/10.1016/j.enggeo.2016.06.001

    Article  Google Scholar 

  35. Tandon, S.R., Gupta, V.: The control of mineral constituents and textural characteristics on the petrophysical & mechanical (PM) properties of different rocks of the Himalaya. Eng. Geol. 153, 125–143 (2013). https://doi.org/10.1016/j.enggeo.2012.11.005

    Article  Google Scholar 

  36. Gupta, V., Sharma, R.: Relationship between textural, petrophysical and mechanical properties of quartzites: a case study from northwestern Himalaya. Eng. Geol. 135–136, 1–9 (2012)

    Article  Google Scholar 

  37. Tamrakar, N.K., Yokota, S., Shrestha, S.D.: Relationships among mechanical, physical and petrographic properties of Siwalik sandstones, Central Nepal Sub-Himalayas. Eng Geol 90, 105–123 (2007). https://doi.org/10.1016/j.enggeo.2006.10.005

    Article  Google Scholar 

  38. Abdi, Y., Yusefi-Yegane, B., Jamshidi, A.: Estimation of mechanical properties of sandstones from petrographic characteristics using artificial neural networks (ANNs). Bull. Geol. Soc. Malaysia 71, 13–22 (2021). https://doi.org/10.7186/bgsm71202102

    Article  Google Scholar 

  39. Pappalardo, G., Punturo, R., Mineo, S., Ortolano, G., Castelli, F.: Engineering geological and petrographic characterization of migmatites belonging to the Calabria-Peloritani Orogen (southern Italy). Rock Mech. Rock Eng. 49, 1143–1160 (2016). https://doi.org/10.1007/s00603-015-0808-9

    Article  Google Scholar 

  40. Manouchehrian, A., Sharifzadeh, M., Hamidzadeh Moghadam, R.: Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int. J. Min. Sci. Technol. 22, 229–236 (2012)

    Article  Google Scholar 

  41. 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 

  42. Åkesson, U., Lindqvist, J., Göransson, M., et al.: Relationship between texture and mechanical properties of granites, central Sweden, by use of image-analysing techniques. Bull. Eng. Geol. Environ. 60, 277–284 (2001). https://doi.org/10.1007/s100640100105

    Article  Google Scholar 

  43. Mlynarczuk, M.: Some remarks on the application of image analysis and image processing for the description of the geometrical structures of rock. Physicochem. Probl. Miner. Process. 33, 107–116 (1999)

    Google Scholar 

  44. Aligholi, S., Lashkaripour, G.R., Ghafoori, M.: Estimating engineering properties of igneous rocks using semi-automatic petrographic analysis. Bull. Eng. Geol. Environ. 78, 2299–2314 (2019). https://doi.org/10.1007/s10064-018-1305-7

    Article  Google Scholar 

  45. Ross, B.J., Fueten, F., Yashkir, D.Y.: Automatic mineral identification using genetic programming. Mach. Vis. Appl. 13, 61–69 (2001). https://doi.org/10.1007/PL00013273

    Article  Google Scholar 

  46. Obara, B.: A new algorithm using image color system transformation for rock grain segmentation. Min. Petrol. 91, 271–285 (2007). https://doi.org/10.1007/s00710-007-0200-x

    Article  Google Scholar 

  47. Yesiloglu-Gultekin, N., Keceli, A., Sezer, E., Can, A., Gokceoglu, C., Bayhan, H.: A computer program (tsecsoft) to determine mineral percentages using photographs obtained from thin sections. Comput. Geosci. 46, 310–316 (2012)

    Article  Google Scholar 

  48. Jungmann, M., Pape, H., Wißkirchen, P., Clauser, C., Berlage, T.: Segmentation of thin section images for grain size analysis using region competition and edge-weighted region merging. Comput. Geosci. 72, 33–48 (2014)

    Article  Google Scholar 

  49. Saedi, B., Mohammadi, S.D.: Prediction of uniaxial compressive strength and elastic modulus of migmatites by microstructural characteristics using artificial neural networks. Rock Mech. Rock Eng. 54, 5617–5637 (2021)

    Article  Google Scholar 

  50. Saxena, N., Mavko, G.: Estimating elastic moduli of rocks from thin sections: digital rock study of 3D properties from 2D images. Comput. Geosci. 88, 9–21 (2016). https://doi.org/10.1016/j.cageo.2015.12.008

    Article  Google Scholar 

  51. ISRM.: The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Hudson JA, Ulusay R (eds) Suggested methods prepared by the commission on testing methods. International Society for Rock Mechanics (ISRM) Turkish National Group, Ankara (2007)

  52. Folk, R.L.: Petrology of Sedimentary Rocks, p. 182. Hemphill Publishing Company, Austin (1974)

    Google Scholar 

  53. Dowlati, M., Mohtasebi, S.S., Omid, M., Razavi, S.H., Jamzad, M.: Miguel de la Guardia, Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. J. Food Eng. 119, 277–287 (2013)

    Article  Google Scholar 

  54. Hornberg, A.: Handbook of Machine Vision. Wiley, Weinheim (2006)

    Book  Google Scholar 

  55. García-Mateosa, G., Hernández-Hernándezc, J.L., Escarabajal-Henarejosb, D.S., Jaén-Terronesa, J.M., Molina-Martínez: Study and comparison of color models for automatic image analysis in irrigation management, applications. Agric. Water Manag. 151, 158–166 (2015)

    Article  Google Scholar 

  56. Zhou, X., Yuan, J., Liu, H.: A traffic light recognition algorithm based on compressive tracking. Int. J.Hybrid Inf. Technol. 8, 323–332 (2015)

    Google Scholar 

  57. Taheri-Garavand, A., Mumivand, H., Fanourakis, D., Fatahi, S., Taghipour, S.: An artificial neural network approach for non-invasive estimation of essential oil content and composition through considering drying processing factors: a case study in Mentha aquatic. Ind. Crops Prod. 171(1), 113985 (2021)

    Article  Google Scholar 

  58. Sun, X., Gong, H.J., Zhang, F., Chen, K.J.: A digital image method for measuring and analyzing color characteristics of various color scores of beef. In: Image and Signal Processing, 2009. CISP'09. 2nd International Congress on (pp. 1–6). IEEE (2009)

  59. Khulal, U., Zhao, J., Hu, W., Chen, Q.: Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem. 197, 1191–1199 (2016)

    Article  Google Scholar 

  60. Jiang, L., Zhu, B., Tao, Y.: Hyperspectral image classification methods. In: Sun, D.W. (ed.) Hyperspectral Imaging for Food Quality Analysis and Control, pp. 79–98. Elsevier, Amsterdam (2010)

    Chapter  Google Scholar 

  61. Karray, F.O., Silva, C.D.: Soft Computing and Intelligent Systems Design: Theory, Tools and Applications. Addison Wesley Pearson, New York (2004)

    Google Scholar 

  62. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1999)

    Google Scholar 

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Acknowledgements

We would like to express our sincere gratitude to Lorestan University for their support and resources provided during the completion of this research project and the subsequent publication of this paper.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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AT-G carried out the image analysis and ANN model, YA wrote the materials and methods, AT-G. Y-A and EM wrote the main manuscript text and prepared figures. All authors reviewed the manuscript.

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Correspondence to Yasin Abdi.

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Taheri-Garavand, A., Abdi, Y. & Momeni, E. Smart Estimation of Sandstones Mechanical Properties Based on Thin Section Image Processing Techniques. J Nondestruct Eval 43, 42 (2024). https://doi.org/10.1007/s10921-024-01056-x

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