Skip to main content

Advertisement

SpringerLink
Log in

A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine

  • Practice-oriented paper
  • Published:
Innovative Infrastructure Solutions Aims and scope Submit manuscript

Abstract

In this paper, the relationships between engineering properties of travertine rock samples including uniaxial compressive strength, density, Brazilian tensile strength and compressional and shear wave velocities were evaluated. The Bukan travertine mine located in Iran was considered as case study here. Various data analysis approaches including simple regression method, multiple regression method and artificial neural network (ANN) have been used for finding optimum estimation model for uniaxial compression strength of travertine rocks. Rock sample preparations difficulties and conducting expensive tests such as UCS motivated many researchers to study different regression methods to estimate UCS from other rock mechanic tests. In this paper, different statistical methods as well as some ANN optimization algorithms that were used by several researchers are compared to find the optimum solution for UCS estimation problem of travertine rock samples. These optimization tools comprising genetic algorithm, particle swarm optimization and imperialist competitive algorithm were applied to improve the efficiency of ANN analysis. Finally, after comparing all of the presented methods, the best results were obtained by ANN-PSO algorithm.

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

Similar content being viewed by others

Data availability

The data are available in Appendix 1

Code availability

Not applicable.

References

  1. Horsrud P (2001) Estimating mechanical properties of shale from empirical correlations. Soc Pet Eng. https://doi.org/10.2118/56017-PA

    Article  Google Scholar 

  2. Kahraman S (2001) Evaluation of simple methods for assessing the uniaxial compressive strength rock. International Journal of Rock Mechanics and Mining Sciences 38(7):981–994. https://doi.org/10.1016/s1365-1609(01)00039-9

    Article  Google Scholar 

  3. 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(5):871–875. https://doi.org/10.1016/j.ijrmms.2004.01.012

    Article  Google Scholar 

  4. Yaşar E, Erdoğan Y (2004) Estimation of rock physicomechanical properties using hardness methods. Eng Geol 71(3–4):281–288. https://doi.org/10.1016/s0013-7952(03)00141-8

    Article  Google Scholar 

  5. Chang C, Zoback MD, Khaksar A (2006) Empirical relations between rock strength and physical properties in sedimentary rocks. J Petrol Sci Eng 51(3–4):223–237. https://doi.org/10.1016/j.petrol.2006.01.003

    Article  Google Scholar 

  6. Çobanoğlu İ, Çelik SB (2008) Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bull Eng Geol Env 67(4):491–498. https://doi.org/10.1007/s10064-008-0158-x

    Article  Google Scholar 

  7. Yagiz S (2008) Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer. Bull Eng Geol Env 68(1):55–63. https://doi.org/10.1007/s10064-008-0172-z

    Article  Google Scholar 

  8. Diamantis K, Gartzos E, Migiros G (2009) Study on uniaxial compressive strength, point load strength index, dynamic and physical properties of serpentinites from Central Greece: test results and empirical relations. Eng Geol 108(3–4):199–207. https://doi.org/10.1016/j.enggeo.2009.07.002

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Minaeian B, Ahangari K (2011) Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arab J Geosci 6(6):1925–1931. https://doi.org/10.1007/s12517-011-0460-y

    Article  Google Scholar 

  11. Singh TN, Kainthola A, Venkatesh A (2011) Correlation between point load index and uniaxial compressive strength for different rock types. Rock Mech Rock Eng 45(2):259–264. https://doi.org/10.1007/s00603-011-0192-z

    Article  Google Scholar 

  12. Bruno G, Vessia G, Bobbo L (2012) Statistical method for assessing the uniaxial compressive strength of carbonate rock by Schmidt Hammer tests performed on core samples. Rock Mech Rock Eng 46(1):199–206. https://doi.org/10.1007/s00603-012-0230-5

    Article  Google Scholar 

  13. Azimian A, Ajalloeian R, Fatehi L (2013) An Empirical Correlation of Uniaxial Compressive Strength with P-wave Velocity and Point Load Strength Index on Marly Rocks Using Statistical Method. Geotech Geol Eng 32(1):205–214. https://doi.org/10.1007/s10706-013-9703-x

    Article  Google Scholar 

  14. Jahed Armaghani D, Hajihassani M, Yazdani Bejarbaneh B, Marto A, Tonnizam Mohamad E (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498. https://doi.org/10.1016/j.measurement.2014.06.001

    Article  Google Scholar 

  15. Karaman K, Kesimal A, Ersoy H (2014) A comparative assessment of indirect methods for estimating the uniaxial compressive and tensile strength of rocks. Arab J Geosci 8(4):2393–2403. https://doi.org/10.1007/s12517-014-1384-0

    Article  Google Scholar 

  16. Kaya A, Karaman K (2015) Utilizing the strength conversion factor in the estimation of uniaxial compressive strength from the point load index. Bull Eng Geol Env 75(1):341–357. https://doi.org/10.1007/s10064-015-0721-1

    Article  Google Scholar 

  17. 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. https://doi.org/10.1016/j.measurement.2016.03.050

    Article  Google Scholar 

  18. Salehin S (2017) Investigation into engineering parameters of marls from Seydoon dam in Iran. Jo Rock Mech Geotech Eng 9(5):912–923

    Article  Google Scholar 

  19. Salehin S, Hadavandi E, Chelgani SC (2020) Exploring relationships between mechanical properties of marl core samples by a coupling of mutual information and predictive ensemble model. Model Earth Syst Environ 6(1):575–583

    Article  Google Scholar 

  20. Matin S, Farahzadi L, Makaremi S, Chelgani SC, Sattari G (2018) Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Appl Soft Comput 70:980–987

    Article  Google Scholar 

  21. Zhang J, Li D, Wang Y (2020) Toward intelligent construction: prediction of mechanical properties of manufactured-sand concrete using tree-based models. J Clean Prod 258:120665

    Article  Google Scholar 

  22. Rabbani E, Sharif F, Koolivand Salooki M (1997) 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

    Article  Google Scholar 

  23. Manouchehrian A, Sharifzadeh M, Moghadam RH (2012) Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int J Min Sci Technol 22(2):229–236

    Article  Google Scholar 

  24. Dehghan S, Sattari G, Chelgani SC, Aliabadi M (2010) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Technol (China) 20(1):41–46

    Article  Google Scholar 

  25. Yılmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795

    Article  Google Scholar 

  26. Mishra D, 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

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. Singh R, Umrao RK, Ahmad M, Ansari M, Sharma L, Singh T (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119

    Article  Google Scholar 

  29. Sharma L, Vishal V, Singh T (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

    Article  Google Scholar 

  30. Motamedi S, Roy C, Shamshirband S, Hashim R, Petković D, Song K-I (2015) Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology. Ultrasonics 61:103–113

    Article  Google Scholar 

  31. Kainthola A, Singh P, Verma D, Singh R, Sarkar K, Singh T (2015) Prediction of strength parameters of himalayan rocks: a statistical and ANFIS approach. Geotech Geol Eng 33(5):1255–1278

    Article  Google Scholar 

  32. Singh R, Vishal V, Singh T, Ranjith P (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506

    Article  Google Scholar 

  33. 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(5):651–663

    Article  Google Scholar 

  34. Beiki M, Majdi A (1997) 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

    Article  Google Scholar 

  35. Mohamad ET, Armaghani DJ, Momeni E, Abad SVANK (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Env 74(3):745–757

    Article  Google Scholar 

  36. Momeni E, Armaghani DJ, 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Kahraman S, Gunaydin O, Fener M (2005) The effect of porosity on the relation between uniaxial compressive strength and point load index. Int J Rock Mech Min Sci 42(4):584–589

    Article  Google Scholar 

  39. Sharma PK, Singh TN (2007) A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bull Eng Geol Env 67(1):17–22. https://doi.org/10.1007/s10064-007-0109-y

    Article  Google Scholar 

  40. Khandelwal M, Singh TN (2009) Correlating static properties of coal measures rocks with P-wave velocity. Int J Coal Geol 79(1–2):55–60. https://doi.org/10.1016/j.coal.2009.01.004

    Article  Google Scholar 

  41. Sarkar K, Vishal V, Singh TN (2011) An empirical correlation of index geomechanical parameters with the compressional wave velocity. Geotech Geol Eng 30(2):469–479. https://doi.org/10.1007/s10706-011-9481-2

    Article  Google Scholar 

  42. Khandelwal M (2012) Correlating P-wave velocity with the physico-mechanical properties of different rocks. Pure Appl Geophys 170(4):507–514. https://doi.org/10.1007/s00024-012-0556-7

    Article  Google Scholar 

  43. Standard A (2008) D2845-08 Standard test method for laboratory determination of pulse velocities and ultrasonic elastic constants of rock. ASTM International, West Conshohocken

    Google Scholar 

  44. ISRM X X (1979) ISRM suggested methods for determining water content, porosity, density, absorption and related properties and swelling and slake-durability index properties. Int J Rock Mechan Min Sci Geomechan Abstr 16(2):143–151

    Article  Google Scholar 

  45. Brown E (1981) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Rock characterization, testing and monitoring—ISRM suggested methods. Pergamon Press, Oxford, pp 113–116

    Google Scholar 

  46. Bieniawski Z, Bernede M (1979) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials: part 1. Suggested method for determining deformability of rock materials in uniaxial compression. Int J Rock Mech Min Sci Geomech 2:138–140

    Article  Google Scholar 

  47. Carneiro F (1947) Une novelle methode d’essai pour determiner la Resistance à la traction du beton. Reunion dês Laboratoires d’Essai de Materiaux

  48. Akazawa T (1953) Tension test methods for concretes, international union of testing and research laboratories for-materials and structures (RILEM), Paris. Bulletin 16:11–23

    Google Scholar 

  49. Meulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36(1):29–39

    Article  Google Scholar 

  50. Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25(7):849–857. https://doi.org/10.1016/s0141-0296(03)00004-x

    Article  Google Scholar 

  51. Yılmaz I, Yuksek AG (2007) An Example of Artificial Neural Network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795. https://doi.org/10.1007/s00603-007-0138-7

    Article  Google Scholar 

  52. Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99(1–2):51–60

    Article  Google Scholar 

  53. Singh R, Vishal V, Singh TN, Ranjith PG (2012) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506. https://doi.org/10.1007/s00521-012-0944-z

    Article  Google Scholar 

  54. Simpson PK (1990) Artificial neural system—foundation, paradigm, application and implementations. Pergamon, New York

    Google Scholar 

  55. Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using Artificial Neural Networks technique. J Sci Ind Res 63:32–38

    Google Scholar 

  56. Haykin S, Network N (2004) A comprehensive foundation. Neural Netw 2(2004):41

    Google Scholar 

  57. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236

    Article  Google Scholar 

  58. Masters T (1993) Practical neural network recipes in C++. Morgan Kaufmann

  59. Paola J (1994) Neural network classification of multispectral imagery. Master Tezi, The University of Arizona, USA

  60. Ripley BD (1993) Statistical aspects of neural networks. Networks and chaos—statistical and probabilistic aspects 50:40-123

  61. Wang C (1994) A theory of generalization in learning machines with neural network applications

  62. Demuth H, Beale M (1993) Neural network toolbox for use with matlab–User’S guide verion 3.0

  63. Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47(2):246–253. https://doi.org/10.1016/j.ijrmms.2009.09.011

    Article  Google Scholar 

  64. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 4661–4667

  65. Wang H, Pan J, Wang S, Zhu H (2015) Relationship between macro-fracture density, P-wave velocity, and permeability of coal. J Appl Geophys 117:111–117. https://doi.org/10.1016/j.jappgeo.2015.04.002

    Article  Google Scholar 

  66. Teng J, Yao Y, Liu D, Cai Y (2015) Evaluation of coal texture distributions in the southern Qinshui basin, North China: investigation by a multiple geophysical logging method. Int J Coal Geol 140:9–22. https://doi.org/10.1016/j.coal.2014.12.014

    Article  Google Scholar 

  67. Kassab MA, Weller A (2015) Study on P-wave and S-wave velocity in dry and wet sandstones of Tushka region, Egypt. Egypt J Petrol 24(1):1–11. https://doi.org/10.1016/j.ejpe.2015.02.001

    Article  Google Scholar 

  68. El Sayed NA, Abuseda H, Kassab MA (2015) Acoustic wave velocity behavior for some Jurassic carbonate samples, north Sinai, Egypt. J Afr Earth Sci 111:14–25

    Article  Google Scholar 

  69. Heidari M, Momeni AA, Naseri F (2013) New weathering classifications for granitic rocks based on geomechanical parameters. Eng Geol 166:65–73. https://doi.org/10.1016/j.enggeo.2013.08.007

    Article  Google Scholar 

  70. Lee J-S, Yoon H-K (2017) Characterization of rock weathering using elastic waves: a Laboratory-scale experimental study. J Appl Geophys 140:24–33. https://doi.org/10.1016/j.jappgeo.2017.03.008

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohammad Ebdali

  2. School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Emad Khorasani & Sohrab Salehin

Authors
  1. Mohammad Ebdali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emad Khorasani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sohrab Salehin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mr. Salehin did the analysis and wrote the article, Dr. Khorasani revised the article, Mr. Ebdali did some of laboratory tests.

Corresponding author

Correspondence to Sohrab Salehin.

Ethics declarations

Conflict of interest

Not applicable.

Appendix

Appendix

See Table 12.

Table 12 The results of laboratory tests on travertine samples obtained in this research
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ebdali, M., Khorasani, E. & Salehin, S. A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine. Innov. Infrastruct. Solut. 5, 93 (2020). https://doi.org/10.1007/s41062-020-00346-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41062-020-00346-3

Keywords

Search

Navigation