Comptes Rendus
A hybrid modelling approach for prediction of UCS of rock materials
Comptes Rendus. Mécanique, Volume 348 (2020) no. 3, pp. 235-243.

It is necessary to make an accurate assessment of uniaxial compressive strength (UCS) for rock mass classification and rock engineering design. However, there are many shortcomings in the conventional tests for UCS of rocks. The aim of this study is to present a hybrid model by integrating the genetic algorithm (GA) into the least-squares support vector machine (LSSVM) to predict the UCS of rock materials. The GA technique was utilized to improve the forecasting accuracy of the proposed LSSVM. To develop the proposed hybrid GA–LSSVM model, four main factors including the block punch index, point load strength, Schmidt rebound hardness and ultrasonic P-wave velocity were considered as input variables, while the UCS of rock materials was the output. A comparison was conducted among the proposed GA–LSSVM, the adaptive neuro-fuzzy inference system, the fuzzy inference system, the artificial neural network and the statistical method in accordance with three statistical indexes. The results of the comparisons show that the developed GA–LSSVM model has great potential to accurately estimate the UCS of rock materials.

Received:
Revised:
Accepted:
Published online:
DOI: 10.5802/crmeca.17
Keywords: Uniaxial compressive strength, Genetic algorithm, Statistical model, Rocks, Least-squares support vector machine
Xinhua Xue 1; Yufeng Wei 2

1 State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, China
2 State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
@article{CRMECA_2020__348_3_235_0,
     author = {Xinhua Xue and Yufeng Wei},
     title = {A hybrid modelling approach for prediction of {UCS} of rock materials},
     journal = {Comptes Rendus. M\'ecanique},
     pages = {235--243},
     publisher = {Acad\'emie des sciences, Paris},
     volume = {348},
     number = {3},
     year = {2020},
     doi = {10.5802/crmeca.17},
     language = {en},
}
TY  - JOUR
AU  - Xinhua Xue
AU  - Yufeng Wei
TI  - A hybrid modelling approach for prediction of UCS of rock materials
JO  - Comptes Rendus. Mécanique
PY  - 2020
SP  - 235
EP  - 243
VL  - 348
IS  - 3
PB  - Académie des sciences, Paris
DO  - 10.5802/crmeca.17
LA  - en
ID  - CRMECA_2020__348_3_235_0
ER  - 
%0 Journal Article
%A Xinhua Xue
%A Yufeng Wei
%T A hybrid modelling approach for prediction of UCS of rock materials
%J Comptes Rendus. Mécanique
%D 2020
%P 235-243
%V 348
%N 3
%I Académie des sciences, Paris
%R 10.5802/crmeca.17
%G en
%F CRMECA_2020__348_3_235_0
Xinhua Xue; Yufeng Wei. A hybrid modelling approach for prediction of UCS of rock materials. Comptes Rendus. Mécanique, Volume 348 (2020) no. 3, pp. 235-243. doi : 10.5802/crmeca.17. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.17/

[1] A. Basu; A. Aydin Predicting uniaxial compressive strength by point load test: significance of cone penetration, Rock Mech. Rock Eng., Volume 39 (2006) no. 5, pp. 483-490 | DOI

[2] I. Yilmza A new testing method for indirect determination of the unconfined compressive strength of rocks, Int. J. Rock Mech. Min. Sci., Volume 46 (2009), pp. 1349-1357 | DOI

[3] D. Q. Dan; H. Konietzky; H. Martin Brazilizan tensile strength tests on some anisotropic rocks, Int. J. Rock Mech. Min. Sci., Volume 58 (2013), pp. 1-7 | DOI

[4] D. A. Mishra; M. Srigyan; A. Basu; P. J. Rokade Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests, Int. J. Rock Mech. Min. Sci., Volume 80 (2015), pp. 418-424 | DOI

[5] M. M. Aliyu; J. Shang; W. Murphy; J. A. Lawrence; R. Collier; F. Kong; Z. Zhao Assessing the uniaxial compressive strength of extremely hard cryptocrystalline flint, Int. J. Rock Mech. Min. Sci., Volume 113 (2019), pp. 310-321 | DOI

[6] C. O. Aksoy; V. Ozacar; N. Demirel; S. C. Ozer; S. Safak Determination of instantaneous breaking rate by geological strength index, block punch index and power of impact hammer for various rock mass conditions, Tunn. Undergr. Space Tech., Volume 26 (2011), pp. 534-540 | DOI

[7] C. O. Aksoy; V. Ozacar; O. Kantarci An example for estimation of rock mass deformations around an underground opening by using numerical modeling, Int. J. Rock Mech. Min. Sci., Volume 47 (2010), pp. 272-278 | DOI

[8] V. K. Singh; D. Singh; T. N. Singh Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks, Int. J. Rock Mech. Min. Sci., Volume 38 (2001), pp. 269-284 | DOI

[9] I. Yilmaz; A. G. Yuksek An example of artificial neural network (ANN) application for indirect estimation of rock parameters, Rock Mech. Rock Eng., Volume 41 (2007) no. 5, pp. 781-795 | DOI

[10] C. Canakci; A. Baskayoglu; H. Gullu Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming, Neural Comput. Appl., Volume 18 (2009), pp. 1031-1041 | DOI

[11] A. Cevik; E. A. Sezer; A. F. Cabalar; C. Gokceoglu Modelling of the uniaxial compressive strength of some clay-bearing rocks using neural network, Appl. Soft Comput., Volume 11 (2011), pp. 2587-2594 | DOI

[12] S. Yagiz; E. A. Sezer; C. Gokceoglu 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., Volume 36 (2012), pp. 1636-1650 | DOI

[13] D. A. Mishra; A. Basu Estimation of uniaxial compression strength of rock materials by index tests using regression analysis and fuzzy inference system, Eng. Geol., Volume 160 (2013), pp. 54-68 | DOI

[14] N. Yesiloglu-Gultekin; E. A. Sezer; C. Gokceoglu; H. Bayhan 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., Volume 40 (2013), pp. 921-928 | DOI

[15] N. Yesiloglu-Gultekin; C. Gokceoglu; E. A. Sezer Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances, Int. J. Rock Mech. Min. Sci., Volume 62 (2013), pp. 113-122 | DOI

[16] R. Barzegar; M. Sattarpour; M. R. Nikudel; A. A. Moghaddam Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran, Model. Earth Syst. Environ., Volume 2 (2016), 76 | DOI

[17] S. H. Jalali; M. Heidari; H. Mohseni Comparison of models for estimating uniaxial compressive strength of some sedimentary rocks from Qom Formation, Environ. Earth Sci., Volume 76 (2017), 753 | DOI

[18] B. Saedi; S. D. Mohammadi; H. Shahbazi Application of fuzzy inference system to predict uniaxial compressive strength and elastic modulus of migmatites, Environ. Earth Sci., Volume 78 (2019), 208 | DOI

[19] L. H. Xiong; M. O. Kieran; S. L. Guo Comparasion of three updating schemes using artificial neural network in flow forecasting, Hydrol. Earth Syst. Sci., Volume 8 (2004), pp. 247-255 | DOI

[20] Y. B. Sun; D. Wendi; D. E. Kim; S. Y. Liong Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest, Hydrol. Earth Syst. Sci., Volume 20 (2016), pp. 1405-1412 | DOI

[21] C. S. Zhang; J. Ji; Y. l. Gui; J. Kodikara; S. Q. Yang; L. He Evaluation of soil-concrete interface shear strength based on LS-SVM, Geomech. Eng., Volume 11 (2016) no. 3, pp. 361-372 | DOI

[22] J. A. K. Suykens; J. Vandewalle; B. De Moor Optimal control by least squares support vector machines, Neural Networks, Volume 14 (2001), pp. 23-35 | DOI

[23] J. Mercer Functions of positive and negative type and their connection with the theory of integral equations, Phil. Trans. R. Soc., Volume 209 (1909), pp. 415-446 | Zbl

[24] Y. Dibike; S. Velickov; D. Solomatine; M. Abbott Model induction with support vector machines: introduction and applications, J. Comput. Civ. Eng., Volume 15 (2001), pp. 208-216 | DOI

Cited by Sources:

Comments - Policy


Articles of potential interest

Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model

Min Wang; Wen Wan; Yanlin Zhao

C. R. Méca (2020)


Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: Neurocomputing and statistical approaches

Goutami Chattopadhyay; Surajit Chattopadhyay; Rajni Jain

C. R. Géos (2010)


A novel model for prediction of uniaxial compressive strength of rocks

Xinhua Xue

C. R. Méca (2022)