ORIGINAL ARTICLE
Development of Soft Computing-based Predictive Tools for Estimating the Young Modulus of Weak Rocks
1
Material Science and Nanotechnology Engineering Department, Abdullah Gul University, Turkey
2
Faculty of Geoengineering, Mining and Geology, Wrocław University of Technology and Science, Poland
These authors had equal contribution to this work
Submission date: 2024-07-04
Final revision date: 2024-07-29
Acceptance date: 2024-07-31
Online publication date: 2024-09-19
Publication date: 2024-09-19
Corresponding author
Ekin Köken
Material Science and Nanotechnology Engineering Department, Abdullah Gul University, Turkey
Material Science and Nanotechnology Engineering Department, Abdullah Gul University, Turkey
Civil and Environmental Engineering Reports 2024;34(3):182-193
KEYWORDS
TOPICS
ABSTRACT
The deformation characteristics of rocks are of vital importance in addressing most geomechanical issues as they are one of the most critical input parameters in rock engineering analyses. For this reason, robust forecasting models are required when analysing the stability of tunnels, slopes, mine galleries, and other underground excavations. In this research, novel predictive models are proposed to estimate the tangential Young modulus (Eti) of weak rocks. To achieve this, an extensive literature review is performed to obtain a comprehensive database including critical physico-mechanical properties of various weak rocks. Thanks to the advantages of soft computing methods such as genetic algorithm (GA), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), novel predictive models are established. The effectiveness of the developed predictive models is investigated using various statistical measures and it is concluded that empirical models utilizing ANN and ANFIS methodologies are the most effective tools for estimating the Eti of weak rocks. In addition, a practical design chart is also developed for assessing the Eti of weak rocks.
REFERENCES (46)
1.
Nicholson, GA and Bieniawski, ZT 1990. A nonlinear deformation modulus based on rock mass classification. Int J Min Geol Eng 8, 181–202. https://doi.org/10.1007/BF0155....
2.
Mitri, HS, Edrissi, R and Henning J 1994. Finite element modelling of cable bolted stopes in hard rock ground mines. Presented at the SME Annual Meeting. New Mexico, Albuquerque, 94–116.
3.
Kayabasi, A, Gokceoglu, C and Ercanoglu E 2003. Estimating the deformation modulus of rock masses: a comparative study. Int J Rock Mech and Min Sci 40, :55–63. https://doi.org/10.1016/S1365-....
4.
Gokceoglu, C, Yesilnacar, E, Sonmez, H and Kayabasi A 2004. A neuro-fuzzy model for modulus of deformation of jointed rock masses. Comput Geotech 31(5), 375–383. https://doi.org/10.1016/j.comp....
5.
Ramamurthy, T 2004. A geo-engineering classifcation for rocks and rock masses. Int J Rock Mech Min Sci 41(1), 89–101. https://doi.org/10.1016/S1365-....
6.
Sonmez, H, Tuncay, E and Gokceoglu C 2004. Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara Agglomerate. Int J Rock Mech Min Sci 41, 717–729. https://doi.org/10.1016/j.ijrm....
7.
Sonmez, H, Gökçeoğlu, C, Nefeslioğlu, HA and Kayabasi A 2006. 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. https://doi.org/10.1016/j.ijrm....
8.
Hoek, E and Diederichs, MS 2006. Empirical estimation of rock mass modulus. Int J Rock Mech Min Sci 43, 203–215. https://doi.org/10.1016/j.ijrm....
9.
Fattahi, H 2016. Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Engineering with Computers 32, 567-580.
10.
Bellapu, HVS, Sinha, RK and Naik SR 2023. Estimation of Modulus of Deformation Using Rock Mass Rating—A Review and Validation Using 3D Numerical Modelling. Sustainability 15(7), 5721.
11.
Saiang, D and Nordlund, E 2009. Numerical analyses of the influence of blast-induced damaged rock around shallow tunnels in brittle rock. Rock mechanics and rock engineering 42, 421-448.
12.
Cai, M 2011. Rock mass characterization and rock property variability considerations for tunnel and cavern design. Rock mechanics and rock engineering 44, 379-399.
13.
Bidgoli, MN, Zhao, Z and Jing, L 2013. Numerical evaluation of strength and deformability of fractured rocks. Rock Mech Geotech Eng 5, 419–430. https://doi.org/10.1016/j.jrmg....
14.
Yang, JP, Chen, WZ, Dai, YH and Yu, HD 2014. Numerical determination of elastic compliance tensor of fractured rock masses by finite element modeling. International Journal of Rock Mechanics and Mining Sciences 70, 474-482.
15.
Xu, H, Zhou, W, Xie, R, Da, L, Xiao, C, Shan, Y and Zhang, H 2016. Characterization of rock mechanical properties using lab tests and numerical interpretation model of well logs. Math Prob Eng 2016, 5967159. https://doi.org/10.1155/2016/5....
16.
Shu, J, Jiang, L, Kong, P and Wang, Q 2019. Numerical analysis of the mechanical behaviors of various jointed rocks under uniaxial tension loading. Appl Sci 9(9), 1824. https://doi.org/10.3390/app909....
17.
Chen, D, Chen, H, Zhang, W, Lou, J and Shan, B 2022. An analytical solution of equivalent elastic modulus considering confining stress and its variables sensitivity analysis for fractured rock masses. Journal of Rock Mechanics and Geotechnical Engineering 14(3), 825-836.
18.
Köken, E 2024. Soft computing implementations for evaluating Los Angeles abrasion value of rock aggregates from Kütahya, Turkey. Acta Technica Jaurinensis 17(1), 36-44.
19.
Zorlu, K, Gokceoglu, C, Ocakoglu, F, Nefeslioglu, HA and Acikalin, SJEG 2008. Prediction of uniaxial compressive strength of sandstones using petrography-based models. Engineering Geology 96(3-4), 141-158.
20.
Ceryan, N 2014. Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. Journal of African Earth Sciences 100, 634-644.
21.
Dehghan, S, Sattar, GH, Chehreh, CS and Aliabadi, MA 2010. Prediction of unconfined compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Sci Technol (China) 20(1), 41–46. https://doi.org/10.1016/S1674-....
22.
Behzadafshar, K, Sarafraz, ME, Hasanipanah, M, Mojtahedi, SFF and Tahir, MM 2019. Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory test results. Bull Eng Geol Environ 78, 1527–1536. https://doi.org/10.1007/s10064....
23.
Roy, DH and Singh, TN 2020. Predicting deformational properties of Indian coal: soft computing and regression analysis approach. Measurement 149, 106975. https://doi.org/10.1016/j.meas....
24.
Aboutaleb, S, Behnia, M, Bagherpour, R and Bluekian, B 2018. Using non-destructive tests for estimating uniaxial compressive strength and static Young’s modulus of carbonate rocks via some modeling techniques. Bulletin of Engineering Geology and the Environment 77, 1717-1728.
25.
Bejarbaneh, BY, Bejarbaneh, EY, Amin, MFM, Fahimifar, A, Jahed Armaghani, D and Majid, MZA 2018. Intelligent modeling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment 77, 345-361.
26.
Köken, E 2021. Assessment of deformation properties of coal measure sandstones through regression analyses and artificial neural networks. Archives of Mining Sciences 66(4), https://doi.org/10.24425/ams.2....
27.
Shahani, NM, Zheng, X, Liu, C, Li, P and Hassan, FU 2022. Application of soft computing methods to estimate uniaxial compressive strength and elastic modulus of soft sedimentary rocks. Arabian Journal of Geosciences 15(5), 384.
28.
Köken, E and Kadakçı Koca, T 2022. Evaluation of soft computing methods for estimating tangential young modulus of intact rock based on statistical performance indices. Geotechnical and Geological Engineering 40(7), 3619-3631. https://doi.org/10.1007/s10706....
29.
Khosravi, M, Tabasi, S, Eldien, HH, Motahari, MR and Alizadeh, SM 2022. Evaluation and prediction of the rock static and dynamic parameters. Journal of Applied Geophysics 199, 104581.
30.
Jin, X, Zhao, R and 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.
31.
Armaghani, DJ, Mohamad, ET, Momeni, E, Narayanasamy, MS and Amin, MFM 2015. 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 7, 1301–1309. https://doi.org/10.1007/s10064....
32.
Abdi, Y, Momeni, E and Armaghani, DJ 2023. Elastic modulus estimation of weak rock samples using random forest technique. Bulletin of Engineering Geology and the Environment 82(5), 1-20.
33.
Matin, SS, Farahzadi, L, Makaremi, S, Chelgani, SC and Sattari, GH 2018. Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Applied Soft Computing 70, 980-987.
34.
Pappalardo, G 2015. Correlation between P-wave velocity and physical–mechanical properties of intensely jointed dolostones, Peloritani mounts, NE Sicily. Rock mechanics and rock engineering 48, 1711-1721.
35.
Abdi, Y, Khanlari, GR and Jamshidi, A 2018. Correlation between mechanical properties of sandstones and P-wave velocity in different degrees of saturation. Geotechnical and Geological Engineering 42, 665-674.
36.
Madhubabu, N, Singh, PK, Kainthola, A, Mahanta, B, Tripathy, A and Singh, TN 2016. Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88, 202-213.
37.
Dinçer, İ, Acar, A and Ural, S 2008. Estimation of strength and deformation properties of Quaternary caliche deposits. Bulletin of Engineering Geology and the Environment 67, 353-366.
38.
Hersat, SB 2010. Volkanik kayaçlarin elastik özelliklerinin ultrases yöntemiyle belirlenmesi, Yüksek Lisans Tezi, (yayınlanmamış), Afyon Kocatepe Üniversitesi, Fen Bilimleri Enstitüsü, 71s, Afyon, Türkiye (in Turkish).
39.
Fereidooni, D, Karimi Z, Ghasemi F, 2024. Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models, Plos One, 19(6): e0302944.
40.
Friedman, JH 2001. Greedy function approximation: a gradient boosting machine. Ann Stat 29, 1189–1232.
41.
Sonmez, H, Gokceoglu, C, Nefeslioglu, HA and Kayabasi, A 2006. Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences 43(2), 224-235. https://doi.org/10.1016/j.ijrm....
42.
Ocak, I and Seker, SE 2012. Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network. Rock Mech Rock Eng 45, 1047–1054. https://doi.org/10.1007/s00603....
43.
Asrari, AA, Shahriar, K and Ataeepour, M 2015. The performance of ANFIS model for prediction of deformation modulus of rock mass. Arab J Geosci 8, 357–365. https://doi.org/10.1007/s12517....
44.
Umrao, RK, Sharma, LK, Singh, R and Singh, TN 2018. Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: An ANFIS predictive technique. Measurement: Journal of the International Measurement Confederation 126, 194–201. https://doi.org/10.1016/j.meas....
45.
Tokgozoglu, K, Aladag, CH and Gokceoglu, C 2021. Artificial neural networks to predict deformation modulus of rock masses considering overburden stress. Geomechanics and Geoengineering 18(1), 48–64. https://doi.org/10.1080/174860....
46.
Zhu, H, Li, X and Zhuang X 2011. Recent advances of digitization in rock mechanics and rock engineering. Journal of Rock Mechanics and Geotechnical Engineering 3(3), 220-233. https://doi.org/10.3724/SP.J.1....
| eISSN: | 2450-8594 |
| ISSN: | 2080-5187 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
