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Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results

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Abstract

An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direct determination of E is considered a time-consuming and complicated analysis. The present study is aimed to introduce a new hybrid intelligent model to predict the E of granitic rock samples. Hence, a series of granitic block samples were collected from the face of a water transfer tunnel excavated in Malaysia and transferred to laboratory to conduct rock index tests for E prediction. Rock index tests including point load, p-wave velocity and Schmidt hammer together with uniaxial compressive strength (UCS) tests were carried out to prepare a database comprised of 62 datasets for the analysis. Results of simple regression analysis showed that there is a need to develop models with multiple inputs. Then, a hybrid genetic algorithm (GA)-artificial neural network (ANN) model was developed considering parameters with the most impact on the GA. In order to have a fair evaluation, a predeveloped ANN model was also performed to predict E of the rock. As a result, a GA-ANN model with a coefficient of determination (R2) of 0.959 and root mean square error (RMSE) of 0.078 for testing datasets was selected and introduced as a new model for engineering practice; the results obtained were 0.766 and 0.098, respectively, for the developed ANN model. Furthermore, based on sensitivity analysis results, p-wave velocity has the most effect on E of the rock samples.

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References

  • Armaghani DJ, Faradonbeh RS, Rezaei H et al (2016a) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2618-8

  • Ahmad M, Ansari MK, Sharma LK, Singh R, Singh TN (2017) Correlation between strength and durability indices of rocks-soft computing approach. Proc Eng 191:458–466

    Article  Google Scholar 

  • Armaghani DJ, Mahdiyar A, Hasanipanah M et al (2016b) Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting. Rock Mech Rock Eng 49:1–11. https://doi.org/10.1007/s00603-016-1015-z

    Article  Google Scholar 

  • Armaghani DJ, Mohamad ET, Momeni E et al (2016c) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:48

    Article  Google Scholar 

  • Baykasoğlu A, Güllü H, Çanakçı H, Özbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123

    Article  Google Scholar 

  • Beiki M, Majdi A, Givshad A (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks

  • Bejarbaneh BY, Bejarbaneh EY, Amin MFM et al (2016) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-016-0983-2

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  • Demuth H, Beale M (2000) Neural Network Toolbox: For Use with Matlab: Computation, Visualization, Programming: User’s Guide, Version 4. The MathWorks

  • Dinçer I, Acar A, Çobanoğlu I, Uras Y (2004) Correlation between Schmidt hardness, uniaxial compressive strength and Young’s modulus for andesites, basalts and tuffs. Bull Eng Geol Environ 63:141–148

    Article  Google Scholar 

  • Dreyfus G (2005) Neural networks: methodology and applications. Springer, Berlin, Heidelberg

    Google Scholar 

  • Eberhart R, Shi Y (1998) Evolving artificial neural networks. Proc Int Conf Neural Networks Brain 1:PL5–PL13

    Google Scholar 

  • Eberhart R, Simpson P, Dobbins R (1996) Computational intelligence PC tools

  • Goh ATC (2000) Search for critical slip circle using genetic algorithms. Civ Eng Syst 17:181–211

  • Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock

  • Grima MA, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Sp Technol 15:259–269

    Article  Google Scholar 

  • Hajihassani M, Armaghani D, Sohaei H, Mohamad E (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

  • Hajihassani M, Jahed Armaghani D, Monjezi M et al (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci. https://doi.org/10.1007/s12665-015-4274-1

  • Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H et al (2016a) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2434-1

  • Hasanipanah M, Jahed Armaghani D, Monjezi M, Shams S (2016b) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci. https://doi.org/10.1007/s12665-016-5503-y

  • Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016c) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput. https://doi.org/10.1007/s00366-016-0447-0

  • Hasanipanah M et al (2016d) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-017-1395-y

  • Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali Aaq Qaleh A (2016e) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput. https://doi.org/10.1007/s00366-016-0462-1

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press

  • Jahed Armaghani D, Hajihassani M, Marto A et al (2015a) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess. https://doi.org/10.1007/s10661-015-4895-6

  • Jahed Armaghani D, Hajihassani M, Marto A et al (2015b) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess. https://doi.org/10.1007/s10661-015-4895-6

  • Jahed Armaghani D, Hajihassani M, Yazdani Bejarbaneh B et al (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2014.06.001

  • Jahed Armaghani D, Hasanipanah M, Mahdiyar A et al (2016a) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2598-8

  • Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016b) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci. https://doi.org/10.1016/j.ijrmms.2016.03.018

  • Jahed Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl. https://doi.org/10.1007/s00521-015-2072-z

  • Kahraman S, Gunaydin O, Alber M, Fener M (2009) Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks. Expert Syst Appl 36:6874–6878

    Article  Google Scholar 

  • Khandelwal M, Armaghani DJ (2016) Prediction of Drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34:605–620. https://doi.org/10.1007/s10706-015-9970-9

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2013) Application of an expert system to predict maximum explosive charge used per delay in surface mining. Rock Mech Rock Eng 6:1551–1558

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

    Article  Google Scholar 

  • Lee Y, Oh S-H, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning. In: Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on. IEEE, pp 765–770

  • Liang M, Mohamad ET, Faradonbeh RS et al (2016) Rock strength assessment based on regression tree technique. Eng Comput. https://doi.org/10.1007/s00366-015-0429-7

  • Liu Z, Shao J, Xu W, Wu Q (2015) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech 10:651–663

    Article  Google Scholar 

  • Mahdiyar A, Hasanipanah M, Armaghani DJ, et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput 0:1–11. doi: https://doi.org/10.1007/s00366-016-0499-1

  • 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 47:246–253

    Article  Google Scholar 

  • Marto A, Hajihassani M, Jahed Armaghani D et al (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J. https://doi.org/10.1155/2014/643715

  • Mohamad ET, Armaghani DJ, Momeni E, et al (2016) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl 1–12. doi: https://doi.org/10.1007/s00521-016-2728-3

  • Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-014-0638-0

  • Momeni E, Jahed Armaghani D, Hajihassani M, Mohd Amin MF (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2014.09.075

  • Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    Article  Google Scholar 

  • Monjezi M, Khoshalan HA, Varjani AY (2012a) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5:441–448

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Moradian ZA, Behnia M (2009) Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test. Int J Geomech 9:14–19

    Article  Google Scholar 

  • Ripley BD (1993) Statistical aspects of neural networks. Networks chaos—statistical probabilistic Asp 50:40–123

    Article  Google Scholar 

  • Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. J Rock Mech Geotech Eng 6:67–76

    Article  Google Scholar 

  • Saadat M, Hasanzade A, Khandelwal M (2015) Differential evolution algorithm for predicting blast induced ground vibrations. Int J Rock Mech Min Sci 77:97–104

    Article  Google Scholar 

  • Saemi M, Ahmadi M, Varjani A (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 59:97–105

    Article  Google Scholar 

  • Sarkar K, Tiwary A, Singh T (2010) Estimation of strength parameters of rock using artificial neural networks

  • Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017a) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507

    Article  Google Scholar 

  • Sharma LK, Vishal V, Singh TN (2017b) 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 

  • Sharma LK, Vishal V, Singh TN (2017c) Predicting CO2 permeability of bituminous coal using statistical and adaptive neuro-fuzzy analysis. J Nat Gas Sci Eng. https://doi.org/10.1016/j.jngse.2017.02.037

  • Singh J, Verma AK, Banka H et al (2016) A study of soft computing models for prediction of longitudinal wave velocity. Arab J Geosci 9:1–11

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sitton Jase D, Zeinali Y, Story Brett A (2017) Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks. Constr Build Mater 138:214–221. https://doi.org/10.1016/j.conbuildmat.2017.02.006

    Article  Google Scholar 

  • Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  • Tonnizam Mohamad E, Hajihassani M, Jahed Armaghani D, Marto A (2012) Simulation of blasting-induced air overpressure by means of Artificial Neural Networks

  • Tonnizam Mohamad E, Jahed Armaghani D, Hasanipanah M et al (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75:1–15. https://doi.org/10.1007/s12665-015-4983-5

    Article  Google Scholar 

  • Ulusay R, Hudson JA, ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006

  • Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27:225–233

    Article  Google Scholar 

  • Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 22:1685–1693

    Article  Google Scholar 

  • Yagiz S, Sezer E, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and

  • Yang Y, Zhang Q (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222

    Article  Google Scholar 

  • Yesiloglu-Gultekin N, Gokceoglu C, Sezer EA (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int J Rock Mech Min Sci. https://doi.org/10.1016/j.ijrmms.2013.05.005

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

    Article  Google Scholar 

  • Yılmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters

  • Zeinali Y, Story B (2016) Structural impairment detection using deep counter propagation neural networks. Proc Eng 145:868–875. https://doi.org/10.1016/j.proeng.2016.04.113

    Article  Google Scholar 

  • Zeinali Y, Story Brett A (2017) Competitive probabilistic neural network. Integrated Comput-Aided Eng 24(2):105–118. https://doi.org/10.3233/ICA-170540

    Article  Google Scholar 

  • Zorlu K, Gokceoglu C, Ocakoglu F et al (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158

    Article  Google Scholar 

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Correspondence to Katayoun Behzadafshar or Mahdi Hasanipanah.

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Behzadafshar, K., Sarafraz, M.E., Hasanipanah, M. et al. Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results. Bull Eng Geol Environ 78, 1527–1536 (2019). https://doi.org/10.1007/s10064-017-1210-5

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