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A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index

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Abstract

When planning rock-based projects, the brittleness index (BI) may play a significant role in the success of various projects, such as tunnel boring machines and road headers. Lack of accurate BI prediction of the rock sample may result in numerous disastrous incidents associated with rock mechanics. Adaptive neuro-fuzzy inference system (ANFIS) is a model for predicting the rock’s BI. However, the performance of this model mainly depends on its parameter values and tuning these values requires knowledge and time. This study improves the performance of ANFIS modeling using an Artificial Bee Colony (ABC) optimization algorithm to automatically optimize the parameters of ANFIS, called ANFIS_ABC. Three versions of ANFIS_ABC algorithms were proposed to predict the BI of rock, in which the ABC algorithm was applied in different model development stages. The performance of the proposed predictive models was evaluated using the rock samples collected from a tunneling project in Malaysia comprising 113 samples. The Schmidt hammer rebound number (Rn) (ranging from 20 to 61), P-wave velocity (Vp) (ranging from 2870 to 7702 m/s), and Point load index (IS50) (ranging from 0.89 to 7.1 MPa) were used as input parameters. According to the results obtained by the various performance indices, the proposed model (i.e., ANFIS_ABC_PC) was able to receive the highest accuracy level in predicting rock BI among all constructed models. The developed model may be applied with caution to relevant areas of rock mechanics.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

UCS:

Uniaxial compressive strength

TBM:

Tunnel boring machine

Gbell:

Generalized Bell

ANN:

Artificial neural network

TS:

Tensile strength

AI:

Artificial intelligence

ML:

Machine learning

PSO:

Particle swarm optimization

ABC:

Artificial bee colony

BI:

Brittleness index

FA:

Firefly algorithm

DE:

Differential evolution

GA:

Genetic algorithm

ICA:

Imperialism competitive algorithm

Ω:

Inertia weight

FIS:

Fuzzy inference system

c1 and c2 :

Acceleration coefficients

ACO:

Ant colony optimization

\({R}_{n}\) :

Schmidt hammer rebound number

RMSE:

Root mean square error

\({V}_{p}\) :

P-wave velocity

\({Is}_{50}\) :

Point load index

R2 :

Coefficient of determination

MAE:

Mean absolute error

SI:

Scatter index

VAF:

Variance account for

SVM:

Support vector machine

FS:

Feature selection

BTS:

Brazilian tensile strength

References

  1. Koopialipoor M, Noorbakhsh A, Noroozi Ghaleini E et al (2019) A new approach for estimation of rock brittleness based on non-destructive tests. Nondestruct Test Eval. https://doi.org/10.1080/10589759.2019.1623214

    Article  Google Scholar 

  2. Hussain A, Surendar A, Clementking A et al (2019) Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm. Eng Comput 35:1027–1035

    Google Scholar 

  3. Yarali O, Kahraman S (2011) The drillability assessment of rocks using the different brittleness values. Tunn Undergr Sp Technol 26:406–414

    Google Scholar 

  4. Yagiz S (2009) Assessment of brittleness using rock strength and density with punch penetration test. Tunn Undergr Sp Technol 24:66–74

    Google Scholar 

  5. Jahed Armaghani D, Asteris PG, Askarian B et al (2020) Examining hybrid and single SVM models with different kernels to predict rock brittleness. Sustainability 12:2229

    Google Scholar 

  6. Yagiz S, Ghasemi E, Adoko AC (2018) Prediction of rock brittleness using genetic algorithm and particle swarm optimization techniques. Geotech Geol Eng 36:3767–3777

    Google Scholar 

  7. Liu B, Yang H, Karekal S (2019) Effect of water content on argillization of mudstone during the tunnelling process. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-019-01947-w

    Article  Google Scholar 

  8. Yang HQ, Xing SG, Wang Q, Li Z (2018) Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides. Eng Geol 239:119–125

    Google Scholar 

  9. Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Sp Technol 81:112–120

    Google Scholar 

  10. Yarali O, Soyer E (2011) The effect of mechanical rock properties and brittleness on drillability. Sci Res Essays 6:1077–1088

    Google Scholar 

  11. Nejati HR, Moosavi SA (2017) A new brittleness index for estimation of rock fracture toughness. J Min Environ 8:83–91

    Google Scholar 

  12. Hajiabdolmajid V, Kaiser P (2003) Brittleness of rock and stability assessment in hard rock tunneling. Tunn Undergr Sp Technol 18:35–48

    Google Scholar 

  13. Altindag R (2010) Assessment of some brittleness indexes in rock-drilling efficiency. Rock Mech rock Eng 43:361–370

    Google Scholar 

  14. Yilmaz NG, Karaca Z, Goktan RM, Akal C (2009) Relative brittleness characterization of some selected granitic building stones: influence of mineral grain size. Constr Build Mater 23:370–375

    Google Scholar 

  15. Khandelwal M, Faradonbeh RS, Monjezi M et al (2017) Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Eng Comput 33:13–21

    Google Scholar 

  16. Mahdiyar A, Armaghani DJ, Marto A et al (2018) Rock tensile strength prediction using empirical and soft computing approaches. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-018-1405-4

    Article  Google Scholar 

  17. Jahed Armaghani D, Mohd Amin MF, Yagiz S et al (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186. https://doi.org/10.1016/j.ijrmms.2016.03.018

    Article  Google Scholar 

  18. Kaunda RB, Asbury B (2016) Prediction of rock brittleness using nondestructive methods for hard rock tunneling. J Rock Mech Geotech Eng 8:533–540

    Google Scholar 

  19. Yang HQ, Zeng YY, Lan YF, Zhou XP (2014) Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading. Int J rock Mech Min Sci 69:59–66

    Google Scholar 

  20. Guo Z, Chapman M, Li X (2012) A shale rock physics model and its application in the prediction of brittleness index, mineralogy, and porosity of the Barnett Shale. In: SEG technical program expanded abstracts 2012. Society of Exploration Geophysicists, pp 1–5

  21. Tarasov B, Potvin Y (2013) Universal criteria for rock brittleness estimation under triaxial compression. Int J Rock Mech Min Sci 59:57–69

    Google Scholar 

  22. Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808–814

    Google Scholar 

  23. Yagiz S, Karahan H (2015) Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. Int J Rock Mech Min Sci 80:308–315

    Google Scholar 

  24. Meng F, Zhou H, Zhang C et al (2015) Evaluation methodology of brittleness of rock based on post-peak stress–strain curves. Rock Mech Rock Eng 48:1787–1805

    Google Scholar 

  25. Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst Appl 37:2265–2272

    Google Scholar 

  26. Sun D, Lonbani M, Askarian B et al (2020) Investigating the applications of machine learning techniques to predict the rock brittleness index. Appl Sci 10:1691

    Google Scholar 

  27. Armaghani DJ, Asteris PG (2020) A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05244-4

    Article  Google Scholar 

  28. Asteris PG, Douvika MG, Karamani CA, Skentou AD, Chlichlia K, Cavaleri L, Daras T, Armaghani DJ, Zaoutis TE (2020) A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon. Comput Model Eng Sci. https://doi.org/10.32604/cmes.2020.013280

    Article  Google Scholar 

  29. Harandizadeh H, Armaghani DJ, Mohamad ET (2020) Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets. Neural Comput Appl 32:14047–14067. https://doi.org/10.1007/s00521-020-04803-z

    Article  Google Scholar 

  30. Harandizadeh H, Armaghani DJ (2020) Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Appl Soft Comput. p 106904

  31. Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:4016003

    Google Scholar 

  32. Zhou J, Qiu Y, Zhu S et al (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015

    Google Scholar 

  33. Zeng J, Roy B, Kumar D et al (2021) Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Eng Comput. https://doi.org/10.1007/s00366-020-01225-2

    Article  Google Scholar 

  34. Zeng J, Asteris PG, Mamou AP et al (2021) The effectiveness of ensemble-neural network techniques to predict peak uplift resistance of buried pipes in reinforced sand. Appl Sci 11:908

    Google Scholar 

  35. Wang S, Zhou J, Li C et al (2021) Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. J Cent South Univ 28:527–542

    Google Scholar 

  36. Zhao J, Nguyen H, Nguyen-Thoi T et al (2021) Improved Levenberg–Marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams. Eng Comput. https://doi.org/10.1007/s00366-020-01267-6

    Article  Google Scholar 

  37. Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390. https://doi.org/10.1016/j.soildyn.2020.106390

    Article  Google Scholar 

  38. Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229

    Google Scholar 

  39. Mahdevari S, Haghighat HS, Torabi SR (2013) A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunn Undergr Sp Technol 38:59–68

    Google Scholar 

  40. Mahdevari S, Torabi SR, Monjezi M (2012) Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon. Int J Rock Mech Min Sci 55:33–44

    Google Scholar 

  41. Parsajoo M, Armaghani DJ, Mohammed AS, Khari M, Jahandari S (2021) Tensile strength prediction of rock material using non-destructive tests: A comparative intelligent study. Transp Geotech 31:100652.

    Google Scholar 

  42. Zhou J, Qiu Y, Khandelwal M et al (2021) Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min Sci 145:104856

    Google Scholar 

  43. Zhou J, Chen C, Wang M, Khandelwal M (2021) Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2021.07.011

    Article  Google Scholar 

  44. Zhou J, Shen X, Qiu Y et al (2021) Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomech Geophys Geo-Energy Geo-Resour. https://doi.org/10.1007/s40948-021-00285-y

    Article  Google Scholar 

  45. Kardani N, Bardhan A, Kim D et al (2021) Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH. ANFIS-BBO and ANFIS-IPSO. J Build Eng 35:102105

    Google Scholar 

  46. Li Y, Hishamuddin FN, Mohammed AS, Armaghani DJ, Ulrikh DV, Dehghanbanadaki A, Azizi A (2021) The effects of rock index tests on prediction of tensile strength of granitic samples: a neuro-fuzzy intelligent system. Sustainability 13(19):10541

    Google Scholar 

  47. Li Z, Yazdani Bejarbaneh B, Asteris PG, Koopialipoor M, Armaghani DJ, Tahir MM (2021) A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Comput 25(17):11877–11895

    Google Scholar 

  48. Parsajoo M, Mohammed AS, Yagiz S, Armaghani DJ, Khandelwal M (2021) An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2021.05.010

    Article  Google Scholar 

  49. Al-Bared MA, Mustaffa Z, Armaghani DJ, Marto A, Yunus NZ, Hasanipanah M (2021) Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive. Transp Geotech 30:100627

    Google Scholar 

  50. Armaghani DJ, Harandizadeh H, Ehsan Momeni HMJZ (2021) An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity. Artif Intell Rev. https://doi.org/10.1007/s10462-021-10065-5

    Article  Google Scholar 

  51. Asteris PG, Apostolopoulou M, Armaghani DJ, Cavaleri L, Chountalas AT, Guney D, Hajihassani M, Hasanipanah M, Khandelwal M, Karamani C, Koopialipoor M, Kotsonis E, Le T-T, Lourenço PB, Ly H-B, Moropoulou A, Nguyen HJ (2020) On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength. Metaheuristic Comput Appl 1:63–99

    Google Scholar 

  52. Armaghani DJ, Asteris PG, Fatemi SA et al (2020) On the use of neuro-swarm system to forecast the pile settlement. Appl Sci 10:1904

    Google Scholar 

  53. Yang H, Wang Z, Song K (2020) A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance. Eng Comput. https://doi.org/10.1007/s00366-020-01217-2

    Article  Google Scholar 

  54. Armaghani DJ, Hajihassani M, Mohamad ET et al (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396

    Google Scholar 

  55. Karaboga D, Kaya E (2016) An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Appl Soft Comput 49:423–436

    Google Scholar 

  56. Dehghanbanadaki A, Khari M, Amiri ST, Armaghani DJ (2020) Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study. Soft Comput. https://doi.org/10.1007/s00500-020-05435-0

    Article  Google Scholar 

  57. Shahnazar A, Nikafshan Rad H, Hasanipanah M et al (2017) A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci. https://doi.org/10.1007/s12665-017-6864-6

    Article  Google Scholar 

  58. Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2746-1

    Article  Google Scholar 

  59. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

  60. Telikani A, Gandomi AH, Shahbahrami A, Dehkordi MN (2020) Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony. Expert Syst Appl 144:113097

    Google Scholar 

  61. Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci (Ny) 192:120–142

    Google Scholar 

  62. Lin JC-W, Liu Q, Fournier-Viger P et al (2016) A sanitization approach for hiding sensitive itemsets based on particle swarm optimization. Eng Appl Artif Intell 53:1–18

    Google Scholar 

  63. Mohamad ET, Faradonbeh RS, Armaghani DJ et al (2017) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28:393–406

    Google Scholar 

  64. Xu C, Gordan B, Koopialipoor M et al (2019) Improving performance of retaining walls under dynamic conditions developing an optimized ANN based on ant colony optimization technique. IEEE Access 7:94692–94700

    Google Scholar 

  65. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    MathSciNet  MATH  Google Scholar 

  66. Hasanipanah M, Zhang W, Armaghani DJ, Rad HN (2020) The potential application of a new intelligent based approach in predicting the tensile strength of rock. IEEE Access 8:57148–57157

    Google Scholar 

  67. Ulusay R, Hudson JA ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. Comm Test methods Int Soc Rock Mech Compil arranged by ISRM Turkish Natl Group, Ankara, Turkey 628

  68. Zhou J, Koopialipoor M, Li E, Armaghani DJ (2020) Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bull Eng Geol Environ 79:4265

    Google Scholar 

  69. Duan J, Asteris PG, Nguyen H et al (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. https://doi.org/10.1007/s00366-020-01003-0

    Article  Google Scholar 

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

    Google Scholar 

  71. Khari M, Armaghani DJ, Dehghanbanadaki A (2020) Prediction of lateral deflection of small-scale piles using hybrid PSO–ANN model. Arab J Sci Eng 45:3499–3509. https://doi.org/10.1007/s13369-019-0413

    Article  Google Scholar 

  72. Alavi Nezhad Khalil Abad SV, Yilmaz M, Jahed Armaghani D, Tugrul A (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2456-8

    Article  Google Scholar 

  73. Armaghani DJ, Mohamad ET, Narayanasamy MS et al (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Sp Technol 63:29–43

    Google Scholar 

  74. 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

    Google Scholar 

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Authors and Affiliations

  1. Department of Mining Engineering, Shams Co, no. 1380. way no. 2818, Postal Code 114, Shatti Al Qurum, P.O.Box 1941, Muscat, Sultanate of Oman

    Maryam Parsajoo

  2. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk, 454080, Russia

    Danial Jahed Armaghani

  3. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121, HeraklionAthens, GR, Greece

    Panagiotis G. Asteris

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  1. Maryam Parsajoo
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  2. Danial Jahed Armaghani
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  3. Panagiotis G. Asteris
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Correspondence to Danial Jahed Armaghani.

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Parsajoo, M., Armaghani, D.J. & Asteris, P.G. A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index. Neural Comput & Applic 34, 3263–3281 (2022). https://doi.org/10.1007/s00521-021-06600-8

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