A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young’s modulus and unconfined compressive strength of rock


To design the tunnel excavations, the most important parameters are the engineering properties of rock, e.g., Young’s modulus (E) and unconfined compressive strength (UCS). Numerous researchers have attempted to propose methods to estimate E and UCS indirectly. This task is complex due to the difficulty of preparing and carrying out such experiments in a laboratory. The main aim of the present study is to propose a new and efficient machine learning model to predict E and UCS. The proposed model combines the extreme gradient boosting machine (XGBoost) with the firefly algorithm (FA), called the XGBoost-FA model. To verify the feasibility of the XGBoost-FA model, a support vector machine (SVM), classical XGBoost, and radial basis function neural network (RBFN) were also employed. Forty-five granite sample sets, collected from the Pahang-Selangor tunnel, Malaysia, were used in the modeling. Several statistical functions, such as coefficient of determination (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE) were calculated to check the acceptability of the methods mentioned above. A review of the results of the proposed models revealed that the XGBoost-FA was more feasible than the others in predicting both E and UCS and could generalize.

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  1. 1.

    Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45

    Article  Google Scholar 

  2. 2.

    Monjezi M, Khoshalan HA, Razifard M (2012) A neuro-genetic network for predicting uniaxial compressive strength of rocks. Geotech Geol Eng 30(4):1053–1062

    Article  Google Scholar 

  3. 3.

    Yilmaz I, Sendir H (2002) Correlation of Schmidt hardness with unconfined compressive strength and Young’s modulus in gypsum from Sivas (Turkey). Eng Geol 66(3):211–219

    Article  Google Scholar 

  4. 4.

    Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the unconfined compressive strength and modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17:61–72

    Article  Google Scholar 

  5. 5.

    Kahraman S (2014) The determination of uniaxial compressive strength from point load strength for pyroclastic rocks. Eng Geol 170:33–42

    Article  Google Scholar 

  6. 6.

    Kahraman S (2001) Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Int J Rock Mech Min Sci 38:981–994

    Article  Google Scholar 

  7. 7.

    Cobanglu I, Celik S (2008) Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bull Eng Geol Environ 67:491–498

    Article  Google Scholar 

  8. 8.

    Kahraman S, Fener M, Kozman E (2012) Predicting the compressive and tensile strength of rocks from indentation hardness index. J S Afr Inst Min Metall 112(5):331–339

    Google Scholar 

  9. 9.

    Nazir R, Momeni E, Jahed Armaghani D, Mohd Amin MF (2013) Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples. Electr J Geotech Eng 18:1737–1746

    Google Scholar 

  10. 10.

    Singh RN, Hassani FP, Elkington PAS (1983) The application of strength and deformation index testing to the stability assessment of coal measures excavations. Proceeding of 24th US symposium on rock mechanism. Texas A and M University, AEG, Balkema, pp 599–609

    Google Scholar 

  11. 11.

    Yasar E, Erdogan Y (2004) Estimation of rock physiomechanical properties using hardness methods. Eng Geol 71:281–288

    Article  Google Scholar 

  12. 12.

    Nazir R, Momeni E, JahedArmaghani D, Mohd Amin MF (2013) Prediction of unconfined compressive strength of limestone rock samples using L-type Schmidt hammer. Electr J Geotech Eng 18:1767–1775

    Google Scholar 

  13. 13.

    Karaman K, Kesimal A (2014) A comparative study of Schmidt hammer test methods for estimating the uniaxial compressive strength of rocks. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-014-0617-5

    Article  Google Scholar 

  14. 14.

    Moradian ZA, Ghazvinian AH, Ahmadi M, Behnia M (2010) Predicting slake durability index of soft sandstone using indirect tests. Int J Rock Mech Min Sci 47(4):666–671

    Article  Google Scholar 

  15. 15.

    Yagiz S (2011) Correlation between slake durability and rock properties for some carbonate rocks. Bull Eng Geol Environ 70(3):377–383

    Article  Google Scholar 

  16. 16.

    Sulukcu S, Ulusay R (2001) Evaluation of the block punch index test with particular reference to the size effect, failure mechanism and its effectiveness in predicting rock strength. Int J Rock Mech Min Sci 38:1091–1111

    Article  Google Scholar 

  17. 17.

    Basu A, Aydin A (2006) Predicting uniaxial compressive strength by point load test: significance of cone penetration. Rock Mech Rock Eng 39:483–490

    Article  Google Scholar 

  18. 18.

    Tandon RS, Gupta V (2015) Estimation of strength characteristics of different Himalayan rocks from Schmidt hammer rebound, point load index, and compressional wave velocity. Bull Eng Geol Environ 74:521–533

    Article  Google Scholar 

  19. 19.

    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(4):803–810

    Article  Google Scholar 

  20. 20.

    Dehghan S, Sattari GH, Chehreh CS, Aliabadi MA (2010) Prediction of unconfined compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Technol 20:0041–0046

    Google Scholar 

  21. 21.

    Beiki M, Majdi A, 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 

  22. 22.

    O’Rourke JE (1989) Rock index properties for geoengineering in underground development. Min Eng 41:106–110

    Google Scholar 

  23. 23.

    Rezaei M, Majdi A, Monjezi M (2012) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 24(1):233–241

    Article  Google Scholar 

  24. 24.

    Singh R, Vishal V, Singh TN et al (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 23:499–506

    Article  Google Scholar 

  25. 25.

    Suthar M (2020) Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes. Neural Comput Appl 32:9019–9028. https://doi.org/10.1007/s00521-019-04411-6

    Article  Google Scholar 

  26. 26.

    Kumar S, Prasad A (2019) Strength retrieval of artificially cemented bauxite residue using machine learning: an alternative design approach based on response surface methodology. Neural Comput Appl 31:6535–6548

    Article  Google Scholar 

  27. 27.

    Tinoco J, Alberto A, da Venda P et al (2019) A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04399-z

    Article  Google Scholar 

  28. 28.

    Tekin E, Akbas SO (2019) Predicting groutability of granular soils using adaptive neuro-fuzzy inference system. Neural Comput Appl 31:1091–1101

    Article  Google Scholar 

  29. 29.

    Sun Y et al (2019) Determination of Young’s modulus of jet grouted coalcretes using an intelligent model. Eng Geol 252:43–53

    Article  Google Scholar 

  30. 30.

    Sun Y, Zhang J, Li G, Wang Y, Sun J, Jiang C (2019) Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes. Int J Numer Anal Meth Geomech 43(4):801–813

    Article  Google Scholar 

  31. 31.

    Sun Y, Li G, Zhang N, Chang Q, Xu J, Zhang J (2020) Development of ensemble learning models to evaluate the strength of coal-grout materials. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2020.09.002

    Article  Google Scholar 

  32. 32.

    Zhang WG et al (2020) State-of-the-art review of soft computing applications in underground excavations. Geosci Front 11:1095–1106

    Article  Google Scholar 

  33. 33.

    Zhang WG, Goh ATC (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52

    Article  Google Scholar 

  34. 34.

    Zhang WG et al (2020) Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling. Undergr Space. https://doi.org/10.1016/j.undsp.2019.12.003

    Article  Google Scholar 

  35. 35.

    Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Sundaram Narayanasamy M, Mohd Amin MF (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 Env 74:1301–1319

    Article  Google Scholar 

  36. 36.

    Jahed Armaghani D, Tonnizam Mohamad E, Sundaram Narayanasamy M, Narita N, Yagiz S (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43

    Article  Google Scholar 

  37. 37.

    Gao J, Koopialipoor M, Armaghani DJ, Ghabussi A, Baharom S, Morasaei A, Shariati A, Khorami M, Zhou J (2020) Evaluating the bond strength of FRP in concrete samples using machine learning methods. Smart Struct Syst 26(4):403–418

    Google Scholar 

  38. 38.

    Gao J, Amar MN, Motahari MR, Hasanipanah M, Armaghani DJ (2020) Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms. Eng Comput. https://doi.org/10.1007/s00366-020-01059-y

    Article  Google Scholar 

  39. 39.

    Zhou J, Qiu Y, Zhu S, Armaghani DJ, Khandelwal M, Mohamad ET (2020) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underg Space. https://doi.org/10.1016/j.undsp.2020.05.008

    Article  Google Scholar 

  40. 40.

    Zhou J, Qiu Y, Armaghani DJ, Zhang W, Li C, Zhu S, Tarinejad R (2020) Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques. Geosci Front. https://doi.org/10.1016/j.gsf.2020.09.020

    Article  Google Scholar 

  41. 41.

    Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50(4):629–644

    Article  Google Scholar 

  42. 42.

    Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civil Eng 30(5):04016003

    Article  Google Scholar 

  43. 43.

    Zhou J, Qiu Y, Zhu S, Armaghani DJ, Li C, Nguyen H, Yagiz S (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015. https://doi.org/10.1016/j.engappai.2020.104015

    Article  Google Scholar 

  44. 44.

    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 

  45. 45.

    Sonmez H, Tuncay E, 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(5):717–729

    Article  Google Scholar 

  46. 46.

    Mishra DA, 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 

  47. 47.

    Cevik A, Sezer EA, Cabalar AF, Gokceoglu C (2011) Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput 11(2):2587–2594

    Article  Google Scholar 

  48. 48.

    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 62:113–122

    Article  Google Scholar 

  49. 49.

    Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38(2):269–284

    Article  Google Scholar 

  50. 50.

    Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng Geol 66(1):39–51

    Article  Google Scholar 

  51. 51.

    Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3):141–158

    Article  Google Scholar 

  52. 52.

    Yagiz S, Sezer EA, 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 modulus of elasticity for carbonate rocks. Int J Numer Anal Method 36(14):1636–1650

    Article  Google Scholar 

  53. 53.

    Sun Y, Li G, Zhang J (2020) Developing hybrid machine learning models for estimating the unconfined compressive strength of jet grouting composite: a comparative study. Appl Sci 10:1612. https://doi.org/10.3390/app10051612

    Article  Google Scholar 

  54. 54.

    Barham WS, Rababah SR, Aldeeky HH et al (2020) Mechanical and physical based artificial neural network models for the prediction of the unconfined compressive strength of rock. Geotech Geol Eng 38:4779–4792. https://doi.org/10.1007/s10706-020-01327-0

    Article  Google Scholar 

  55. 55.

    Sun J, Zhang J, Gu Y, Huang Y, Sun Y, Ma G (2019) Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression. Constr Build Mater 207:440–449

    Article  Google Scholar 

  56. 56.

    Lu X, Zhou W, Ding X, Shi X, Luan B, Li M (2019) Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill. IEEE Access 7:72125–72133

    Article  Google Scholar 

  57. 57.

    Rezaei M, Asadizadeh M (2020) Predicting unconfined compressive strength of intact rock using new hybrid intelligent models. J Min Environ 11(1):231–246

    Google Scholar 

  58. 58.

    ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Hudson JA (ed) Ulusay R. International Society for Rock Mechanics, Suggested methods prepared by the commission on testing methods

    Google Scholar 

  59. 59.

    Tianqi C, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, ACM, pp 785–794

  60. 60.

    Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. IJBIC 2:78

    Article  Google Scholar 

  61. 61.

    Wusi C, Hasanipanah M, Nikafshan HR, Jahed DA, Tahir MM (2019) A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Eng Comput. https://doi.org/10.1007/s00366-019-00895-x

    Article  Google Scholar 

  62. 62.

    Haiqing Y, Nikafshan HR, Hasanipanah M, Bakhshandeh HA, Nekouie A (2019) Prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms. Natl Resour Res. https://doi.org/10.1007/s11053-019-09597-z

    Article  Google Scholar 

  63. 63.

    Shan S (2016) Support vector machine. Machine learning models and algorithms for big data classification. Springer, Boston, pp 207–235

    Google Scholar 

  64. 64.

    Nikafshan Rad H, Hasanipanah M, Rezaei M, Lotfi Eghlim A (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34:709–717

    Article  Google Scholar 

  65. 65.

    Hossam F, Aljarah I, Mirjalili S (2017) Evolving radial basis function networks using moth–flame optimizer. Handbook of neural computation. Academic Press, London, pp 537–550

    Google Scholar 

  66. 66.

    Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci Iran 18(1):53–58

    MATH  Article  Google Scholar 

  67. 67.

    Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22(7–8):1637–1643

    Article  Google Scholar 

  68. 68.

    Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast induced ground vibration developing a CART model. Eng Comput 33(2):307–316

    Article  Google Scholar 

  69. 69.

    Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050

    Article  Google Scholar 

  70. 70.

    Qi C, Fourie A, Chen Q (2018) Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill. Constr Build Mater 159:473–478

    Article  Google Scholar 

  71. 71.

    Hasanipanah M, Bakhshandeh Amnieh H, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024

    Article  Google Scholar 

  72. 72.

    Qi C, Fourie A, Du X, Tang X (2018) Prediction of open stope hangingwall stability using random forests. Nat Hazards 92(2):1179–1197

    Article  Google Scholar 

  73. 73.

    Hasanipanah M et al (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm based fuzzy system. Int J Environ Sci Technol 15(3):551–560

    Article  Google Scholar 

  74. 74.

    Luo Z, Hasanipanah M, Amnieh HB, Brindhadevi K, Tahir MM (2019) GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles. Eng Comput. https://doi.org/10.1007/s00366-019-00858-2

    Article  Google Scholar 

  75. 75.

    Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2020) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurement 131:35–41

    Article  Google Scholar 

  76. 76.

    Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2020) Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput 36(2):703–712

    Article  Google Scholar 

  77. 77.

    Hasanipanah M, Bakhshandeh HA (2020) Developing a new uncertain rule-based fuzzy approach for evaluating the blast-induced backbreak. Eng Comput. https://doi.org/10.1007/s00366-019-00919-6

    Article  Google Scholar 

  78. 78.

    Ceryan N, Samui P (2020) Application of soft computing methods in predicting uniaxial compressive strength of the volcanic rocks with different weathering degree. Arab J Geosci 13(7):1–18

    Article  Google Scholar 

  79. 79.

    Ding X, Hasanipanah M, Rad HN, Zhou W (2020) Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm. Eng Comput. https://doi.org/10.1007/s00366-020-00937-9

    Article  Google Scholar 

  80. 80.

    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

    Article  Google Scholar 

  81. 81.

    Kumar M, Samui P (2020) Reliability analysis of settlement of pile group in clay using LSSVM, GMDH. GPR Geotechn Geolog Eng 38(6):6717–6730

    Article  Google Scholar 

  82. 82.

    Qi C (2020) Big data management in the mining industry. Int J Miner Metall Mater 27(2):131–139

    Article  Google Scholar 

  83. 83.

    Hasanipanah M, Meng D, Keshtegar B, Trung NT, Thai DK (2020) Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05252-4

    Article  Google Scholar 

  84. 84.

    Kaloop MR, Kumar D, Samui P, Hu JW, Kim D (2020) Compressive strength prediction of high-performance concrete using gradient tree boosting machine. Constr Build Mater 264:120198

    Article  Google Scholar 

  85. 85.

    Hasanipanah M, Bakhshandeh HA (2020) A fuzzy rule-based approach to address uncertainty in risk assessment and prediction of blast-induced flyrock in a quarry. Nat Resour Res. https://doi.org/10.1007/s11053-020-09616-4

    Article  Google Scholar 

  86. 86.

    Kumar S, Rai B, Biswas R, Samui P, Kim D (2020) Prediction of rapid chloride permeability of self-compacting concrete using multivariate adaptive regression spline and minimax probability machine regression. J Build Eng 32:101490

    Article  Google Scholar 

  87. 87.

    Hasanipanah M, Keshtegar B, Thai DK, Troung NT (2020) An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting. Eng Comput. https://doi.org/10.1007/s00366-020-01105-9

    Article  Google Scholar 

  88. 88.

    Asheghi R, Abbaszadeh Shahri A, Khorsand Zak M (2019) Prediction of uniaxial compressive strength of different quarried rocks using metaheuristic algorithm. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04046-8

    Article  Google Scholar 

  89. 89.

    Rezaei M (2017) Feasibility of novel techniques to predict the elastic modulus of rocks based on the laboratory data. Int J Geotech Eng. https://doi.org/10.1080/19386362.2017.1397873

    Article  Google Scholar 

  90. 90.

    Jing H, Rad HN, Hasanipanah M, Armaghani DJ, Qasem SN (2020) Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS. Eng Comput. https://doi.org/10.1007/s00366-020-00977-1

    Article  Google Scholar 

  91. 91.

    Fattahi H (2017) Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values. Comput Geosci 21:665–681

    MathSciNet  Article  Google Scholar 

  92. 92.

    Jahed Armaghani D et al (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 

  93. 93.

    Yang Y, Zhang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30(4):207–222

    Article  Google Scholar 

  94. 94.

    Xie C, Nguyen H, Bui XN, Choi Y, Zhou J, Nguyen-Trang T (2020) Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms. Geosci Front. https://doi.org/10.1016/j.gsf.2020.11.005

  95. 95.

    Zhou J, Li C, Arslan CA, Hasanipanah M, Amnieh HB (2019) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng Comput. https://doi.org/10.1007/s00366-019-00822-0

  96. 96.

    Li E, Zhou J, Shi X, Armaghani DJ, Yu Z, Chen X, Huang P (2020) Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill. Eng Comput. https://doi.org/10.1007/s00366-020-01014-x

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Cao, J., Gao, J., Nikafshan Rad, H. et al. A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young’s modulus and unconfined compressive strength of rock. Engineering with Computers (2021). https://doi.org/10.1007/s00366-020-01241-2

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  • Rock properties
  • XGBoost
  • Machine learning
  • Firefly algorithm