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A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration

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Abstract

The relevance vector machine (RVM) is considered a robust machine learning method and its superior performance has been confirmed through many successful engineering applications. To improve the performance of the RVM model, three single kernel functions, and three multikernel functions, including two newly proposed multikernel functions, tenfold cross-validation, and the hybrid particle swarm optimization with grey wolf optimizer (HPSOGWO) algorithm were combined to develop an artificial intelligence (AI) model framework. Afterwards, a new application of the RVM method was used and introduced for two different datasets of the blast-induced ground vibration. In addition, an artificial neural network (ANN) model and seven empirical equations were also developed for comparison purposes, and their prediction performances were evaluated considering three performance metrics, i.e., root mean square error (RMSE), correlation coefficient (R2), and mean absolute error (MAE). The obtained results showed that the multikernel RVM model can provide better performance capacity than the single-kernel RVM model. As a result, the AI models were found to be more applicable than the empirical equations in estimating blast-induced ground vibration. The prediction performance results of these models confirmed that the selected database has a great impact on the prediction capacity. Therefore, it is a common act to compare the performance of various models based on the selected database before selecting an optimal predictive model. The proposed model in this study provides new theoretical and practical support for the prediction of blast-induced ground vibration and can be utilized by other researchers in similar fields.

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References

  1. Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659. https://doi.org/10.1016/j.tust.2018.08.029

    Article  Google Scholar 

  2. Zhou J, Li C, Koopialipoor M et al (2020) Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC). Int J Min Reclam Environ 00:1–21. https://doi.org/10.1080/17480930.2020.1734151

    Article  Google Scholar 

  3. Zhou J, Guo H, Koopialipoor M et al (2020) Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Eng Comput. https://doi.org/10.1007/s00366-019-00908-9

    Article  Google Scholar 

  4. Zhang H, Zhou J, Jahed Armaghani D et al (2020) A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Appl Sci 10:869. https://doi.org/10.3390/app10030869

    Article  Google Scholar 

  5. Koopialipoor M, Fallah A, Armaghani DJ et al (2019) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput 35:243–256. https://doi.org/10.1007/s00366-018-0596-4

    Article  Google Scholar 

  6. 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 34:354–375. https://doi.org/10.1080/10589759.2019.1623214

    Article  Google Scholar 

  7. 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. https://doi.org/10.1007/s10064-020-01788-w

    Article  Google Scholar 

  8. Mahdiyar A, Jahed Armaghani D, Koopialipoor M et al (2020) Practical risk assessment of ground vibrations resulting from blasting, using gene expression programming and Monte Carlo simulation techniques. Appl Sci 10:472. https://doi.org/10.3390/app10020472

    Article  Google Scholar 

  9. Ribeiro Junior RF, de Almeida FA, Gomes GF (2020) Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04868-w

    Article  Google Scholar 

  10. Maral H, Alpman E, Kavurmacıoğlu L, Camci C (2019) A genetic algorithm based aerothermal optimization of tip carving for an axial turbine blade. Int J Heat Mass Transf. https://doi.org/10.1016/j.ijheatmasstransfer.2019.07.069

    Article  Google Scholar 

  11. Hemmatian B, Casal J, Planas E et al (2020) Prediction of BLEVE mechanical energy by implementation of artificial neural network. J Loss Prev Process Ind 63:104021. https://doi.org/10.1016/j.jlp.2019.104021

    Article  Google Scholar 

  12. Chen R, Zhang P, Wu H et al (2019) Prediction of shield tunneling-induced ground settlement using machine learning techniques. Front Struct Civ Eng 13:1363–1378. https://doi.org/10.1007/s11709-019-0561-3

    Article  Google Scholar 

  13. Asteris PG, Tsaris AK, Cavaleri L et al (2016) Prediction of the fundamental period of infilled rc frame structures using artificial neural networks. Comput Intell Neurosci. https://doi.org/10.1155/2016/5104907

    Article  Google Scholar 

  14. Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl 31:4837–4847. https://doi.org/10.1007/s00521-018-03965-1

    Article  Google Scholar 

  15. 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 

  16. Asteris P, Roussis P, Douvika M (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17:1344. https://doi.org/10.3390/s17061344

    Article  Google Scholar 

  17. Apostolopoulou M, Armaghani DJ, Bakolas A et al (2019) Compressive strength of natural hydraulic lime mortars using soft computing techniques. Proc Struct Integr 17:914–923. https://doi.org/10.1016/j.prostr.2019.08.122

    Article  Google Scholar 

  18. Asteris PG, Apostolopoulou M, Skentou AD, Moropoulou A (2019) Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Comput Concr 24:329–345. https://doi.org/10.12989/cac.2019.24.4.329

    Article  Google Scholar 

  19. Xu H, Zhou J, Asteris GP et al (2019) Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate. Appl Sci 9:3715. https://doi.org/10.3390/app9183715

    Article  Google Scholar 

  20. Chen H, Asteris P, Jahed Armaghani D et al (2019) Assessing dynamic conditions of the retaining wall: developing two hybrid intelligent models. Appl Sci 9:1042. https://doi.org/10.3390/app9061042

    Article  Google Scholar 

  21. Asteris PG, Nozhati S, Nikoo M et al (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26:1146–1153. https://doi.org/10.1080/15376494.2018.1430874

    Article  Google Scholar 

  22. Sharma M, Singh G, Singh R (2017) Stark assessment of lifestyle based human disorders using data mining based learning techniques. IRBM 38:305–324

    Article  Google Scholar 

  23. Gautam R, Kaur P, Sharma M (2019) A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings. Prog Artif Intell 8:401–424

    Article  Google Scholar 

  24. Ruiming F (2019) Wavelet based relevance vector machine model for monthly runoff prediction. Water Qual Res J 54:134–141. https://doi.org/10.2166/wcc.2018.196

    Article  Google Scholar 

  25. Widodo A, Yang BS (2011) Application of relevance vector machine and survival probability to machine degradation assessment. Expert Syst Appl 38:2592–2599. https://doi.org/10.1016/j.eswa.2010.08.049

    Article  Google Scholar 

  26. Agrawal RK, Muchahary F, Tripathi MM (2019) Ensemble of relevance vector machines and boosted trees for electricity price forecasting. Appl Energy 250:540–548. https://doi.org/10.1016/j.apenergy.2019.05.062

    Article  Google Scholar 

  27. Lima CAM, Coelho ALV, Madeo RCB, Peres SM (2016) Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Comput Appl 27:791–804. https://doi.org/10.1007/s00521-015-1953-5

    Article  Google Scholar 

  28. Imani M, Kao HC, Lan WH, Kuo CY (2018) Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Glob Planet Change 161:211–221. https://doi.org/10.1016/j.gloplacha.2017.12.018

    Article  Google Scholar 

  29. Zhang J, Yan J, Wu W, Liu Y (2019) Research on short-term forecasting and uncertainty of wind turbine power based on relevance vector machine. Energy Proc 158:229–236. https://doi.org/10.1016/j.egypro.2019.01.081

    Article  Google Scholar 

  30. Chen F, Cheng M, Tang B et al (2020) A novel optimized multi-kernel relevance vector machine with selected sensitive features and its application in early fault diagnosis for rolling bearings. Measurement 156:107583. https://doi.org/10.1016/j.measurement.2020.107583

    Article  Google Scholar 

  31. Yang F, Sun W, Lin G, Zhang W (2016) Prediction of military vehicle’s drawbar pull based on an improved relevance vector machine and real vehicle tests. Sensors 16:1–20. https://doi.org/10.3390/s16030351

    Article  Google Scholar 

  32. Chen F, Yang Y, Tang B et al (2020) Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation. Measurement 151:107116. https://doi.org/10.1016/j.measurement.2019.107116

    Article  Google Scholar 

  33. Wang T, He Y, Shi T et al (2019) Transformer health management based on self-powered RFID sensor and multiple kernel RVM. IEEE Trans Instrum Meas 68:818–828. https://doi.org/10.1109/TIM.2018.2851840

    Article  Google Scholar 

  34. Zhao W, Wang L (2019) Multiple-kernel MRVM with LBFO algorithm for fault diagnosis of broken rotor bar in induction motor. IEEE Access 7:182173–182184. https://doi.org/10.1109/ACCESS.2019.2958689

    Article  Google Scholar 

  35. Liu Y, Ye Y, Wang Q et al (2019) Predicting the loose zone of roadway surrounding rock usingwavelet relevance vector machine. Appl Sci. https://doi.org/10.3390/app9102064

    Article  Google Scholar 

  36. Chen Y, Zhang T, Zhao W et al (2019) Rotating machinery fault diagnosis based on improved multiscale amplitude-aware permutation entropy and multiclass relevance vector machine. Sensors. https://doi.org/10.3390/s19204542

    Article  Google Scholar 

  37. An JY, Meng FR, You ZH et al (2016) Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model. Protein Sci 25:1825–1833. https://doi.org/10.1002/pro.2991

    Article  Google Scholar 

  38. Gou Y, Shi X, Zhou J et al (2020) Attenuation assessment of blast-induced vibrations derived from an underground mine. Int J Rock Mech Min Sci 127:104220. https://doi.org/10.1016/j.ijrmms.2020.104220

    Article  Google Scholar 

  39. Nguyen H, Bui XN, Moayedi H (2019) A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophys 67:1025–1037. https://doi.org/10.1007/s11600-019-00304-3

    Article  Google Scholar 

  40. Nguyen H, Bui X-N, Tran Q-H et al (2019) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl Sci 1:125. https://doi.org/10.1007/s42452-018-0136-2

    Article  Google Scholar 

  41. Zhang X, Nguyen H, Bui XN et al (2019) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res. https://doi.org/10.1007/s11053-019-09492-7

    Article  Google Scholar 

  42. Hasanipanah M, Monjezi M, Shahnazar A et al (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297. https://doi.org/10.1016/j.measurement.2015.07.019

    Article  Google Scholar 

  43. Yu Z, Shi X, Zhou J et al (2020) Effective assessment of blast-induced ground vibration using an optimized random forest model based on a Harris Hawks optimization algorithm. Appl Sci 10:1403. https://doi.org/10.3390/app10041403

    Article  Google Scholar 

  44. Nguyen H, Drebenstedt C, Bui XN, Bui DT (2019) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09470-z

    Article  Google Scholar 

  45. Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blast-induced ground vibration. Eng Comput 27:117–125. https://doi.org/10.1007/s00366-009-0157-y

    Article  Google Scholar 

  46. Khandelwal M, Armaghani DJ, Faradonbeh RS et al (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Eng Comput 33:45–53. https://doi.org/10.1007/s00366-016-0455-0

    Article  Google Scholar 

  47. Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32:705–715. https://doi.org/10.1007/s00366-016-0447-0

    Article  Google Scholar 

  48. Naghibi SA, Ahmadi K, Daneshi A (2017) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour Manag 31:2761–2775. https://doi.org/10.1007/s11269-017-1660-3

    Article  Google Scholar 

  49. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244. https://doi.org/10.1162/15324430152748236

    Article  MathSciNet  MATH  Google Scholar 

  50. Wang F, Gou B, Qin Y (2013) Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine. Comput Geotech 54:125–132. https://doi.org/10.1016/j.compgeo.2013.07.004

    Article  Google Scholar 

  51. Liu P, Zhu X, Hu X et al (2019) Local tangent space alignment and relevance vector machine as nonlinear methods for estimating sensory quality of tea using NIR spectroscopy. Vib Spectrosc 103:102923. https://doi.org/10.1016/j.vibspec.2019.05.005

    Article  Google Scholar 

  52. Xia C, Huang M, Qian X et al (2019) Novel intelligent approach for peak shear strength assessment of rock joints on the basis of the relevance vector machine. Math Probl Eng. https://doi.org/10.1155/2019/3182736

    Article  Google Scholar 

  53. Wang P (2017) Application of Ground Settlement Prediction Based on EMD-RVM. In: Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017). Atlantis Press, Paris, France, pp 193–198. https://doi.org/10.2991/icmeit-17.2017.35

  54. An JY, You ZH, Zhou Y, Wang DF (2019) Sequence-based prediction of protein-protein interactions using gray wolf optimizer–based relevance vector machine. Evol Bioinform. https://doi.org/10.1177/1176934319844522

    Article  Google Scholar 

  55. Ocak I, Seker SE (2013) Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environ Earth Sci 70:1263–1276. https://doi.org/10.1007/s12665-012-2214-x

    Article  Google Scholar 

  56. Shreyas SK, Dey A (2019) Application of soft computing techniques in tunnelling and underground excavations: state of the art and future prospects. Innov Infrastruct Solut. https://doi.org/10.1007/s41062-019-0234-z

    Article  Google Scholar 

  57. Zhang P, Wu HN, Chen RP, Chan THT (2020) Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: a comparative study. Tunn Undergr Space Technol 99:103383. https://doi.org/10.1016/j.tust.2020.103383

    Article  Google Scholar 

  58. Nguyen H, Choi Y, Bui X-N, Nguyen-Thoi T (2019) Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors 20:132. https://doi.org/10.3390/s20010132

    Article  Google Scholar 

  59. Zhao D, Liu H, Zheng Y et al (2019) Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis. J Biomed Inform 92:103124. https://doi.org/10.1016/j.jbi.2019.103124

    Article  Google Scholar 

  60. Liu X, Yang C (2013) A kernel spectral angle mapper algorithm for remote sensing image classification. In: Proc 2013 6th int congr image signal process CISP 2013 2:814–818. https://doi.org/10.1109/CISP.2013.6745277

  61. Herawan T, Ghazali R, Deris MM (2014) Recent advances on soft computing and data mining: proceedings of the first international conference on soft computing and data mining (SCDM-2014) Universiti Tun Hussein Onn Malaysia, Johor, Malaysia June, 16th-18th, 2014. Adv Intell Syst Comput 287:273–281. https://doi.org/10.1007/978-3-319-07692-8

    Article  MATH  Google Scholar 

  62. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, 27 November-01 December 1995, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  63. Zeng N, Zhang H, Liu W et al (2017) A switching delayed PSO optimized extreme learning machine for short-term load forecasting. Neurocomputing 240:175–182. https://doi.org/10.1016/j.neucom.2017.01.090

    Article  Google Scholar 

  64. Kamboj VK (2016) A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput Appl 27:1643–1655. https://doi.org/10.1007/s00521-015-1962-4

    Article  Google Scholar 

  65. Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Jahed Armaghani D (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33:23–31. https://doi.org/10.1007/s00366-016-0453-2

    Article  Google Scholar 

  66. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, CEC 1999, pp 1945–1950

  67. Mohamad ET, Jahed Armaghani D, Momeni E, Abad SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74:745–757. https://doi.org/10.1007/s10064-014-0638-0

    Article  Google Scholar 

  68. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  69. Yu Z, Shi X, Zhou J et al (2020) Prediction of blast-induced rock movement during bench blasting: use of gray wolf optimizer and support vector regression. Nat Resour Res 29:843–865. https://doi.org/10.1007/s11053-019-09593-3

    Article  Google Scholar 

  70. Bian XQ, Zhang L, Du ZM et al (2018) Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine. J Mol Liq 261:431–438. https://doi.org/10.1016/j.molliq.2018.04.070

    Article  Google Scholar 

  71. Photophotp. https://www.photophoto.cn. Accessed 26 Mar 2019

  72. Singh N, Singh SB (2017) Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance. J Appl Math. https://doi.org/10.1155/2017/2030489

    Article  MathSciNet  MATH  Google Scholar 

  73. Pourtaghi A, Lotfollahi-Yaghin MA (2012) Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunn Undergr Space Technol 28:257–271. https://doi.org/10.1016/j.tust.2011.11.008

    Article  Google Scholar 

  74. Moghaddasi MR, Noorian-Bidgoli M (2018) ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling. Tunn Undergr Space Technol 79:197–209. https://doi.org/10.1016/j.tust.2018.04.016

    Article  Google Scholar 

  75. Zhou J, Sh X, Du K et al (2017) Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel. Int J Geomech 17:1–12. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000817

    Article  Google Scholar 

  76. Verma AK, Singh TN, Chauhan NK, Sarkar K (2016) A hybrid FEM–ANN approach for slope instability prediction. J Inst Eng Ser A 97:171–180. https://doi.org/10.1007/s40030-016-0168-9

    Article  Google Scholar 

  77. Sevgen K, Nefeslioglu G (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression. ANN Random For Sensors 19:3940. https://doi.org/10.3390/s19183940

    Article  Google Scholar 

  78. Zhou J, Aghili N, Ghaleini EN et al (2019) A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Eng Comput. https://doi.org/10.1007/s00366-019-00726-z

    Article  Google Scholar 

  79. Zhou J, Koopialipoor M, Murlidhar BR et al (2019) Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance. Nat Resour Res. https://doi.org/10.1007/s11053-019-09519-z

    Article  Google Scholar 

  80. Majdi A, Rezaei M (2013) Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Comput Appl 23:381–389. https://doi.org/10.1007/s00521-012-0925-2

    Article  Google Scholar 

  81. Fang Q, Yazdani Bejarbaneh B, Vatandoust M et al (2019) Strength evaluation of granite block samples with different predictive models. Eng Comput. https://doi.org/10.1007/s00366-019-00872-4

    Article  Google Scholar 

  82. Armaghani DJ, Tonnizam Mohamad E, Momeni E et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:1–16. https://doi.org/10.1007/s12517-015-2057-3

    Article  Google Scholar 

  83. Yu Z, Shi X, Zhou J et al (2019) Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models. Eng Comput. https://doi.org/10.1007/s00366-019-00868-0

    Article  Google Scholar 

  84. Li C, Zhou J, Armaghani DJ, Li X (2020) Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques. Underground Space. https://doi.org/10.1016/j.undsp.2020.05.005

    Article  Google Scholar 

  85. Duvall WI, Petkof B (1959) Spherical propagation of explosion-generated strain pulses in rock. US Department of the Interior, Bureau of Mines

  86. Langefors U, Kihlström B (1963) The modern technique of rock blasting. Wiley, Hoboken

    Google Scholar 

  87. Davies B, Farmer IW, Attewell PB (1964) Ground vibration from shallow sub-surface blasts. Engineer 217:553–559

    Google Scholar 

  88. Indian Standard (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull I:IS–6922:6

  89. Pal Roy P (1993) Putting ground vibration predictions into practice. Colliery Guard 241:63–67. https://doi.org/10.1016/0148-9062(93)92499-g

    Article  Google Scholar 

  90. Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses. In Rock Mechanics in Engineering Practice (KG Stagg & OC Zienkievicz, Eds.). London:wiley: 203–207.

  91. Gupta RN, Roy PP, Singh B (1988) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference on safety in mines, pp 1015–1021

  92. Qi C, Fourie A, Ma G, Tang X (2018) A hybrid method for improved stability prediction in construction projects: a case study of stope hangingwall stability. Appl Soft Comput J 71:649–658. https://doi.org/10.1016/j.asoc.2018.07.035

    Article  Google Scholar 

  93. Moayedi H, Osouli A, Nguyen H, Rashid ASA (2019) A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Eng Comput. https://doi.org/10.1007/s00366-019-00828-8

    Article  Google Scholar 

  94. Qi C, Fourie A, Chen Q, Zhang Q (2018) A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill. J Clean Prod 183:566–578. https://doi.org/10.1016/j.jclepro.2018.02.154

    Article  Google Scholar 

  95. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc 14th Int. Joint Conf. Artif Intell, 14:1137–1145

  96. 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. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553

    Article  Google Scholar 

  97. Lin Y, Zhou K, Li J (2018) Prediction of slope stability using four supervised learning methods. IEEE Access 6:31169–31179. https://doi.org/10.1109/ACCESS.2018.2843787

    Article  Google Scholar 

  98. Li E, Zhou J, Shi X et al (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

    Article  Google Scholar 

  99. Koopialipoor M, Tootoonchi H, Jahed Armaghani D et al (2019) Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Environ 78:6347–6360. https://doi.org/10.1007/s10064-019-01538-7

    Article  Google Scholar 

  100. Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng 11:779–789. https://doi.org/10.1016/j.jrmge.2019.01.002

    Article  Google Scholar 

  101. Jahed Armaghani D, Hasanipanah M, Mahdiyar A et al (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29:619–629. https://doi.org/10.1007/s00521-016-2598-8

    Article  Google Scholar 

  102. Armaghani DJ, Hasanipanah M, Amnieh HB, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29:457–465. https://doi.org/10.1007/s00521-016-2577-0

    Article  Google Scholar 

  103. Ly H-B, G.Asteris P, Pham BT (2020) Accuracy assessment of extreme learning machine in predicting soil compression coefficient. Vietnam J Earth Sci 42. https://doi.org/10.15625/0866-7187/42/3/14999

  104. Armaghani DJ, Asteris PG, Fatemi SA et al (2020) On the use of neuro-swarm system to forecast the pile settlement. Appl Sci. https://doi.org/10.3390/app10061904

    Article  Google Scholar 

  105. 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. Underground Space. https://doi.org/10.1016/j.undsp.2020.05.008

    Article  Google Scholar 

  106. Gao J, Amar MN, Motahari MR et al (2020) Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms. Engineering with Computers. https://doi.org/10.1007/s00366-020-01059-y

    Article  Google Scholar 

  107. 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. https://doi.org/10.1016/j.enggeo.2007.10.009

    Article  Google Scholar 

  108. Jianguo W (2011) Numerical simulation study on the stability of Buzhao Dam West under sudden blasting. Kunming University of Science and Technology

  109. Jianguo W, Yonghui H, Jianming Z (2016) BP neural network prediction for blasting vibration in open- pit coal mine. J Henan Polytech Univ Nat Sci 35:322–328. https://doi.org/10.16186/j.cnki.1673-9787.2016.03.006

    Article  Google Scholar 

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Acknowledgements

The National Natural Science Foundation Project of China (Xiuzhi Shi, 51874350). The National Natural Science Foundation Project of China (Jian Zhou, 41807259). The National Key R&D Program of China (Xiuzhi Shi, 2017YFC0602902). The Fundamental Research Funds for the Central Universities of Central South University (Zhi Yu, 2018zzts217). The Innovation-Driven Project of Central South University (Jian Zhou, 2020CX040).

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, Hunan, China

    Zhi Yu, Xiuzhi Shi, Jian Zhou, Yonggang Gou, Xiaofeng Huo & Junhui Zhang

  2. Institute of Geology and Mineral Engineering, Xinjiang University, Urumqi, 830046, Xinjiang, China

    Junhui Zhang

  3. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

    Danial Jahed Armaghani

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  1. Zhi Yu
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  2. Xiuzhi Shi
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Correspondence to Xiuzhi Shi or Danial Jahed Armaghani.

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Yu, Z., Shi, X., Zhou, J. et al. A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration. Engineering with Computers 38, 1905–1920 (2022). https://doi.org/10.1007/s00366-020-01136-2

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