Skip to main content
SpringerLink
Log in

A novel algorithm of Nested-ELM for predicting blasting vibration

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The prediction model of blasting vibration has always been a hot and difficult topic because of the very complex nonlinear relationship between the blasting vibration and its influencing factors. A novel algorithm of Nested-ELM for predicting blasting vibration was proposed in this paper. Nested-ELM algorithm can quickly select the optimal input weights and biases of hidden nodes by setting MSE as the fitness function and combining with RWS method. And the algorithm can also quickly determine the optimal number of hidden nodes by setting its initial value according to the empirical formulas and selecting MAPE as the diffusion search index. The feasibility and superiority of Nested-ELM algorithm for predicting blasting vibration were proved by the application of Nested-ELM model on four different types of blasting vibration samples. This paper can provide a novel improved ELM algorithm for predicting blasting vibration with good performance in operation efficiency, prediction accuracy, generalization and sample-number independence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Hakan AK, Konuk Adnan (2008) The effect of discontinuity frequency on ground vibrations produced from bench blasting: a case study. Soil Dyn Earthq Eng 28:686–694. https://doi.org/10.1016/j.soildyn.2007.11.006

    Article  Google Scholar 

  2. Khandelwal M, Monjezi M (2013) Prediction of flyrock in open pit blasting operation using machine learning method. Int J Min Sci Technol 23:313–316. https://doi.org/10.1016/j.ijmst.2013.05.005

    Article  Google Scholar 

  3. Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani SA (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. https://doi.org/10.1007/s12517-013-1174-0

    Article  Google Scholar 

  4. Hasanipanah M, Monjezi M, Shahnazar A, Armaghani DJ, Farazmand A (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 

  5. Hasanipanah M, Bakhshandeh Amnieh H, Khamesi H, Jahed Armaghani D, Bagheri Golzar S, Shahnazar A (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol 15:551–560. https://doi.org/10.1007/s13762-017-1395-y

    Article  Google Scholar 

  6. Cardu M, Coragliotto D, Oreste P (2019) Analysis of predictor equations for determining the blast-induced vibration in rock blasting. Int J Min Sci Technol 29:905–915. https://doi.org/10.1016/j.ijmst.2019.02.009

    Article  Google Scholar 

  7. Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32:441–455. https://doi.org/10.1007/s00366-015-0425-y

    Article  Google Scholar 

  8. Bui X, Nguyen H, Le H, Bui H, Do N (2020) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res 29:571–591. https://doi.org/10.1007/s11053-019-09461-0

    Article  Google Scholar 

  9. Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34:241–251. https://doi.org/10.1007/s00366-017-0535-9

    Article  Google Scholar 

  10. Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34:709–717. https://doi.org/10.1007/s00366-017-0568-0

    Article  Google Scholar 

  11. Hasanipanah M, Bakhshandeh Amnieh H (2020) A fuzzy rule-based approach to address uncertainty in risk assessment and prediction of blast-induced flyrock in a quarry. Nat Resour Res 29:669–689. https://doi.org/10.1007/s11053-020-09616-4

    Article  Google Scholar 

  12. Bian JQ, Bi JY, Wang HD (2019) Numerical simulation study on unsteady distribution law of dust in blasting roadway of inclined ditch mine. China Min Mag 28:269–274. https://doi.org/10.12075/j.issn.1004-4051.2019.S1.037

    Article  Google Scholar 

  13. Zou CF, Deng YF (2019) Study on dust production law and dust reduction technology in blasting stope. Min Res Dev 39:34–37. https://doi.org/10.13827/j.cnki.kyyk.2019.08.007

    Article  Google Scholar 

  14. Ghasemi Ebrahim (2017) Particle swarm optimization approach for forecasting backbreak induced by bench blasting. Neural Comput Appl 28:1855–1862. https://doi.org/10.1007/s00521-016-2182-2

    Article  Google Scholar 

  15. Hasanipanah M, Bakhshandeh Amnieh H (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 

  16. Khandelwal M, Saadat M (2015) A dimensional analysis approach to study blast-induced ground vibration. Rock Mech Rock Eng 48:727–735. https://doi.org/10.1007/s00603-014-0604-y

    Article  Google Scholar 

  17. Zhao HB, Long Y, Li XH, Lu L (2016) Experimental and numerical investigation of the effect of blast-induced vibration from adjacent tunnel on existing tunnel. KSCE J Civ Eng 20:431–439. https://doi.org/10.1007/s12205-015-0130-9

    Article  Google Scholar 

  18. Xu C, Deng CF (2016) Investigating spectral behavior of tunnel blast-induced vibration using wavelet analysis: a case study of a dam in China. J Civ Struct Health Monit 6:637–647. https://doi.org/10.1007/s13349-016-0183-6

    Article  Google Scholar 

  19. Yang JH, Lu WB, Li P, Yan P (2018) Evaluation of rock vibration generated in blasting excavation of deep-buried tunnels. KSCE J Civ Eng 22:2593–2608. https://doi.org/10.1007/s12205-017-0240-7

    Article  Google Scholar 

  20. Zhou SR, Zhou JM, Wang HH, Ding XF, Ye JB, Zhao MS (2018) Analysis of blasting parameters optimization and blasting vibration effects of subway excavation. Blasting 35:85–89. https://doi.org/10.3963/j.issn.1001-487X.2018.02.015

    Article  Google Scholar 

  21. Ma Q, Zhou FX, Zhang WY, Li YX (2019) An analytical study on blast-induced ground vibration with gravitational effect. Soil Mech Found Eng 56:287–293. https://doi.org/10.1007/s11204-019-09604-8

    Article  Google Scholar 

  22. Xu SD, Li YH, Liu JP, Zhang FP (2019) Optimization of blasting parameters for an underground mine through prediction of blasting vibration. J Vib Control 25:1585–1595. https://doi.org/10.1177/1077546319829938

    Article  MathSciNet  Google Scholar 

  23. Tian E, Zhang J, Soltani Tehrani M, Surendar A, Ibatova AZ (2019) Development of GA-based models for simulating the ground vibration in mine blasting. Eng Comput 35:849–855. https://doi.org/10.1007/s00366-018-0635-1

    Article  Google Scholar 

  24. Khandelwal M, Armaghani DJ, Faradonbeh RS, Yellishetty M, Majid MZA, Monjezi M (2016) 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 

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

    Article  Google Scholar 

  26. Fouladgar N, Hasanipanah M, Bakhshandeh Amnieh H (2017) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput 33:181–189. https://doi.org/10.1007/s00366-016-0463-0

    Article  Google Scholar 

  27. Behzadafshar K, Mohebbi F, Soltani Tehrani M, Hasanipanah M, Tabrizi O (2018) Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm. Eng Comput 35:1774–1787. https://doi.org/10.1108/EC-08-2017-0290

    Article  Google Scholar 

  28. Nguyen H, Bui X, Bui H, Cuong DT (2019) Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys 67:477–490. https://doi.org/10.1007/s11600-019-00268-4

    Article  Google Scholar 

  29. Nguyen H, Bui X, Tran Q, Mai N (2019) A new soft computing model for estimating and controlling blast produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:1–20. https://doi.org/10.1016/j.asoc.2019.01.042

    Article  Google Scholar 

  30. Monjezi M, Baghestani M, Faradonbeh RS, Saghand MP, Armaghani DJ (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Eng Comput 32:717–728. https://doi.org/10.1007/s00366-016-0448-z

    Article  Google Scholar 

  31. Wu X, Zhang YP, Guo QF (2017) Amplification and attenuation effect of blasting vibration on step topography. Explos Shock Waves 37:1017–1022. https://doi.org/10.11883/1001-1455(2017)06-1017-06

    Article  Google Scholar 

  32. Dauji Saha (2018) New approach for identification of suitable vibration attenuation relationship for underground blasts. Eng J 22:147–159. https://doi.org/10.4186/ej.2018.22.4.147

    Article  Google Scholar 

  33. Liu GP (2019) Prediction of blasting vibration velocity for large-section tunnel blasting. Blasting 36:129–136. https://doi.org/10.3963/j/issn.1001-487X.2019.03.020

    Article  Google Scholar 

  34. Saheli R, Saha D (2019) Ground vibration attenuation relationship for underground blast: a case study. J Inst Eng (India) Ser A 100:763–775. https://doi.org/10.1007/s40030-019-00382-y

    Article  Google Scholar 

  35. Aawal H, Mishra AK (2019) Modified scaled distance regression analysis approach for prediction of blast-induced ground vibration in multi-hole blasting. J Rock Mech Geotech Eng 11:202–207. https://doi.org/10.1016/j.jrmge.2018.07.004

    Article  Google Scholar 

  36. Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artifcial neural networks. Tunn Undergr Space Technol 26:46–50. https://doi.org/10.1016/j.tust.2010.05.002

    Article  Google Scholar 

  37. 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 22:1637–1643. https://doi.org/10.1007/s00521-012-0856-y

    Article  Google Scholar 

  38. Nguyen H, Bui X, Tran Q, Moayedi H (2019) Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam. Environ Earth Sci 78:479. https://doi.org/10.1007/s12665-019-8491-x

    Article  Google Scholar 

  39. Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 22:1685–1693. https://doi.org/10.1007/s00521-012-0817-5

    Article  Google Scholar 

  40. Dindarloo Saeid R (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25:1011–1015. https://doi.org/10.1016/j.ijmst.2015.09.020

    Article  Google Scholar 

  41. Bhagwat VP, Dey K (2016) Comparison of some blast vibration predictors for blasting in underground drifts and some observations. J Inst Eng (India) Ser D 97:33–38. https://doi.org/10.1007/s40033-015-0086-4

    Article  Google Scholar 

  42. Himanshu VK, Roy MP, Mishra AK, Paswan RK, Panda D, Singh PK (2018) Multivariate statistical analysis approach for prediction of blast-induced ground vibration. Arab J Geosci 11:460. https://doi.org/10.1007/s12517-018-3796-8

    Article  Google Scholar 

  43. Matidza MI, Zhang J, Huang G, Mwanggi AD (2020) Assessment of blast-induced ground vibration at Jinduicheng Molybdenum open pit mine. Nat Resour Res 29:831–841. https://doi.org/10.1007/s11053-020-09623-5

    Article  Google Scholar 

  44. Hajihassani M, Jahed Armaghani D, Marto A, Mohamad ET (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886. https://doi.org/10.1007/s10064-014-0657-x

    Article  Google Scholar 

  45. Khandelwal M, Armaghani DJ, Faradonbeh RS, Yellishetty M, Majid MZA, Monjezi M (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 

  46. Samareh H, Khoshrou SH, Shahriar K, Saberi MM (2015) Seismic data classification using cluster analysis for predicting ground vibration caused by blast. Arab J Geosci 8:9679–9692. https://doi.org/10.1007/s12517-015-1923-3

    Article  Google Scholar 

  47. Faradonbeh RS, Armaghani DJ, Abd Majid MZ, Tahir MD, Ramesh Murlidhar B, Monjezi M, Wong HM (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int J Environ Sci Technol 13:1453–1464. https://doi.org/10.1007/s13762-016-0979-2

    Article  Google Scholar 

  48. Hasanipanah M, Faradonbeh RS, Bakhshandeh Amnieh H, Armaghani DJ, Monjezi M (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33:307–316. https://doi.org/10.1007/s00366-016-0475-9

    Article  Google Scholar 

  49. Hasanipanah M, Golzar SB, Larki IA, Maryaki MY, Ghahremanians T (2017) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput 33:951–959. https://doi.org/10.1007/s00366-017-0508-z

    Article  Google Scholar 

  50. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Qaleh AZA (2017) Prediction of blast-produced ground vibration using particle swarm optimization. Eng Comput 33:173–179. https://doi.org/10.1007/s00366-016-0462-1

    Article  Google Scholar 

  51. Eskandar H, Heydari E, Hasanipanah M, Jalil Masir M, Mahmodi Derakhsh A (2018) Feasibility of particle swarm optimization and multiple regression for the prediction of an environmental issue of mine blasting. Eng Comput 35:363–376. https://doi.org/10.1108/EC-01-2017-004

    Article  Google Scholar 

  52. Arthur CK, Temeng VA, Ziggah YY (2020) Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Eng Comput 36:29–42. https://doi.org/10.1007/s00366-018-0686-3

    Article  Google Scholar 

  53. Zhang YN, Yang YW, Li W (2010) Neural network with direct weight determination method. Sun Yat-sen University Press, Guangzhou

    Google Scholar 

  54. Zhang ZX (2011) Neural networks control and Matlab simulation. Harbin Institute of Technology Press, Harbin

    Google Scholar 

  55. Monjezi M, Ahmadi M, Sheikhan M, Bahrami A, Salimi AR (2010) Predicting blast-induced ground vibration using various types of neural networks. Soil Dyn Earthq Eng 30:1233–1236. https://doi.org/10.1016/j.soildyn.2010.05.005

    Article  Google Scholar 

  56. Zhao HB, Long Y, Liu HQ, Li XH, Zhong MS (2014) Application of neural networks to prediction of peak amplitude of blasting vibration velocity of urban tunnel. Eng Blasting 20:23–27. https://doi.org/10.3969/j.issn.1006-7051.2014.05.005

    Article  Google Scholar 

  57. Ram Chandar K, Sastry VR, Hegde C (2017) A critical comparison of regression models and artificial neural networks to predict ground vibrations. Geotech Geol Eng 35:573–583. https://doi.org/10.1007/s10706-016-0126-3

    Article  Google Scholar 

  58. Shi JJ, Li QY, Zhang Q, Wei X, Wang H (2017) Forecast system for blasting vibration velocity peak based on Matlab and BP neural network. Explos Shock Waves 37:1087–1092. https://doi.org/10.11883/1001-1455(2017)06-1087-06

    Article  Google Scholar 

  59. Taheri K, Hasanipanah M, Bagheri Golzar S, Abd Majid MZ (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33:689–700. https://doi.org/10.1007/s00366-016-0497-3

    Article  Google Scholar 

  60. Ragam P, Nimaje DS (2018) Evaluation and prediction of blast-induced peak particle velocity using artificial neural network: a case study. Noise Vib Worldw 49:111–119. https://doi.org/10.1177/0957456518763161

    Article  Google Scholar 

  61. Nguyen H, Bui X, Tran Q, Le T, Do N, Hoa LTT (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 

  62. Ganguly S (2019) Development of a blast induced vibration prediction model using an artificial neural network. J S Afr Inst Min Metall 119:187–200. https://doi.org/10.17159/2411-9717/2019/v119n2a11

    Article  Google Scholar 

  63. Ma HY, Zhang YP, Liu HY (2019) Prediction of blasting vibration velocity in underground stope based on BP neural network. Explos Mater 48:55–59. https://doi.org/10.3969/j.issn.1001-8352.2019.06.011

    Article  Google Scholar 

  64. Nguyen H, Drebenstedt C, Bui X, Bui DT (2020) 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 29:691–709. https://doi.org/10.1007/s11053-019-09470-z

    Article  Google Scholar 

  65. Li YM (2019) Research and application of improved algorithm of classifier based on BP neural network. Dissertation, China University of Geosciences

  66. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 international joint conference on neural networks (IJCNN2004), Budapest, July 25-29, pp 985–990. https://doi.org/10.1109/IJCNN.2004.1380068

  67. Huang GB, Ding XJ, Zhou HM (2010) Optimization method based extreme learning machine for classification. Neuro Comput 74:155–163. https://doi.org/10.1016/j.neucom.2010.02.019

    Article  Google Scholar 

  68. Xie ZG, Xu K, Liu LG, Xiong YS (2014) 3D shape segmentation and labeling via extreme learning machine. Comput Graph Forum 33:85–95. https://doi.org/10.1111/cgf.12434

    Article  Google Scholar 

  69. Cao JW, Zhang K, Luo MX, Yin C, Lai XP (2016) Extreme learning machine and adaptive sparse representation for image classification. Neural Netw 81:91–102. https://doi.org/10.1016/j.neunet.2016.06.001

    Article  Google Scholar 

  70. Wang MJ, Chen HL, Li HZ, Cai ZN, Zhao XH, Tong CF, Li J, Xu X (2017) Grey wolf optimization evolving kernel extreme learning machine: application to bankruptcy prediction. Eng Appl Artif Intell 63:54–68. https://doi.org/10.1016/j.engappai.2017.05.003

    Article  Google Scholar 

  71. Lv FC, Liu Y, Qi YX, Yan YH, Zhang JT, Xie Q (2018) Short-term load forecasting based on optimized learning machine using improved genetic algorithm. J North China Electric Power Univ 45:1–7. https://doi.org/10.3969/j.issn.1007-2691.2018.06.01

    Article  Google Scholar 

  72. Xu JQ, Wu WN, Sun W, Wang YN, Liang QK (2019) Optimized extreme learning machines and their application in decoupling of multi-component force information. Chin J Sens Actuators 32:1487–1492. https://doi.org/10.3969/j.issn.1004-1699.2019.10.010

    Article  Google Scholar 

  73. Yin HH, Yang XW, Liu JM, Han X (2019) An extreme learning machine based on ant lion optimization. Comput Appl Softw 36:230–234. https://doi.org/10.3969/j.issn.1000-386x.2019.08.039

    Article  Google Scholar 

  74. Fu B (2015) Passive target localisation algorithm based on optimizing extreme learning machine with PSO. Comput Appl Softw 32:325–328. https://doi.org/10.3969/j.issn.1000-386x.2015.11.076

    Article  Google Scholar 

  75. Pang S, Yang XY, Lin XS (2017) Evolutionary extreme learning machine optimized by quantum-behaved particle swarm optimization. J Syst Simul 29:2447–2458. https://doi.org/10.16182/j.issn1004731x.joss.201710028

    Article  Google Scholar 

  76. Chu ZY, Ma Y, Cui J (2018) Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation of unknown objects. Nonlinear Dyn 91:1321–1335. https://doi.org/10.1007/s11071-017-3947-6

    Article  MATH  Google Scholar 

  77. Li QW, Han F, Ling QH (2018) An improved double hidden-layer variable length incremental extreme learning machine based on particle swarm optimization. In: International conference on intelligent computing (ICIC 2018), Wuhan, August 15-18, pp 34–43. https://doi.org/10.1007/978-3-319-95933-7_5

  78. Zhang TH, Yu J, Ye ZL, Lin Y (2019) Classification model research of mixed kernel extreme learning machine based on particle swarm optimization. J Geomat Sci Technol 36:56–61. https://doi.org/10.3969/j.issn.1673-6338.2019.01.011

    Article  Google Scholar 

  79. Zhao H, Zuo KW, Qin YZ (2016) Improved artificial bee colony optimize ELM classification model. Comput Meas Control 24:251–254. https://doi.org/10.16526/j.cnki.11-4762/tp.2016.10.071

    Article  Google Scholar 

  80. Wang Y, Wang A, Ai Q, Sun HJ (2017) A novel artificial bee colony optimization strategy-based extreme learning machine algorithm. Prog Artif Intell 6:41–52. https://doi.org/10.1007/s13748-016-0102-4

    Article  Google Scholar 

  81. Wang XM, Wan XH, Zhu YY, Jiang ZL, Liu JX (2014) Prediction for building vibration velocity caused by blasting based on PSO-ELM. Sci Technol Rev 32:15–20. https://doi.org/10.3981/j.issn.1000-7857.2014.19.001

    Article  Google Scholar 

  82. Jahed Armaghani D, Kumar D, Samui P, Hasanipanah M, Roy B (2020) A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine. Eng Comput. https://doi.org/10.1007/s00366-020-00997-x

    Article  Google Scholar 

  83. Li XB, Deng GD, Liu ZX (2015) On the predication for the blast vibration velocity based on SA-ELM. J Saf Environ 15:115–119. https://doi.org/10.13637/j.issn.1009-6094.2015.03.025

    Article  Google Scholar 

  84. Wen TX, Chen XY, Shao LB, Dou R, Wei P (2017) Prediction on parameters optimized GA-ELM model for cast blasting in open-pit mine. J China Coal Soc 42:630–638. https://doi.org/10.13225/j.cnki.jccs.2016.0572

    Article  Google Scholar 

  85. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and application. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  86. Huang GB, Wang DH, Lan Y (2011) Extreme learning machine: a survey. Int J Mach Learn Cybern 2:107–122. https://doi.org/10.1007/s13042-011-0019-y

    Article  Google Scholar 

  87. Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, Baltimore

    MATH  Google Scholar 

  88. Serre D (2002) Theory and applications. Springer, New York

    MATH  Google Scholar 

  89. Wei B, Yu F, Xu X, Xie C (2014) Interactive evolutionary algorithm based on improved roulette wheel selection strategy. Comput Digit Eng 42:1763–1767. https://doi.org/10.3969/j.issn.1672-9722.2014.10.004

    Article  Google Scholar 

  90. Xia GM, Zeng JC (2007) A stochastic particle swarm optimization algorithm based on the genetic algorithm of roulette wheel selection. Comput Eng Sci 29:51–54. https://doi.org/10.3969/j.issn.1007-130X.2007.06.016

    Article  Google Scholar 

  91. Ma JY (2018) Research on chaotic firefly algorithm based on roulette wheel selection strategy. Dissertation, Xidian University

  92. Zhu DQ, Shi H (2006) Theory and application of artificial neural network. Science Press, Beijing

    Google Scholar 

  93. Ma CD, Dong LJ, Zhou YN, Xu JC (2014) Nonlinear model for predicting characteristic parameters of blasting vibration and its application. Ming Metall Eng 34:1–4. https://doi.org/10.3969/j.issn.0253-6099.2014.06.001

    Article  Google Scholar 

  94. Li B, Bi TH, Liu XW (2018) Influence factors of blasting vibration of shallow tunnel based on grey relational analysis. Eng Blasting 24:86–90. https://doi.org/10.3969/j.issn.1006-7051.2018.04.017

    Article  Google Scholar 

  95. Guan XM, Nie QK, Li HW, An JY (2019) Research overview on dynamic response and damage of existing structure under tunnel blasting vibration. China Civ Eng J 52:151–158. https://doi.org/10.15951/j.tmgcxb.2019.s1.021

    Article  Google Scholar 

  96. Singh CP, Agrawal H, Mishra AK, Singh PK (2019) Reducing environmental hazards of blasting using electronic detonators in a large opencast coal project-a case study. J Mines Met Fuels 67:345–350

    Google Scholar 

  97. Golafshani EM, Ashour A (2016) A feasibility study of BBP for predicting shear capacity of FRP reinforced concrete beams without stirrups. Adv Eng Softw 97:29–39. https://doi.org/10.1016/j.advengsoft.2016.02.007

    Article  Google Scholar 

  98. Gong SW, Ling TH (2009) Fuzzy neural networks model of peak particle vibration velocity forecast for blasting and its application. Ming Res Dev 29:95–97. https://doi.org/10.13827/j.cnki.kyyk.2009.02.031

    Article  Google Scholar 

  99. Tang H, Shi YQ, Li HB, Li JR, Wang XW, Jiang PC (2007) Prediction of peak velocity of blasting vibration based on neural network. Chin J Rock Mech Eng 26:3533–3539. https://doi.org/10.3321/j.issn:1000-6915.2007.z1.145

    Article  Google Scholar 

  100. Shi XZ (2007) Study of time and frequency analysis of blasting vibration signal and the prediction of blasting vibration characteristic parameters and damage. Dissertation, Central South University

  101. Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H, Majid MZZ, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28:1043–1050. https://doi.org/10.1007/s00521-016-2434-1

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the support by the National Natural Science Foundation of China (Grant Nos. 51874123 and 51504082) and Open Project Foundation of Fujian Research Center for Tunneling and Urban Underground Space Engineering.

Author information

Authors and Affiliations

  1. School of Civil Engineering, Henan Polytechnic University, Jiaozuo, 454000, China

    Haixia Wei, Jinfeng Chen, Jie Zhu, Xiaolin Yang & Huaibao Chu

Authors
  1. Haixia Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinfeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huaibao Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Zhu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, H., Chen, J., Zhu, J. et al. A novel algorithm of Nested-ELM for predicting blasting vibration. Engineering with Computers 38, 1241–1256 (2022). https://doi.org/10.1007/s00366-020-01082-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01082-z

Keywords

Search

Navigation