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A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

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Abstract

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA–SVR, PSO–SVR, GA–SVR, FA–ANN, PSO–ANN, and GA–ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA–SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA–SVR with the coefficient of determination (R2) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO–SVR with R2 = 0.977 and RMSE = 0.725, the FA–SVR with R2 = 0.964 and RMSE = 0.923, the GA–SVR with R2 = 0.957 and RMSE = 1.016, the GA–ANN with R2 = 0.936 and RMSE = 1.252, the FA–ANN with R2 = 0.925 and RMSE = 1.368, and the PSO–ANN with R2 = 0.924 and RMSE = 1.366. Consequently, the MFA–SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

BP:

Backpropagation

BGAMs:

Boosted generalised additive models

R 2 :

Coefficient of determination

C1:

Cognitive acceleration

CS:

Cuckoo search

Di :

Distance between blasting point and measurement point

XGBoost:

Extreme gradient boosting

FA:

Firefly algorithm

FM:

Fuzzy model

GPR:

Gaussian process regression

GA:

Genetic algorithm

ICA:

Imperialist competitive algorithm

HKM:

K-means clustering algorithm

MCPD:

Maximum charge used per delay

MAE:

Mean absolute error

MFA:

Modified firefly algorithm

MLP:

Multilayer perceptron

PSO:

Particle swarm optimization

PPV:

Peak particle velocity

Vp :

p wave velocity

RBF:

Radial basis function

RF:

Random forest

RMSE:

Root mean square error

C2:

Social acceleration

SVM:

Support vector machine

SVR:

Support vector regression

SVs:

Support vectors

UCS:

Unconfined compressive strength

WI:

Willmott’s index of agreement

References

  1. Hagan TN (1973) Rock breakage by explosives. In: Proceedings of national symposium on rock fragmentation, Adelaide, Australia, 26–28 February 1973, pp 1–17

  2. Hasanipanah M, Armaghani DJ, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808

    Google Scholar 

  3. Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455

    Google Scholar 

  4. Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO-SVR model. Eng Comput 33(1):23–31

    Google Scholar 

  5. Hasanipanah M, Shahnazar A, Arab H, Golzar SB, Amiri M (2017) Developing a new hybrid-AI model to predict blast induced backbreak. Eng Comput 33(3):349–359

    Google Scholar 

  6. Hasanipanah M, Faradonbeh RS, Armaghani DJ, Amnieh HB, Khandelwal M (2017) Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environ Earth Sci 76(1):27

    Google Scholar 

  7. Nguyen H, Bui XN, Tran QH, Mai NL (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. https://doi.org/10.1016/j.asoc.2019.01.042

    Article  Google Scholar 

  8. Ozdemir B, Kumral M (2019) A system-wide approach to minimize the operational cost of bench production in open-cast mining operations. Int J Coal Sci Technol 6(1):84–94

    Google Scholar 

  9. Wiss JF, Linehan PW (1978) Control of vibration and air noise from surface coal mines III. USBM Report 103(3)–79:623

  10. Görgülü K, Arpaz E, Demirci A, Koçaslan A, Dilmaç MK, Yüksek AG (2013) Investigation of blast-induced ground vibrations in the Tülü boron open pit mine. Bull Eng Geol Environ 72(3–4):555–564

    Google Scholar 

  11. Pal RP (2005) Rock blasting. IBH, New Delhi

    Google Scholar 

  12. 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

    Google Scholar 

  13. Taheri K, Hasanipanah M, Bagheri Golzar S, Majid MZA (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700

    Google Scholar 

  14. 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(4):951–959

    Google Scholar 

  15. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Aaq Qaleh AZ (2017) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179

    Google Scholar 

  16. 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

    Google Scholar 

  17. 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

    Google Scholar 

  18. Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. Report of Investigation. US Bureau of Mines, Pittsburgh, pp 5483–5521

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

    Google Scholar 

  20. Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses, rock mechanics in engineering practices. Wiley, London

    Google Scholar 

  21. ISI (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull 6922

  22. Langefors U, Kihlström B (1978) The modern technique of rock blasting, 3rd edn. Wiley, Stockholm

    Google Scholar 

  23. Ghosh A, Daemen JK (1983) A simple new blast vibration predictor. In: Proceedings of the 24th US symposium on rock mechanics, College Station, TX, USA, pp 151–61

  24. Gupta RN, Roy PP, Bagachi A, Singh B (1987) Dynamic effects in various rock mass and their predictions. J Mines Met Fuels 35(11):455–462

    Google Scholar 

  25. Gupta RN, Roy PP, Singh B (1988) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of 22nd international conference of safety in mines, Beijing, China, 2–6 Nov 1987, pp 1015–1021

  26. Roy PP (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12:157–165

    Google Scholar 

  27. Arpaz E, Uysal Ö, Tola Y, Görgülü K, Cavus M (2012) Comparison of blast-induced ground vibration predictors in Seyitomer coal mine. In: 12th Rock mechanics symposium, Beijing, China, 18–21 October 2011, pp 1161–1163

  28. ISRM (1992) Suggested method for blast vibration monitoring. Int J Rock Mech Min Sci Geol Abstr 29:143–156

    Google Scholar 

  29. Arpaz E (2000) Monitoring and evaluation of blast induced vibrations in some open-pit mines in Turkey. Dissertation, Cumhuriyet University, Sivas (in Turkish)

  30. Blair DP, Spathis AT (1982) Attenuation of explosion-generated pulse in rock masses. J Geophys Res 87(5):3885–3892

    Google Scholar 

  31. Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. A.A Balkema, Rotterdam, p 390

    Google Scholar 

  32. Aldaș GGU (2002) Effect of some rock mass properties on blasting induced ground vibration wave characteristics at Orhaneli surface coal mine. Dissertation, Middle East Technical University, Ankara, Turkey

  33. Nguyen H, Bui XN, Tran QH, 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

    Google Scholar 

  34. Fang Q, Nguyen H, Bui XN, Nguyen-Thoi T (2019) Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Nat Resour Res. https://doi.org/10.1007/s11053-019-09577-3

    Article  Google Scholar 

  35. 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

    Google Scholar 

  36. Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409–424

    Google Scholar 

  37. Lu X, Hasanipanah M, Brindhadevi K, Amnieh HB, Khalafi S (2019) ORELM: A novel machine learning approach for prediction of flyrock in mine blasting. Nat Resour Res. https://doi.org/10.1007/s11053-019-09532-2

    Article  Google Scholar 

  38. Jahed Armaghani D, Hasanipanah M, Amnieh HB, Mohamad ET (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29(9):457–465

    Google Scholar 

  39. Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blastinduced airblast using a modified conjugate FR method. Measurement 131:35–41

    Google Scholar 

  40. 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(4):709–717

    Google Scholar 

  41. Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715

    Google Scholar 

  42. Faradonbeh RS, Hasanipanah M, Amnieh HB et al (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 190:351

    Google Scholar 

  43. Gao W, Alqahtani AS, Mubarakali A, Mavaluru D, Khalafi S (2019) Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Eng Comput 35(131):1–8

    Google Scholar 

  44. 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

    Google Scholar 

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

    Google Scholar 

  46. Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659

    Google Scholar 

  47. Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Google Scholar 

  48. Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33(3):04019024

    Google Scholar 

  49. Ghasemi E, Ataei M, Hashemolhosseini H (2013) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19(5):755–770

    Google Scholar 

  50. Radojica L, Kostić S, Pantović R, Vasović N (2014) Prediction of blast-produced ground motion in a copper mine. Int J Rock Mech Min Sci 69:19–25

    Google Scholar 

  51. 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(3):873–886

    Google Scholar 

  52. Jahed Armaghani D, 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

    Google Scholar 

  53. Jahed Armaghani D, Raja SNSB, Faizi K, Rashid ASA (2015) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl. https://doi.org/10.1007/s00521-015-2072-z

    Article  Google Scholar 

  54. Shahnazar A, Rad HN, Hasanipanah M, Tahir MM, Armaghani DJ, Ghoroqi M (2017) A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci 76(15):527

    Google Scholar 

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

    Article  Google Scholar 

  56. Arthur CK, Temeng VA, Ziggah YY (2019) Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Eng Comput. https://doi.org/10.1007/s00366-018-0686-3

    Article  Google Scholar 

  57. 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 

  58. Zhang X, Nguyen H, Bui XN, Tran QH, Nguyen DA, Tien Bui D, Moayedi H (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 

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

    Google Scholar 

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

    Google Scholar 

  61. Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226

    Google Scholar 

  62. Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading

    Google Scholar 

  63. Vapnik NV (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  64. Yan K, Shi C (2010) Prediction of elastic modulus of normal and high strength concrete by support vector machine. Constr Build Mater 24(8):1479–1485

    Google Scholar 

  65. Safarzadegan Gilan S, Bahrami Jovein H, Ramezanianpour AA (2012) Hybrid support vector regression—particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin. Constr Build Mater 34:321–329

    Google Scholar 

  66. 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

    Google Scholar 

  67. Chen Y, Tan H (2017) Short-term prediction of electric demand in building sector via hybrid support vector regression. Appl Energy 204:1363–1374

    Google Scholar 

  68. Gunn S (1998) Support vector machines for classification and regression. ISIS Technical Report

  69. Vapnik VN, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advance in neural information processing system, vol 9. MIT Press, Cambridge, pp 281–287

    Google Scholar 

  70. Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2012) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci. https://doi.org/10.1007/s12517-012-0770-8

    Article  Google Scholar 

  71. Tawadrous AS, Katsabanis PD (2007) Prediction of surface crown pillar stability using artificial neural networks. Int J Numer Anal Met 31(7):917–931

    MATH  Google Scholar 

  72. Rezaei M, Monjezi M, Moghaddam SG, Farzaneh F (2012) Burden prediction in blasting operation using rock geomechanical properties. Arab J Geosci 5:1031–1037

    Google Scholar 

  73. Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi

    MATH  Google Scholar 

  74. Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using artificial neural networks technique. J Sci Ind Res India 63:32–38

    Google Scholar 

  75. Ch S, Sohani SK, Kumar D et al (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288. https://doi.org/10.1016/j.neucom.2013.09.030

    Article  Google Scholar 

  76. Majumder A, Das A, Kr Das P (2016) A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2471-9

    Article  Google Scholar 

  77. Li J et al (2019) Hybrid soft computing approach for determining water quality indicator: euphrates River. Neural Comput Appl 31:827–837

    Google Scholar 

  78. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications SAGA 2009, Lecture Notes in Computer Science (vol. 5792, pp 169–178)

  79. Mohammadi S, Mozafari B, Solimani S, Niknam T (2013) An adaptive modified firefly optimisation algorithm based on hong’s point estimate method to optimal operation management in a microgrid with consideration of uncertainties. Energy 51:339–348

    Google Scholar 

  80. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948

  81. Ren F, Wu X, Zhang K, Niu R (2015) Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China. Environ Earth Sci 73:4791–4804

    Google Scholar 

  82. Yang H, Hasanipanah M, Tahir MM, Bui DT (2019) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat Resour Res. https://doi.org/10.1007/s11053-019-09515-3

    Article  Google Scholar 

  83. 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(1):363–376

    Google Scholar 

  84. Abdi MJ, Salimi H (2010) Farsi handwriting recognition with mixture of RBF experts based on particle swarm optimization. Int J Inf Sci Comput Math 2:129–136

    Google Scholar 

  85. Wei J, Jian-qi Z, Xiang Z (2011) Face recognition method based on support vector machine and particle swarm optimization. Expert Syst Appl 38:4390–4393

    Google Scholar 

  86. Abdi MJ, Giveki D (2013) Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules. Eng Appl Artif Intell 26:603–608

    Google Scholar 

  87. Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  88. Chipperfield A, Fleming P, Pohlheim H et al (2006) Genetic algorithm toolbox for use with MATLAB user’s guide, version 1.2. University of Sheffield

  89. Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Res PL-ASCE 120:423–443

    Google Scholar 

  90. 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 

  91. 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

    Article  Google Scholar 

  92. Behzadafshar K, Esfandi Sarafraz M, Hasanipanah M, Mojtahedi SFS, Tahir MM (2019) Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results. Bull Eng Geol Environ 78(3):1527–1536

    Google Scholar 

  93. Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47:246–253

    Google Scholar 

  94. Rashidian V, Hassanlourad M (2013) Predicting the shear behavior of cemented and uncemented carbonate sands using a genetic algorithm-based artificial neural network. Geotech Geol Eng 2:1–18

    Google Scholar 

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

    Google Scholar 

  96. Lazzús JA, Salfate I, Montecinos S (2014) hybrid neural network {particle swarm algorithm to describe chaotic time series. Neural Netw World 6(14):601–617

    Google Scholar 

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

    Google Scholar 

  98. Samadianfard S, Ghorbani MA, Mohammadi B (2018) Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Inf Process Agric 5:465–476

    Google Scholar 

  99. Shirani Faradonbeh R, Jahed Armaghani D, Bakhshandeh Amnieh H, Tonnizam Mohamad E (2016) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2537-8

    Article  Google Scholar 

  100. Shang Y, Nguyen H, Bui XN, Tran QH, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09503-7

    Article  Google Scholar 

  101. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Hoboken

    Google Scholar 

  102. Baykasoğlu A, Ozsoydan FB (2014) An improved firefly algorithm for solving dynamic multidimensional knapsack problems. Expert Syst Appl 41(8):3712–3725

    Google Scholar 

  103. Yang H, Rad HN, Hasanipanah M, Amnieh HB, Nekouie A (2019) Prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms. Nat Resour Res. https://doi.org/10.1007/s11053-019-09597-z

    Article  Google Scholar 

  104. Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146–1153

    Google Scholar 

  105. Asteris PG, Roussis PC, Douvika MG (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17(6):1344

    Google Scholar 

  106. Asteris PG, Armaghani DJ, Hatzigeorgiou Karayannis CG, Pilakoutas K (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr 24(5):469–488

    Google Scholar 

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

    Google Scholar 

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Authors and Affiliations

  1. Chongqing Jianzhu College, Chongqing, 400072, China

    Wusi Chen

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

    Mahdi Hasanipanah

  3. College of Computer Science, Tabari University of Babol, Babol, Iran

    Hima Nikafshan Rad

  4. Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Danial Jahed Armaghani

  5. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    M. M. Tahir

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  1. Wusi Chen
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Chen, W., Hasanipanah, M., Nikafshan Rad, H. et al. A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Engineering with Computers 37, 1455–1471 (2021). https://doi.org/10.1007/s00366-019-00895-x

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