Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm

  • Danial Jahed Armaghani
  • Mahdi Hasanipanah
  • Hassan Bakhshandeh Amnieh
  • Dieu Tien BuiEmail author
  • Peyman Mehrabi
  • Majid Khorami
Original Article


This paper is aimed to develop and verify a novel hybrid intelligent model, which is indeed a new version of GMDH algorithm and named generalized structure of GMDH (GS-GMDH) to solve engineering problems. The proposed GS-GMDH model was validated its capability of predicting blast-induced ground vibration, a very important safety issues in the mining industry. For this regard, a data set with a totally of 96 samples was gathered from a blasting site in Shur River Dam region, Iran. Among them, 67 and 29 samples were used for constructing and testing the model, respectively. To check the accuracy and robustness of the proposed algorithm, the values of the performance evaluation measures, i.e., R2, variance accounted for (VAF), mean absolute error, root mean square error, scatter index, and Nash–Sutcliffe model efficiency (EN–S), were used. The results showed the efficiency of the GS-GMDH algorithm in the prediction of the blast-induced ground vibration. It was also confirmed that the proposed algorithm can be applied effectively to solving/predicting the engineering problems and it has also the potential to be generalized to other fields.


GMDH GS-GMDH Blasting Ground vibration 



  1. 1.
    Elevli B, Arpaz E (2010) Evaluation of parameters affected on the blast induced ground vibration (BIGV) by using relation diagram method (RDM). Acta Montan Slovaca 15(4):261–268Google Scholar
  2. 2.
    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):1043–1050Google Scholar
  3. 3.
    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):709–717Google Scholar
  4. 4.
    Faradonbeh RS, Hasanipanah M, Amnieh HB, Armaghani DJ, Monjezi M (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 190(6):351Google Scholar
  5. 5.
    Hasanipanah M, Armaghani DJ, Amnieh HB, Koopialipoor M, Arab H (2018) A risk-based technique to analyze flyrock results through rock engineering system. Geotech Geol Eng 36:2247–2260Google Scholar
  6. 6.
    Roy PP (1993) Putting ground vibration predictors into practice. J Colliery Guard 241:63–67Google Scholar
  7. 7.
    Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors. Indian J Eng Mater Sci 11:178–184Google Scholar
  8. 8.
    Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghani D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297Google Scholar
  9. 9.
    Hasanipanah M, Shirani Faradonbeh R, Bakhshandeh Amnieh H, Jahed Armaghani D, Monjezi M (2017) Forecasting blast induced ground vibration developing a CART model. Eng Comput 33(2):307–316Google Scholar
  10. 10.
    Hasanipanah M et al (2016) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol. Google Scholar
  11. 11.
    Singh TN (2004) Artificial neural network approach for prediction and control of ground vibrations in mines. Min Technol 113(4):251–256Google Scholar
  12. 12.
    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):951–959Google Scholar
  13. 13.
    Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali Aaq Qaleh A (2017) Prediction of blast produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179. Google Scholar
  14. 14.
    Duvall WI, Petkof BB (1959) Spherical propagation of explosion generated strain pulses in rock. US Bur Mines, RI, p 5483Google Scholar
  15. 15.
    Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New YorkGoogle Scholar
  16. 16.
    Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Engineering 217:553–559Google Scholar
  17. 17.
    Gupta RN, Pal Roy P, Singh B (1987) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference on safety in mines research institute, Beijing, China, pp 1015–1021Google Scholar
  18. 18.
    Mohammadhassani M, Nezamabadi-pour H, Shariati M, Suhatril M (2013) Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams. Struct Eng Mech 46(6):853–868Google Scholar
  19. 19.
    Toghroli A, Mohammadhassani M, Shariati M, Suhatril M, Ibrahim Z, Ramli Sulong NH (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct J 17(5):623–639Google Scholar
  20. 20.
    Safa M et al (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Composite Struct 21(3):679–688Google Scholar
  21. 21.
    Mansouri I, Shariati M, Safa M, Ibrahim Z, Tahir MM, Petković D (2017) Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J Intell Manuf. Google Scholar
  22. 22.
    Toghroli A (2015) Applications of the ANFIS and LR models in the prediction of shear connection in composite beams. Universiti Malaya, Kuala LumpurGoogle Scholar
  23. 23.
    Chahnasir ES et al (2018) Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors. Smart Struct Syst 22(4):413–424Google Scholar
  24. 24.
    Sedghi Y et al (2018) Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct Syst 22(3):335–340MathSciNetGoogle Scholar
  25. 25.
    Sari PA et al (2018) An intelligent based-model role to simulate the factor of safe slope by support vector regression. Eng Comput. Google Scholar
  26. 26.
    Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27:193–200Google Scholar
  27. 27.
    Mohamadnejad M, Gholami R, Ataei M (2012) Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunn Undergr Space Technol 28:238–244Google Scholar
  28. 28.
    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):755–770Google Scholar
  29. 29.
    Radojica L et al (2014) Prediction of blast-induced ground motion in a copper mine. Int J Rock Mech Min Sci 69:19–25Google Scholar
  30. 30.
    Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ 74:873–886Google Scholar
  31. 31.
    Hajihassani M, Jahed Armaghani D, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74:2799–2817Google Scholar
  32. 32.
    Jahed Armaghani D, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860Google Scholar
  33. 33.
    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):1637–1643Google Scholar
  34. 34.
    Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024Google Scholar
  35. 35.
    Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34(2):339–345Google Scholar
  36. 36.
    Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024Google Scholar
  37. 37.
    Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  38. 38.
    Ebtehaj I, Bonakdari H, Gharabaghi B (2018) Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement 116:473–482Google Scholar
  39. 39.
    Ebtehaj I, Bonakdari H, Sharifi A (2014) Design criteria for sediment transport in sewers based on self-cleansing concept. J Zhejiang Univ Sci A 15(11):914–924Google Scholar
  40. 40.
    Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Mohamad ET (2016) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 1:2–3. Google Scholar
  41. 41.
    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):349–359Google Scholar
  42. 42.
    Mahdiyar A et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput 33(4):807–817Google Scholar
  43. 43.
    Hasanipanah M et al (2017) Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environ Earth Sci 76(1):27Google Scholar
  44. 44.
    Mansouri I et al (2018) Strength prediction of rotary brace damper using MLR and MARS. Struct Eng Mech 60(3):471–488Google Scholar
  45. 45.
    Shariat M, Shariati M, Madadi A, Wakil K (2018) Computational Lagrangian multiplier method by using for optimization and sensitivity analysis of rectangular reinforced con crete beams. Steel Compos Struct 29(2):243–256Google Scholar
  46. 46.
    Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast induced airblast using a modified conjugate FR method. Measurement 131:35–41Google Scholar
  47. 47.
    Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Danial Jahed Armaghani
    • 1
  • Mahdi Hasanipanah
    • 2
  • Hassan Bakhshandeh Amnieh
    • 3
  • Dieu Tien Bui
    • 4
    • 5
    Email author
  • Peyman Mehrabi
    • 6
  • Majid Khorami
    • 7
  1. 1.Institute of Research and DevelopmentDuy Tan UniversityDa Nang 550000Vietnam
  2. 2.Department of Mining EngineeringUniversity of KashanKashanIran
  3. 3.School of Mining, College of EngineeringUniversity of TehranTehran 11155-4563Iran
  4. 4.Geographic Information Science Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityVietnam
  6. 6.Department of Civil EngineeringK.N Toosi University of TechnologyTehranIran
  7. 7.Universidad UTEFacultad de Arquitectura y UrbanismoQuitoEcuador

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