Neural Computing and Applications

, Volume 29, Issue 9, pp 457–465 | Cite as

Feasibility of ICA in approximating ground vibration resulting from mine blasting

  • Danial Jahed Armaghani
  • Mahdi Hasanipanah
  • Hassan Bakhshandeh Amnieh
  • Edy Tonnizam Mohamad
Original Article


Precise prediction of blast-induced ground vibration is an essential task to reduce the environmental effects in the surface mines, civil and tunneling works. This research investigates the potential of imperialist competitive algorithm (ICA) in approximating ground vibration as a result of blasting at three quarry sites, namely Ulu Tiram, Pengerang and Masai in Malaysia. In ICA modeling, two forms of equations, namely power and quadratic, were developed. For comparison aims, several empirical models were also used. In order to develop the ICA and empirical models, maximum charge weight used per delay (W) and the distance between blasting sites and monitoring stations (D) were utilized as the independent variables, while, peak particle velocity (PPV), as a blast-induced ground vibration descriptor, was utilized as the dependent variable. Totally, 73 blasting events were monitored, and the values of W, D and PPV were carefully measured. Two statistical functions, i.e., root mean square error and coefficient of multiple determination (R 2) were used to compare the performance capability of those prediction models. Simulation results show that the proposed ICA quadratic form can get more accurate predicting results than the ICA power form and empirical models.


Blasting Peak particle velocity Imperialist competitive algorithm Empirical models 


Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Singh TN, Verma AK (2010) Sensitivity of total charge and maximum charge per delay on ground vibration. Geomat Nat Hazards Risk 1(3):259–272CrossRefGoogle Scholar
  2. 2.
    Khandelwal M, Singh TN (2013) Application of an expert system to predict maximum explosive charge used per delay in surface mining. Rock Mech Rock Eng 46(6):1551–1558CrossRefGoogle Scholar
  3. 3.
    Trivedi R, Singh TN, Raina AK (2014) Prediction of blast induced flyrock in Indian limestone mines using neural networks. J Rock Mech Geotech Eng 6:447–454CrossRefGoogle Scholar
  4. 4.
    Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2015) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput. doi: 10.1007/s00366-015-0425-y Google Scholar
  5. 5.
    Trivedi R, Singh TN, Gupta N (2015) Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech Geol Eng 33:875–891CrossRefGoogle Scholar
  6. 6.
    Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Jahed Armaghani D (2016) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput. doi: 10.1007/s00366-016-0453-2 Google Scholar
  7. 7.
    Jahed Armaghani D, Hasanipanah M, Tonnizam Mohamad E (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171CrossRefGoogle Scholar
  8. 8.
    Tonnizam Mohamad E, Jahed Armaghani D, Hasanipanah M, Murlidhar BR, Alel MNA (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75(2):1–15CrossRefGoogle Scholar
  9. 9.
    Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H, Zaimi Abd Majid M, Mahmood MDT (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Eng Comput. doi: 10.1007/s00521-016-2434-1 Google Scholar
  10. 10.
    Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali Aaq Qaleh A (2016) Prediction of blast-produced ground vibration using particle swarm optimization. Eng Comput. doi: 10.1007/s00366-016-0462-1 Google Scholar
  11. 11.
    Fouladgar N, Hasanipanah M, Bakhshandeh Amnieh H (2016) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput. doi: 10.1007/s00366-016-0463-0 Google Scholar
  12. 12.
    Singh TN (2004) Artificial neural network approach for prediction and control of ground vibrations in mines. Min Technol 113(4):251–256CrossRefGoogle Scholar
  13. 13.
    Singh TN, Singh V (2005) An intelligent approach to predict and control ground vibration in mines. Geotech Geol Eng 23(3):249–262CrossRefGoogle Scholar
  14. 14.
    Iphar M, Yavuz M, Ak H (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56(1):97–107CrossRefGoogle Scholar
  15. 15.
    Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50CrossRefGoogle Scholar
  16. 16.
    Fisne A, Kuzu C, Hudaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174(1–4):461–470CrossRefGoogle Scholar
  17. 17.
    Alvarez-Vigil AE, Gonzalez-Nicieza C, Gayarre Lopez F, Alvarez- Fernandez MI (2012) Predicting blasting propagation velocity and vibration frequency using artificial neural network. Int J Rock Mech Min Sci 55:108–116Google Scholar
  18. 18.
    Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-EGohar iron ore mine, Iran. J Rock Mech Geotech Eng 6:67–76CrossRefGoogle Scholar
  19. 19.
    Vasovic D, Kostic S, Ravilic M, Trajkovic S (2014) Environmental impact of blasting at Drenovac limestone quarry (Serbia). Environ Earth Sci 72:3915–3928CrossRefGoogle Scholar
  20. 20.
    Dindarloo SR (2015) Prediction of blast-induced ground vibrations via genetic programming. Int J Min Sci Technol 25(6):1011–1015CrossRefGoogle Scholar
  21. 21.
    Khandelwal M, Singh TN (2006) Prediction of blast induced vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289:711–725CrossRefGoogle Scholar
  22. 22.
    Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125CrossRefGoogle Scholar
  23. 23.
    Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222CrossRefGoogle Scholar
  24. 24.
    Bakhshandeh Amnieh H, Mozdianfard MR, Siamaki A (2010) Predicting of blasting vibrations in Sarcheshmeh copper mine by neural network. Saf Sci 48(3):319–325CrossRefGoogle Scholar
  25. 25.
    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(11):1233–1236CrossRefGoogle Scholar
  26. 26.
    Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blasting-induced ground vibration. Eng Comput 27(2):117–125CrossRefGoogle Scholar
  27. 27.
    Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48(5):845–851CrossRefGoogle Scholar
  28. 28.
    Ghasemi E, Ataei M, Hashemolhosseini H (2012) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Contr 19(5):755–770CrossRefGoogle Scholar
  29. 29.
    Verma AK, Singh TN (2013) Comparative study of cognitive systems for ground vibration measurements. Neural Comput Appl 22:341–1643CrossRefGoogle Scholar
  30. 30.
    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 22:1637–1643CrossRefGoogle Scholar
  31. 31.
    Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput. doi: 10.1007/s00366-016-0442-5 Google Scholar
  32. 32.
    Duvall WI, Petkof BB (1959) Spherical propagation of explosion generated strain pulses in rock. US Bur Mines, RI 5483 Google Scholar
  33. 33.
    Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New YorkGoogle Scholar
  34. 34.
    Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Engineering 217:553–559Google Scholar
  35. 35.
    Nicholls HR, Johnson CF, Duvall WI (1971) Blasting vibrations and their effects on structures. US Bur Mines, RI 656 Google Scholar
  36. 36.
    Indian Standard Institute (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bull No 6922Google Scholar
  37. 37.
    German Institute of Standards (1986) Vibration of building effects on structures. DIN 4150, vol 3, pp 1–5Google Scholar
  38. 38.
    Siskind DE (2000) Vibrations from blasting. ISEE Publication, ClevelandGoogle Scholar
  39. 39.
    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
  40. 40.
    Pal Roy P (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Tech 12(2):157–165CrossRefGoogle Scholar
  41. 41.
    Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors. Indian J Eng Mater Sci 11(3):178–184Google Scholar
  42. 42.
    Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghanid D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297CrossRefGoogle Scholar
  43. 43.
    Singh TN, Kanchan R, Verma AK (2004) Prediction of blast induced ground vibration and frequency using an artificial intelligent technique. Noise Vib Worldw 35(11):7–15CrossRefGoogle Scholar
  44. 44.
    Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233CrossRefGoogle Scholar
  45. 45.
    Singh R, Vishal V, Singh TN (2012) Soft computing method for assessment of compressional wave velocity. Sci Iran 19(4):1018–1024CrossRefGoogle Scholar
  46. 46.
    Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506CrossRefGoogle Scholar
  47. 47.
    Verma AK, Singh TN (2013) A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 22(7):1685–1693CrossRefGoogle Scholar
  48. 48.
    Verma AK, Singh TN, Maheshwar Sachin (2014) Comparative study of intelligent prediction models for pressure wave velocity. J Geosci Geomat 2(3):130–138Google Scholar
  49. 49.
    Kainthola A, Singh PK, Verma D, Singh R, Sarkar K, Singh TN (2015) Prediction of strength parameters of himalayan rocks: a statistical and ANFIS approach. Geotech Geol Eng 33(5):1255–1278CrossRefGoogle Scholar
  50. 50.
    Verma AK, Sirvaiya A (2016) Intelligent prediction of Langmuir isotherms of Gondwana coals in India. J Pet Explor Prod Technol 6(1):135–143CrossRefGoogle Scholar
  51. 51.
    Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27:193–200CrossRefGoogle Scholar
  52. 52.
    Jahed Armaghani D, Momeni E, Khalil Abad SVAN, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860CrossRefGoogle Scholar
  53. 53.
    Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, pp 4661–4667Google Scholar
  54. 54.
    Atashpaz-Gargari E, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm, a novel approach for PID controller design in MIMO distillation column process. Int J Intell Comput Cybern 1:337–355MathSciNetCrossRefzbMATHGoogle Scholar
  55. 55.
    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 74:873–886CrossRefGoogle Scholar
  56. 56.
    Demuth H, Beale M, Hagan M (2009) MATLAB Version; Neural Network Toolbox for Use with Matlab. The MathworksGoogle Scholar
  57. 57.
    Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13(2):1085–1098CrossRefGoogle Scholar
  58. 58.
    Ebrahimi E, Mollazade K, Babaei S (2014) Toward an automatic wheat purity measuring device: a machine vision-based neural networks-assisted imperialist competitive algorithm approach. Measurement 55:196–205CrossRefGoogle Scholar
  59. 59.
    Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J, Article ID 643715Google Scholar
  60. 60.
    Jahed Armaghani D, Mohd Amin MF, Yagiz S, Shirani Faradonbeh R, Abdullah RA (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186Google Scholar
  61. 61.
    Jahed Armaghani D, Mohamad ET, Momeni E, Monjezi M, Narayanasamy MS (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci. doi: 10.1007/s12517-015-2057-3 Google Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Danial Jahed Armaghani
    • 1
  • Mahdi Hasanipanah
    • 2
  • Hassan Bakhshandeh Amnieh
    • 3
  • Edy Tonnizam Mohamad
    • 1
  1. 1.Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  2. 2.Department of Mining EngineeringUniversity of KashanKashanIran
  3. 3.School of Mining, College of EngineeringUniversity of TehranTehranIran

Personalised recommendations