Engineering with Computers

, Volume 35, Issue 1, pp 315–322 | Cite as

Development of a novel soft-computing framework for the simulation aims: a case study

  • Wei GaoEmail author
  • Masoud Karbasi
  • Ali Mahmodi DerakhshEmail author
  • Ahmad Jalili
Original Article


The simulation of blast-induced air-overpressure (AOp) has been a major area of interest in the recent years, and many models have been employed in this field. The scope of this paper is to propose a novel soft-computing framework for predicting the AOp through the implementation of hybrid evolutionary model based on artificial neural network (ANN) with teaching–learning-based optimization (TLBO). The parameters considered during the formulation of the prediction model were maximum charge per delay, rock mass rating, and distance from the blasting face as the inputs and AOp as the output. Totally, 85 blasting events in Shur river dam region have been monitored and the mentioned parameters have been measured. Then, the performances and prediction efficiency of the models have been compared on the basis of performance indices, namely the R square (R2), root-mean-square error (RMSE). The obtained results show that the ANN–TLBO with R2 of 0.932 and RMSE of 2.56 yields the better performance for the prediction of AOp as compared to ANN. As a conclusion, it can be found that the proposed ANN–TLBO model has an excellent potential for the prediction aims.


Blasting Air-overpressure Hybrid model ANN–TLBO 



The authors really appreciate Dr. Mahdi Hasanipanah who allowed us to access and use his data.


  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.
    Rezaei M, Monjezi M, Varjani AY (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf sci 49(2):298–305CrossRefGoogle Scholar
  3. 3.
    Verma AK, Singh TN (2013) Comparative study of cognitive systems for ground vibration measurements. Neural Comput Appl 22:341–350CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Jahed Armaghani D, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arabian J Geosci. Google Scholar
  6. 6.
    Hasanipanah M, Bakhshandeh Amnieh H, Arab H, Zamzam MS (2016) Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl. Google Scholar
  7. 7.
    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. Google Scholar
  8. 8.
    Roy PP (2005) Rock blasting effects and operations. A.A Balkema, IndiaGoogle Scholar
  9. 9.
    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. Google Scholar
  10. 10.
    Siskind DE, Stachura VJ, Stagg MS, Koop JW (1980). In: Siskind DE (ed) Structure response and damage produced by airblast from surface mining. United States Bureau of Mines, Washington, D.C.Google Scholar
  11. 11.
    Segarra P, Domingo JF, López LM, Sanchidrián JA, Ortega MF (2010) Prediction of near field overpressure from quarry blasting. Appl Acoust 71:1169–1176CrossRefGoogle Scholar
  12. 12.
    Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arabian J Geosci 4:427–433CrossRefGoogle Scholar
  13. 13.
    Kuzu C, Fisne A, Ercelebi SG (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70:404–411CrossRefGoogle Scholar
  14. 14.
    Armaghani DJ, Hasanipanah M, Tonnizam Mohamad E (2016) A combination of the ICA-ANN model to predict airoverpressure resulting from blasting. Eng Comput 32(1):155–171CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Taheri K, Hasanipanah M, Bagheri Golzar S, Abd Majid MZ (2016) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput. Google Scholar
  17. 17.
    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
  18. 18.
    Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Tonnizam Mohamad E (2016) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl. Google Scholar
  19. 19.
    Sharma LK, Vishal V, Singh TN (2017) Predicting CO2 permeability of bituminous coal using statistical and adaptive neurofuzzy analysis. J Nat Gas Sci Eng. Google Scholar
  20. 20.
    Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507CrossRefGoogle Scholar
  21. 21.
    Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169CrossRefGoogle Scholar
  22. 22.
    Sirdesai NN, Singh A, Sharma LK, Singh R, Singh TN (2017) Development of novel methods to predict the strength properties of thermally treated sandstone using statistical and soft-computing approach. Neural Comput Appl. Google Scholar
  23. 23.
    Ahmad M, Ansari MK, Sharma LK, Singh R, TN Singh (2017) Correlation between strength and durability indices of rocks-soft computing approach. Proc Eng 191:458–466CrossRefGoogle Scholar
  24. 24.
    Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119CrossRefGoogle Scholar
  25. 25.
    Gao W, Farahani MR, Aslam A, Hosamani S (2017) Distance learning techniques for ontology similarity measuring and ontology mapping. Clust Comput J Netw Softw Tools Appl 20(2):959–968Google Scholar
  26. 26.
    Gao W, Zhu LL, Guo Y, Wang KY (2017) Ontology learning algorithm for similarity measuring and ontology mapping using linear programming. J Intell Fuzzy Syst 33(5):3153–3163CrossRefGoogle Scholar
  27. 27.
    Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2017) Developing of GPR model for forecasting the rock fragmentation in surface mines. Eng Comput. Google Scholar
  28. 28.
    Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Control Worldw 36:7–16CrossRefGoogle Scholar
  29. 29.
    Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851CrossRefGoogle Scholar
  30. 30.
    Hajihassani M, Jahed Armaghani D, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67CrossRefGoogle Scholar
  31. 31.
    Çelik Ö, Teke A, Yıldırım HB (2016) The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. J Clean Prod 116(Supplement C): 1–12. CrossRefGoogle Scholar
  32. 32.
    Kaur T, Kumar S, Segal R (2016) Application of artificial neural network for short term wind speed forecasting. In: Paper presented at the 2016 Biennial international conference on power and energy systems: towards sustainable energy (PESTSE)Google Scholar
  33. 33.
    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. CrossRefGoogle Scholar
  34. 34.
    Togan V (2013) Design of pin jointed structures using teaching-learning based optimization. Struct Eng Mech 47(2):209–225CrossRefGoogle Scholar
  35. 35.
    Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15. MathSciNetCrossRefGoogle Scholar
  36. 36.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  37. 37.
    Kankal M, Uzlu E (2017) Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Comput Appl 28(1):737–747. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and TechnologyYunnan Normal UniversityKunmingChina
  2. 2.Water Engineering Department, Faculty of AgricultureUniversity of ZanjanZanjanIran
  3. 3.Young Researchers and Elite Club, West Tehran BranchIslamic Azad UniversityTehranIran
  4. 4.Department of Computer Engineering and ITShiraz University of TechnologyShirazIran

Personalised recommendations