Neural Computing and Applications

, Volume 29, Issue 9, pp 619–629 | Cite as

Airblast prediction through a hybrid genetic algorithm-ANN model

  • Danial Jahed Armaghani
  • Mahdi Hasanipanah
  • Amir Mahdiyar
  • Muhd Zaimi Abd Majid
  • Hassan Bakhshandeh Amnieh
  • Mahmood M. D. Tahir
Original Article

Abstract

Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp. For this purpose, a database was compiled from 97 blasting events in a granite quarry in Penang, Malaysia. The values of maximum charge per delay and the distance from the blast-face were set as model inputs to predict AOp. To verify the quality and reliability of the ANN and GA-ANN models, several statistical functions, i.e., root means square error (RMSE), coefficient of determination (R 2) and variance account for (VAF) were calculated. Based on the obtained results, the GA-ANN model is found to be better than ANN model in estimating AOp induced by blasting. Considering only testing datasets, values of 0.965, 0.857, 0.77 and 0.82 for R 2, 96.380, 84.257, 70.07 and 78.06 for VAF, and 0.049, 0.117, 8.62 and 6.54 for RMSE were obtained for GA-ANN, ANN, USBM and MLR models, respectively, which prove superiority of the GA-ANN in AOp prediction. It can be concluded that GA-ANN model can perform better compared to other implemented models in predicting AOp.

Keywords

Blast-induced air overpressure ANN GA GA-ANN 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Danial Jahed Armaghani
    • 1
  • Mahdi Hasanipanah
    • 2
  • Amir Mahdiyar
    • 3
  • Muhd Zaimi Abd Majid
    • 4
  • Hassan Bakhshandeh Amnieh
    • 5
  • Mahmood M. D. Tahir
    • 4
  1. 1.Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  2. 2.Department of Mining Engineering, Faculty of EngineeringUniversity of KashanKashanIran
  3. 3.Department of Structure and Material, Faculty of Civil EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  4. 4.UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  5. 5.School of Mining, College of EngineeringUniversity of TehranTehranIran

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