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Airblast prediction through a hybrid genetic algorithm-ANN model

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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.

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Authors and Affiliations

  1. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Danial Jahed Armaghani

  2. Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

    Mahdi Hasanipanah

  3. Department of Structure and Material, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Amir Mahdiyar

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

    Muhd Zaimi Abd Majid & Mahmood M. D. Tahir

  5. School of Mining, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran

    Hassan Bakhshandeh Amnieh

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  1. Danial Jahed Armaghani
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Correspondence to Danial Jahed Armaghani.

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Jahed Armaghani, D., Hasanipanah, M., Mahdiyar, A. et al. Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput & Applic 29, 619–629 (2018). https://doi.org/10.1007/s00521-016-2598-8

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  • DOI: https://doi.org/10.1007/s00521-016-2598-8

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