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Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique

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Abstract

This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R 2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R 2 = 0.908, RMSE = 17.638 and VAF = 89.917, while the corresponding values for R 2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively.

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Authors and Affiliations

  1. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Roohollah Shirani Faradonbeh

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

    Danial Jahed Armaghani

  3. Department of Mining, Tarbiat Modares University, Tehran, 14115-143, Iran

    Masoud Monjezi

Authors
  1. Roohollah Shirani Faradonbeh
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  2. Danial Jahed Armaghani
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  3. Masoud Monjezi
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Correspondence to Masoud Monjezi.

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Faradonbeh, R.S., Jahed Armaghani, D. & Monjezi, M. Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75, 993–1006 (2016). https://doi.org/10.1007/s10064-016-0872-8

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  • DOI: https://doi.org/10.1007/s10064-016-0872-8

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