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Stability prediction of gate roadways in longwall mining using artificial neural networks

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Abstract

Roadways stability in longwall coal mining is critical to mine productivity and safety of the personnel. In this regard, a typical challenge in longwall mining is to predict roadways stability equipped with a reliable support system in order to ensure their serviceability during mining life. Artificial neural networks (ANNs) were employed to predict the stability conditions of longwall roadways based on roof displacements. In this respect, datasets of the roof displacements monitored in different sections of a 1.2-km-long roadway in Tabas coal mine, Iran, were set up to develop an ANN model. On the other hand, geomechanical parameters obtained through site investigations and laboratory tests were introduced to the ANN model as independent variables. In order to predict the roadway stability, these data were introduced to a multilayer perceptron (MLP) network to estimate the unknown nonlinear relationship between the rock parameters and roof displacements in the gate roadways. A four-layer feed-forward backpropagation neural network with topology 9-7-6-1 was found to be optimum. As a result, the MLP proposed model predicted values close enough to the measured ones with an acceptable range of correlation. A high conformity (R 2 = 0.911) was observed between predicted and measured roof displacement values. Concluding remark is the proposed model appears to be a suitable tool for prediction of gate roadways stability in longwall mining.

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Acknowledgments

The authors are greatly thankful to Tabas Parvadeh Coal Company for cooperation in site investigations. Also, engineer R. Koriti Sani is appreciated for providing facilities and access to the data.

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Authors and Affiliations

  1. Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

    Satar Mahdevari, Kourosh Shahriar & Mostafa Sharifzadeh

  2. Western Australian School of Mine, Curtin University, Kalgoorlie, WA, Australia

    Mostafa Sharifzadeh

  3. School of Engineering, University of British Columbia, Kelowna, BC, Canada

    Dwayne D. Tannant

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  1. Satar Mahdevari
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  2. Kourosh Shahriar
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Correspondence to Satar Mahdevari or Kourosh Shahriar.

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Mahdevari, S., Shahriar, K., Sharifzadeh, M. et al. Stability prediction of gate roadways in longwall mining using artificial neural networks. Neural Comput & Applic 28, 3537–3555 (2017). https://doi.org/10.1007/s00521-016-2263-2

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