Stability prediction of gate roadways in longwall mining using artificial neural networks
© 2016 The Natural Computing Applications Forum 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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Springer
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/44479 |
| _version_ | 1848757013053440000 |
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| author | Mahdevari, S. Shahriar, K. Sharifzadeh, Mostafa Tannant, D. |
| author_facet | Mahdevari, S. Shahriar, K. Sharifzadeh, Mostafa Tannant, D. |
| author_sort | Mahdevari, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2016 The Natural Computing Applications Forum 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 (R2 = 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. |
| first_indexed | 2025-11-14T09:21:20Z |
| format | Journal Article |
| id | curtin-20.500.11937-44479 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:21:20Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-444792017-09-13T14:13:05Z Stability prediction of gate roadways in longwall mining using artificial neural networks Mahdevari, S. Shahriar, K. Sharifzadeh, Mostafa Tannant, D. © 2016 The Natural Computing Applications Forum 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 (R2 = 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. 2016 Journal Article http://hdl.handle.net/20.500.11937/44479 10.1007/s00521-016-2263-2 Springer restricted |
| spellingShingle | Mahdevari, S. Shahriar, K. Sharifzadeh, Mostafa Tannant, D. Stability prediction of gate roadways in longwall mining using artificial neural networks |
| title | Stability prediction of gate roadways in longwall mining using artificial neural networks |
| title_full | Stability prediction of gate roadways in longwall mining using artificial neural networks |
| title_fullStr | Stability prediction of gate roadways in longwall mining using artificial neural networks |
| title_full_unstemmed | Stability prediction of gate roadways in longwall mining using artificial neural networks |
| title_short | Stability prediction of gate roadways in longwall mining using artificial neural networks |
| title_sort | stability prediction of gate roadways in longwall mining using artificial neural networks |
| url | http://hdl.handle.net/20.500.11937/44479 |