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Rezaei, Mohammad, Hossaini, Mohammad Farouq, Majdi, Abbas, Najmoddini, Iraj. (1396). Determination of the height of destressed zone above the mined panel: An ANN model. , 51(1), 1-7. doi: 10.22059/ijmge.2017.62147
Mohammad Rezaei; Mohammad Farouq Hossaini; Abbas Majdi; Iraj Najmoddini. "Determination of the height of destressed zone above the mined panel: An ANN model". , 51, 1, 1396, 1-7. doi: 10.22059/ijmge.2017.62147
Rezaei, Mohammad, Hossaini, Mohammad Farouq, Majdi, Abbas, Najmoddini, Iraj. (1396). 'Determination of the height of destressed zone above the mined panel: An ANN model', , 51(1), pp. 1-7. doi: 10.22059/ijmge.2017.62147
Rezaei, Mohammad, Hossaini, Mohammad Farouq, Majdi, Abbas, Najmoddini, Iraj. Determination of the height of destressed zone above the mined panel: An ANN model. , 1396; 51(1): 1-7. doi: 10.22059/ijmge.2017.62147

Determination of the height of destressed zone above the mined panel: An ANN model

International Journal of Mining and Geo-Engineering
مقاله 1، دوره 51، شماره 1، شهریور 2017، صفحه 1-7    اصل مقاله (601.03 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/ijmge.2017.62147
نویسندگان
Mohammad Rezaei* 1؛ Mohammad Farouq Hossaini2؛ Abbas Majdi2؛ Iraj Najmoddini1
1Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
چکیده
The paper describes an artificial neural network (ANN) model to predict the height of destressed zone (HDZ) which is taken as equivalent to the combined height of caved and fractured zones above the mined panel in longwall mining. For this, the suitable datasets have been collected from the literatures and prepared for modeling. The data were used to construct a multilayer perceptron (MLP) network to approximate the unknown nonlinear relationship between the input parameters and HDZ. The MLP proposed model predicted values in enough agreements with the measured ones in a satisfactory correlation, in which, a high conformity (R2=0.989) was observed. To approve the capability of proposed ANN model, the obtained results are compared to the results of the conventional regression analysis (CRA) method. The calculated performance evaluation indices show the higher level of accuracy of the proposed ANN model compared to CRA. For further evaluation, the ANN model results are compared with the results of available models and in-situ measurements reported in literatures. Comparative results present a logical agreement between ANN model and available methods. Obtained results remark that the proposed ANN model is a suitable tool in HDZ estimation. At the end of modeling, the parametric study shows that the most effective parameter is unit weight whereas elastic modulus is the least effective parameter on the HDZ in this study.
کلیدواژه‌ها
Height of destressed zone؛ Artificial Neural Network؛ Conventional regression analysis؛ Parametric Study
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