Prediction of ground subsidence due to underground mining through time using multilayer feed-forward artificial neural networks and back-propagation algorithm – case study at Mong Duong underground coal mine (Vietnam)


https://doi.org/10.17073/2500-0632-2021-4-241-251

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Abstract

The paper is devoted to studying the possibility of using artificial neural networks (ANN) to estimate ground subsidence caused by underground mining. The experiments showed that the most suitable network structure is a network with three layers of perceptrons and four neurons in the hidden layer with the back propagation algorithm (BP) as a training algorithm. The subsidence observation data in the Mong Duong underground coal mine and other parameters, including: (1) the distance from the centre of the stope to the ground monitoring points; (2) the volume of mined-out space; (3) the positions of the ground points in the direction of the main cross-section of the trough; and (4) the time (presented by cycle number), were used as the input data for the ANN. The findings showed that the selected model was suitable for predicting subsidence along the main profile within the subsidence trough. The prediction accuracy depended on the number of cycles used for the network training as well as the time interval between the predicted cycle and the last cycle in the training dataset. When the number of monitoring cycles used for the network training was greater than eight, the largest values of RMS and MAE were less than 10 % compared to the actual maximum subsidence value for each cycle. If the network training was less than eight cycles, the results of prediction did not meet the accuracy requirements.

About the Authors

Q. L. Nguyen
Hanoi University of Mining and Geology
Viet Nam

Quoc Long Nguyen – PhD, Department of Mine Surveying, Faculty of Geomatics and Land Administration

Scopus ID 57204138384

Hanoi



Q. M. Nguyen
Hanoi University of Mining and Geology
Viet Nam

Quang Minh Nguyen – Associate Professor of Surveying, Department of Surveying, Faculty of Geomatics and Land Administration

Scopus ID 57198770131

Hanoi



D. T. Tran
Hanoi University of Civil Engineering
Viet Nam

Dinh Trong Tran – PhD, Deparment of Geodesy and Geomatics Engineering, Faculty of Bridges and Roads

Scopus ID 57222386870

Hanoi



X. N. Bui
Hanoi University of Mining and Geology
Viet Nam

Xuan Nam Bui – Professor of Mining, Full Professor, Department of Surface Mining, Faculty of Mining

Scopus ID 36892679300

Hanoi



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Supplementary files

For citation: Nguyen Q.L., Nguyen Q.M., Tran D.T., Bui X.N. Prediction of ground subsidence due to underground mining through time using multilayer feed-forward artificial neural networks and back-propagation algorithm – case study at Mong Duong underground coal mine (Vietnam). Gornye nauki i tekhnologii = Mining Science and Technology (Russia). 2021;6(4):241–251. https://doi.org/10.17073/2500-0632-2021-4-241-251

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