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)

Q L Nguyen,X N Bui,Q M Nguyen,D T Tran

doi:10.17073/2500-0632-2021-4-241-251

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.

Highlights

Underground mining produces large goaf that unbalances natural stress in the ground
The accuracy of the subsidence prediction results was evaluated based on mean absolute error (MAE), root mean square (RMS) and r values for all monitoring points in the same cycle
The three-layer feed-forward neural network applying back-propagation algorithm, the most common training algorithm, form a suitable Artificial Neural Network (ANN) model to predict subsidence of monitoring points located on the surface along the main profile within a subsidence trough in underground coal mining area

Summary

Introduction

Underground mining produces large goaf that unbalances natural stress in the ground. gravity forces the soil and rock above to tend towards filling the goaf below to achieve a new equilibrium [1]. Lee et al [10] used the input data including topography, geology, mining methods etc., for ANN training and creating the map of ground surface subsidence prediction in an underground mining area. ANN seems to be the most suitable method for predicting and interpolating surface subsidence through time in mining areas, especially at underground coal mines such as in Quang Ninh, Vietnam. The testing results allowed evaluating the effect of the number of training cycles as well as the ANN ability to predict surface subsidence at underground coal mines in Quang Ninh area

Function of subsidence prediction for Multi-Layer Perceptron ANN

Selection of ANN architectural parameters

Trainning feed-forward ANN

29 Epochs

Results and discussion

Conclusion