Document Type : Original Research Paper

Authors

1 Faculty of Mining Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea

2 School of Science and Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea

3 Department of Applied Mathematics, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea

Abstract

Anthracite coal seam of Democratic People’s Republic of Korea was broken into particles to be soft due to geological tectonic actions through several stages in the Mesozoic era. Because the folds and faults have excessively developed and the shape of coal seam is very complicated, it is impossible to extract the anthracite coal by longwall mining system, and coal has been mainly mined by entry caving mining system. The aim of this work is to assess effectiveness of new combination of flying squirrel search algorithm (SSA) and artificial neural-network (ANN) for back-analysis of time-depending mechanical parameters of anthracite coal based on timber loads and displacements measured in the coal face entry. The case study deals with a coal face entry in Sinchang Coal Mine located in the Unsan County, South Pyongan Province, DPR Korea. To verify the good performance of new combination of the SSA and ANN, the comparison studies between proposed back-analysis method and other methods with the same purpose, are conducted using data measured in coal face entry. The mean absolute error (MAE) of weighted error norm of ANN-SSA is relatively smaller in comparison with other methods, which is 2.49. The new back-analysis is the good method to determine the suitable time-dependent mechanical parameters of anthracite coal surrounding the entry in very soft coal seam.

Keywords

[1]. Shang, Y.J., Cai, J.G., Hao, W.D., Wu, X.Y., and Li, S.H. (2002). Intelligent back analysis of displacements using precedent type analysis for tunnelling. Tunnelling and Underground Space Technology, 17: 381–389.
[2]. Sakurai, S. and Takeuchi, K. (1983). Back-analysis of measured displacement of tunnels. Rock Mechanics and Rock Engineering, 16: 173–180.
[3]. Oreste, P. (2005). Back-analysis techniques for the improvement of the understanding of rock in underground constructions. Tunnelling and Underground Space Technology, 20: 7–21.
[4]. Han, U.C., Choe, C.S., Hong, K.U., and Han, H.I. (2021). Intelligent back-analysis of geotechnical parameters for time-dependent rock mass surrounding mine openings using grey Verhulst model. Journal of Central South University, 28: 3099-3116.
[5]. Kaiser, P.K., Zou, D. P., and Lang, A. (1990). Stress determination by back analysis of excavation-induced stress changes: a case study. Rock Mechanics and Rock Engineering, 23: 185–200.
[6]. Li, F., Wang, J., and Brigham, J.C. (2014) Inverse calculation of in situ stress in rock mass using the surrogate-model accelerated random search algorithm. Computers and Geotechnics, 61: 24–32.
[7]. Ghorbani, M. and Sharifzadeh, M. (2009). Long term stability assessment of Siah Bisheh powerhouse cavern based on displacement back-analysis method. Tunnelling and Underground Space Technology, 24: 574–583.
[8]. Zhang, L.Q., Yue, Z.Q., Yang, Z.F., Qi, J.X., and Liu, F.C. (2006). A displacement- based back-analysis method for rock mass modulus and horizontal in situ stress in tunnelling-illustrated with a case study. Tunnelling and Underground Space Technology, 21: 636–649.
[9]. Hisatake, M. and Hieda, Y. (2008). Three-dimensional back-analysis method for the mechanical parameters of the new ground ahead of a tunnel face. Tunnelling and Underground Space Technology, 23: 373–380.
[10]. Yang, L., Zhang, K., and Wang, Y. (1996). Back analysis of initial rock time-dependent parameters. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 33(6): 641-645.
[11]. Liang, Y.C., Feng, D.P., Liu, G.R., Yang, X.W., and Han, X. (2003). Neural identification of rock parameters using fuzzy adaptive learning parameters. Computers & Structures, 81: 2373–2382.
[12]. Feng, X.-T., Zhao, H., and Li, S. (2004). A new displacement back analysis to identify mechanical geo-material parameters based on hybrid intelligent methodology. International Journal for Numerical and Analytical Methods in Geomechanics, 28: 1141–65.
[13]. Feng, X.-T., Zhang, Z., and Sheng, Q. (2000). Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method,” International Journal of Rock Mechanics and Mining Sciences, 37(7): 1039-1054.
[14]. Yu, Y., Zhang, B., and Yuan, H. (2007). An intelligent displacement back-analysis method for earth-rockfill dams. Computers and Geotechnics, 34: 423–434.
[15]. Zhang, L.Q., Yue, Z.Q., Yang, Z.F., Qi, J.X., and Liu, F.C. (2006). A displacement-based back-analysis method for rock mass modulus and horizontal in situ stress in tunneling – Illustrated with a case study. Tunnelling and Underground Space Technology, 21: 636–649.
[16]. Yazdani, M., Sharifzadeh, M., Kamrani, K., and Ghorbani, M. (2012). Displacement-based numerical back analysis for estimation of rock mass parameters in Siah Bisheh powerhouse cavern using continuum and discontinuum approach. Tunnelling and Underground Space Technology, 28: 41–48.
[17]. Sharifzadeh, M., Tarifard, A., and Moridi, M.A. (2013). Time-dependent behaviour of tunnel lining in weak rock mass based on displacement back-analysis method,” Tunnelling and Underground Space Technology, 38: 348–356.
[18]. Gao, W. and Ge, M. (2016). Back-analysis of rock mass parameters and initial stress for the Longtan tunnel in China. Engineering with Computers, 32: 497–515.
[19]. Yu, F., Peng, X., and Su, L. (2017). A back-propagation neural-network-based displacement back analysis for the identification of the geo-mechanical parameters of the Yonglang landslide in China. Journal of Mountain Science, 14: 1739–1750.
[20]. Gao, W., Chen, D., Dai, S., and Wang, X. (2018). Back-analysis for mechanical parameters of surrounding rock for underground roadways based on new neural network. Engineering with Computers, 34: 25–36.
[21]. Luo, Y., Chen, J., Chen, Y., Diao, P., and Qiao, X. (2018). Longitudinal deformation profile of a tunnel in weak rock mass by using the back analysis method. Tunnelling and Underground Space Technology, 71: 478-493.
[22]. Mohamad, T., Armaghani, E. J., and Moment, D. (2015). Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74(3): 745-757.
[23]. Rezaei, M. (2020). Predicting Unconfined Compressive Strength of Intact Rock using New Hybrid Intelligent Models. Journal of Mining and Environment, 11: 231-246.
[24]. Rezaei, M. (2022). Feasibility of novel techniques to predict the elastic modulus of rocks based on the laboratory data. International Journal of Geotechnical Engineering, 14: 25-34.
[25]. Rezaei, M. (2018). Indirect measurement of the elastic modulus of intact rock using the Mamdani fuzzy inference system. Measurement, 129: 319-331.
[26]. Wang, Y. and Rezaei, M. (2023). Developing Two Hybrid Algorithms for predicting the elastic modulus of intact rocks. Sustainability, 15: 1-24.
[27]. Jain, M., Singh, V., and Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44: 148-175.