充填体–围岩组合体异源信息相空间重构与稳定性预测

陈大鹏; 尹升华; 龙卫国; 严荣富; 张宇飞; 闫泽鹏; 王雷鸣; 陈威

doi:10.1007/s12613-024-2916-6

Volume 31 Issue 7

Jul. 2024

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 31(7): 1500-1511

Dapeng Chen, Shenghua Yin, Weiguo Long, Rongfu Yan, Yufei Zhang, Zepeng Yan, Leiming Wang, and Wei Chen, Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination, Int. J. Miner. Metall. Mater., 31(2024), No. 7, pp. 1500-1511. https://doi.org/10.1007/s12613-024-2916-6

Cite this article as:

Dapeng Chen, Shenghua Yin, Weiguo Long, Rongfu Yan, Yufei Zhang, Zepeng Yan, Leiming Wang, and Wei Chen, Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination, Int. J. Miner. Metall. Mater., 31(2024), No. 7, pp. 1500-1511. https://doi.org/10.1007/s12613-024-2916-6

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研究论文

充填体–围岩组合体异源信息相空间重构与稳定性预测

1)
北京科技大学土木与资源工程学院, 北京 100083, 中国
2)
金川集团龙首矿生产技术室, 金昌 737100, 中国
3)
华东交通大学土木建筑学院, 南昌 330013, 中国

通讯作者:
尹升华 E-mail: csuysh@126.com

文章亮点

（1）构建了充填体–围岩组合体异源信息相空间。

（2）提出以相空间中最邻近点距离作为评估充填体–围岩组合体稳定性的指标。

（3）对比分析了ARIMA、Transformer和LSTM三种模型在充填体–围岩组合体稳定性预测方面的性能。

中文摘要

传统研究认为充填体可以有效控制应力集中，而忽视了充填体–围岩组合体在高应力条件下应力分布复杂多变、稳定性不明的问题。目前的监测数据处理方法存在不能充分考虑监测对象的复杂性、监测方法的多元性及监测数据的动态性等特点。为了解决这个问题，本文提出了充填体–围岩组合体异源信息相空间重构与稳定性预测方法。采用钻孔应力计、多点位移计以及测斜仪等设备构建了龙首矿大面积充填体–围岩组合体立体监测体系，持续获取充填体–围岩组合体应力、位移等多元信息。结合平均互信息法和虚假最邻近点法构建了充填体–围岩组合体异源信息相空间。本文以相点间最邻近点距离作为评估充填体–围岩组合体稳定性的指标——“评估距离”。本文应用该方法计算了龙首矿12个测点的历史评估距离时间序列，结果表明评估距离对充填体–围岩组合体稳定性变化表现出高度敏感性，这些序列的突变时刻比巷道返修时间提前了至少3个月。在评估距离预测实验中，ARIMA模型的预测准确度高于深度学习模型（LSTM和Transformer），它的预测结果的均方根误差分布峰值为0.26，且在70%的情况下优于无预测方法。

关键词:

Research Article

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

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Abstract

Abstract

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions. Current monitoring data processing methods cannot fully consider the complexity of monitoring objects, the diversity of monitoring methods, and the dynamics of monitoring data. To solve this problem, this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations. The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress, multipoint displacement meter, and inclinometer. Varied information, such as the stress and displacement of the filling body–surrounding rock combination, was continuously obtained. Combined with the average mutual information method and the false nearest neighbor point method, the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed. In this paper, the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination. The evaluated distances (ED) revealed a high sensitivity to the stability of the filling body–surrounding rock combination. The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine. The moments of mutation in these time series were at least 3 months ahead of the roadway return dates. In the ED prediction experiments, the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models (long short-term memory and Transformer). Furthermore, the root-mean-square error distribution of the prediction results peaked at 0.26, thus outperforming the no-prediction method in 70% of the cases.
- deep mining;
- filling body–surrounding rock combination;
- phase space reconstruction;
- multiple time series;
- stability prediction

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[1]	S.H. Yin, Z.P. Yan, X. Chen, R.F. Yan, D.P. Chen, and J.W. Chen, Mechanical properties of cemented tailings and waste-rock backfill (CTWB) materials: Laboratory tests and deep learning modeling, Constr. Build. Mater., 369(2023), art. No. 130610. doi: 10.1016/j.conbuildmat.2023.130610
[2]	Z.L. Xue, H.K. Sun, D.Q. Gan, Z.P. Yan, and Z.Y. Liu, Wall slip behavior of cemented paste backfill slurry during pipeline based on noncontact experimental detection, Int. J. Miner. Metall. Mater., 30(2023), No. 8, p. 1515. doi: 10.1007/s12613-023-2610-0
[3]	L.H. Yang, J.C. Li, H.B. Liu, et al., Systematic review of mixing technology for recycling waste tailings as cemented paste backfill in mines in China, Int. J. Miner. Metall. Mater., 30(2023), No. 8, p. 1430. doi: 10.1007/s12613-023-2609-6
[4]	C. Hou, W.C. Zhu, B.X. Yan, K. Guan, and L.L. Niu, Analytical and experimental study of cemented backfill and pillar interactions, Int. J. Geomech., 19(2019), No. 8, art. No. 04019080. doi: 10.1061/(ASCE)GM.1943-5622.0001441
[5]	Q.H. Ma, G.S. Liu, X.C. Yang, and L.J. Guo, Physical model investigation on effects of drainage condition and cement addition on consolidation behavior of tailings slurry within backfilled stopes, Int. J. Miner. Metall. Mater., 30(2023), No. 8, p. 1490. doi: 10.1007/s12613-023-2642-5
[6]	Y.Y. Li, W.J. Guo, H.Q. Zhang, and Z.J. Wei, The application of combined GPS.RTK with electronic level on high-precision surface monitoring, Adv. Mater. Res., 718-720(2013), p. 1191. doi: 10.4028/www.scientific.net/AMR.718-720.1191
[7]	Y. Chen, J. Li, H.Z. Li, et al., Revealing land surface deformation over the Yineng backfilling mining area, China, by integrating distributed scatterer SAR interferometry and a mining subsidence model, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 16(2023), p. 3611. doi: 10.1109/JSTARS.2023.3250419
[8]	H.D. Fan, Q. Xu, Z.B. Hu, and S. Du, Using temporarily coherent point interferometric synthetic aperture radar for land subsidence monitoring in a mining region of Western China, J. Appl. Remote Sens, 11(2017), No. 2, art. No. 026003. doi: 10.1117/1.JRS.11.026003
[9]	C.D. Maio, G. Fornaro, D. Gioia, D. Reale, M. Schiattarella, and R. Vassallo, In situ and satellite long-term monitoring of the Latronico landslide, Italy: Displacement evolution, damage to buildings, and effectiveness of remedial works, Eng. Geol., 245(2018), p. 218. doi: 10.1016/j.enggeo.2018.08.017
[10]	W. Lei, S.J. Zhu, C. Jiang, et al., Research on 3D laser scanning monitoring method for mining subsidence based on the auxiliary for probability integral method, KSCE J. Civ. Eng., 25(2021), No. 11, p. 4403. doi: 10.1007/s12205-021-0053-6
[11]	Y.Y. Gu, D.W. Zhou, D.M. Zhang, K. Wu, and B.H. Zhou, Study on subsidence monitoring technology using terrestrial 3D laser scanning without a target in a mining area: An example of Wangjiata coal mine, China, Bull. Eng. Geol. Environ., 79(2020), No. 7, p. 3575. doi: 10.1007/s10064-020-01767-1
[12]	C. Liao, W.L. Luo, D.S. Cai, and M. Li, In situ measurement of rockfill dam settlement using fiber optic gyroscope monitoring system, Struct. Contr. Health Monit., 29(2022), No. 4, art. No. e2917.
[13]	R.C.S.B. Allil, L.A.C. Lima, A.S. Allil, and M.M. Werneck, FBG-based inclinometer for landslide monitoring in tailings dams, IEEE Sens. J., 21(2021), No. 15, p. 16670. doi: 10.1109/JSEN.2021.3081025
[14]	C. Zhang, S.Y. Zhang, and J.Y. Cao, In-place fiber-optic inclinometer based on a vertical cantilever beam and dual FBGs, Opt. Laser Technol., 159(2023), art. No. 108933. doi: 10.1016/j.optlastec.2022.108933
[15]	Y.Q. Wang, S.B. Zhang, L.L. Chen, Y.L. Xie, and Z.F. Wang, Field monitoring on deformation of high rock slope during highway construction: A case study in Wenzhou, China, Int. J. Distrib. Sens. Netw., 15(2019), No. 12, art. No. 1550147719895953.
[16]	V. Bennett, T. Abdoun, and M. Barendse, Evaluation of soft clay field consolidation using MEMS-based in-place inclinometer–accelerometer array, Geotech. Test. J., 38(2015), No. 3, p. 290. doi: 10.1520/GTJ20140048
[17]	D.W. Ha, J.M. Kim, Y. Kim, and H.S. Park, Development and application of a wireless MEMS-based borehole inclinometer for automated measurement of ground movement, Autom. Constr., 87(2018), p. 49. doi: 10.1016/j.autcon.2017.12.011
[18]	M. Khan, X.Q. He, J. Guo, and D.Z. Song, Accurate prediction of indicators for engineering failures in complex mining environments, Eng. Fail. Anal., 155(2024), art. No. 107736. doi: 10.1016/j.engfailanal.2023.107736
[19]	H.W. Jia, B.X. Yan, Z. Yang, and E. Yilmaz, Identification of goaf instability under blasting disturbance using microseismic monitoring technology, Geomech. Geophys. Geo Energy Geo Resour., 9(2023), No. 1, art. No. 142. doi: 10.1007/s40948-023-00681-6
[20]	X.G. Cheng, W. Qiao, and H. He, Study on deep learning methods for coal burst risk prediction based on mining-induced seismicity quantification, Geomech. Geophys. Geo Energy Geo Resour., 9(2023), No. 1, art. No. 145. doi: 10.1007/s40948-023-00684-3
[21]	T.W. Lan, X.T. Guo, Z.J. Zhang, and M.W. Liu, Prediction of microseismic events in rock burst mines based on MEA-BP neural network, Sci. Rep., 13(2023), No. 1, art. No. 9523. doi: 10.1038/s41598-023-35500-1
[22]	C.Y. Liu, G.H. Sun, X.X. Liu, et al., Construction of filling body instability failure warning model under single-side unloading condition, Rock Mech. Rock Eng., 55(2022), No. 7, p. 4257. doi: 10.1007/s00603-022-02864-1
[23]	Q. Yin, J.Y. Wu, C. Zhu, M.C. He, Q.X. Meng, and H.W. Jing, Shear mechanical responses of sandstone exposed to high temperature under constant normal stiffness boundary conditions, Geomech. Geophys. Geo Energy Geo Resour., 7(2021), No. 2, art. No. 35. doi: 10.1007/s40948-021-00234-9
[24]	Q.C. Ran, Y.P. Liang, Q.L. Zou, et al., Experimental investigation on mechanical characteristics of red sandstone under graded cyclic loading and its inspirations for stability of overlying strata, Geomech. Geophys. Geo Energy Geo Resour., 9(2023), No. 1, art. No. 11. doi: 10.1007/s40948-023-00555-x
[25]	L.M. Wang, X.Q. Zhang, S.H. Yin, X.L. Zhang, P.Z. Liu, and I.M.S.K. Ilankoon, Three-dimensional characterisation of pore networks and fluid flow in segregated heaps in the presence of crushed ore and agglomerates, Hydrometallurgy, 219(2023), art. No. 106082. doi: 10.1016/j.hydromet.2023.106082
[26]	H.J. Lu, Y.R. Wang, D.Q. Gan, J. Wu, and X.J. Wu, Numerical investigation of the mechanical behavior of the backfill–rock composite structure under triaxial compression, Int. J. Miner. Metall. Mater., 30(2023), No. 5, p. 802. doi: 10.1007/s12613-022-2554-9
[27]	W.Y. Cai, Z.C. Chang, D.S. Zhang, X.F. Wang, W.H. Cao, and Y.Z. Zhou, Roof filling control technology and application to mine roadway damage in small pit goaf, Int. J. Min. Sci. Technol., 29(2019), No. 3, p. 477. doi: 10.1016/j.ijmst.2018.10.001
[28]	S.H. Yin, Z.P. Yan, X. Chen, et al., Active roof-contact: The future development of cemented paste backfill, Constr. Build. Mater., 370(2023), art. No. 130657. doi: 10.1016/j.conbuildmat.2023.130657
[29]	M.L. Walske, H. McWilliam, J. Doherty, and A. Fourie, Influence of curing temperature and stress conditions on mechanical properties of cementing paste backfill, Can. Geotech. J., 53(2016), No. 1, p. 148. doi: 10.1139/cgj-2014-0502
[30]	M. Helinski, M. Fahey, and A. Fourie, Behavior of cemented paste backfill in two mine stopes: Measurements and modeling, J. Geotech. Geoenviron. Eng., 137(2011), No. 2, p. 171. doi: 10.1061/(ASCE)GT.1943-5606.0000418
[31]	M. Zhong, P. Yang, and Y.P. Hu, Study of instability mechanism and roof caving mode of cementing filling stope: The case study of a nonferrous metal mine in China, Adv. Civ. Eng., 2022(2022), art. No. 1658021.
[32]	K. Ding, F.S. Ma, J. Guo, H.J. Zhao, R. Lu, and F. Liu, Investigation of the mechanism of roof caving in the Jinchuan nickel mine, China, Rock Mech. Rock Eng., 51(2018), No. 4, p. 1215. doi: 10.1007/s00603-017-1374-0
[33]	H.J. Zhao, F.S. Ma, Y.M. Zhang, and J. Guo, Monitoring and mechanisms of ground deformation and ground fissures induced by cut-and-fill mining in the Jinchuan Mine 2, China, Environ. Earth Sci., 68(2013), No. 7, p. 1903. doi: 10.1007/s12665-012-1877-7
[34]	G. Li, Y. Wan, J. Guo, F.S. Ma, H.J. Zhao, and Z.Q. Li, A case study on ground subsidence and backfill deformation induced by multi-stage filling mining in a steeply inclined ore body, Remote Sens., 14(2022), No. 18, art. No. 4555. doi: 10.3390/rs14184555
[35]	T.B. Zhao, Z.Y. Fu, and G. Li, In situ investigation into fracture and subsidence of overburden strata for solid backfill mining, Arab. J. Geosci., 11(2018), No. 14, art. No. 398. doi: 10.1007/s12517-018-3769-y
[36]	D.P. Chen, S.H. Yin, R.F. Yan, Y. Zhou, Y.F. Zhang, and L.M. Wang, State analysis of the inclinometer tube for monitoring relative slippage between backfill and surrounding rock mass, Int. J. Min. Reclam. Environ., 37(2023), No. 10, p. 856. doi: 10.1080/17480930.2023.2245667
[37]	A.M. Fraser and H.L. Swinney, Independent coordinates for strange attractors from mutual information, Phys. Rev. A, 33(1986), No. 2, p. 1134. doi: 10.1103/PhysRevA.33.1134
[38]	M.B. Kennel, R. Brown, and H.D. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical construction, Phys. Rev. A, 45(1992), No. 6, p. 3403. doi: 10.1103/PhysRevA.45.3403
[39]	H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, Cambridge University Press, Cambridge, 2003.
[40]	S. Wallot and D. Mønster, Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time series in Matlab, Front. Psychol., 9(2018), art. No. 1679. doi: 10.3389/fpsyg.2018.01679
[41]	G.E.P. Box and D.A. Pierce, Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, J. Am. Stat. Assoc., 65(1970), No. 332, p. 1509. doi: 10.1080/01621459.1970.10481180
[42]	D.A. Dickey and W.A. Fuller, Distribution of the estimators for autoregressive time series with a unit root, J. Am. Stat. Assoc., 74(1979), No. 366a, p. 427. doi: 10.1080/01621459.1979.10482531
[43]	H. Akaike, A new look at the statistical model identification, IEEE Trans. Autom. Contr., 19(1974), No. 6, p. 716. doi: 10.1109/TAC.1974.1100705
[44]	G. Schwarz, Estimating the dimension of a model, Ann. Statist., 6(1978), No. 2, p. 461.
[45]	S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput., 9(1997), No. 8, p. 1735. doi: 10.1162/neco.1997.9.8.1735
[46]	A. Vaswani, N. Shazeer, N. Parmar, et al., Attention is all you need, [in] NIPS'17 : Proceedings of the 31st International Conference on Neural Information Processing Systems, California, 2017, p. 6000.