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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://dx.doi.org/10.1007/s12613-024-2916-6
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://dx.doi.org/10.1007/s12613-024-2916-6
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充填体–围岩组合体异源信息相空间重构与稳定性预测

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。

 

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

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.

 

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