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Vibration compaction characteristics of rock filling embankment 被引量:1
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作者 杨明辉 刘广轩 赵明华 《Journal of Central South University》 SCIE EI CAS 2009年第6期1034-1038,共5页
The dynamic model experiment of the rock filling embankment was carried out to investigate the vibration compaction mechanism. The rock filling materials were compacted by the plate-vibrated compactor, and the charact... The dynamic model experiment of the rock filling embankment was carried out to investigate the vibration compaction mechanism. The rock filling materials were compacted by the plate-vibrated compactor, and the characteristics of the rock filling materials, such as settlement, pressure change and response waveform, were measured by the dynamic earth pressure gauge and aceelerometer. Moreover, a new method for detecting the compactness of the rock filling embankment was proposed based on the maximum dry density and modulus of deformation. The results show that the process of vibration compaction includes compact, elastic deformation and loose stages, and the vibratory pressure transfers to the surroundings from the vibration center in non-linear rule. Furthermore, the test results obtained by the present method are basically in agreement with those obtained by the traditional method, and the maximum relative error between them is about 0.5%. 展开更多
关键词 rock filling EMBANKMENT model test vibration compaction COMPACTNESS
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Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination
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作者 Dapeng Chen Shenghua Yin +5 位作者 Weiguo Long Rongfu Yan Yufei Zhang Zepeng Yan Leiming Wang Wei Chen 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第7期1500-1511,共12页
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... 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. 展开更多
关键词 deep mining filling body–surrounding rock combination phase space reconstruction multiple time series stability prediction
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