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Global stability coefficient of large underground caverns under static loading and earthquake wave condition

Journal of Central South University 2024年第8期31卷 2826-2843页

作者： Chen, Peng-fei Jiang, Quan Liu, Jian Li, Shao-jun Chen, Tao He, Ben-guo State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan China University of Chinese Academy of Sciences Beijing China Power China Chengdu Engineering Corporation Limited Chengdu China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang China

underground energy and resource development, deep underground energy storage and other projects involve the global stability of multiple interconnected cavern groups under internal and external dynamic disturbances. An evaluation method of the global stability coefficient of underground caverns based on static overload and dynamic overload was proposed. Firstly, the global failure criterion for caverns was defined based on its band connection of plastic-strain between multi-caverns. Then, overloading calculation of the boundary geostress and seismic intensity on the caverns model was carried out, and the critical unstable state of multi-caverns can be identified, if the plastic-strain band appeared between caverns during these overloading processes. Thus, the global stability coefficient for the multi-caverns under static loading and earthquake was obtained based on the corresponding overloading coefficient. Practical analysis for the Yingliangbao (YLB) hydraulic caverns indicated that this method can not only effectively obtain the global stability coefficient of caverns under static and dynamic earthquake conditions, but also identify the caverns’ high-risk zone of local instability through localized plastic strain of surrounding rock. This study can provide some reference for the layout design and seismic optimization of underground cavern group.

关键词： underground caverns global stability coefficient static-dynamic overload local instability

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Transient electromagnetic detection method in water-sealed underground storage caverns

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underground Space 2016年第1期1卷 44-61页

作者： Fang Lin Shougen Chen Guowei Ma MOE Key Laboratory of Transportation Tunnel Engineering Southwest Jiaotong UniversityChengdu 610031SichuanChina School of Civil Environmental and Mining EngineeringThe University of Western AustraliaPerth 6009Western AustraliaAustralia

Taking advantage of the water sensitivity of the transient electromagnetic method(TEM),this study assesses the effectiveness of the water curtain system for underground LPG storage caverns during the excavation *** also detects fracture water flow during the excavation process in light of the practice of two pilot large underground LPG storage caverns in *** maps of apparent resistivity derived from TEM measurements before and after water-filling during the excavation process have been discussed to improve the quality of the water curtain *** is the first case to apply TEM to detect the quality of the water curtain system during the construction of underground LPG storage cavern projects,and it is found to be practical,more visualized and worth popularizing.

关键词： LPG storage Transient electromagnetic method underground caverns Water curtain system Fracture flow

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Prediction of rockburst classification using Random Forest

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Transactions of Nonferrous Metals Society of China 2013年第2期23卷 472-477页

作者：董陇军李夕兵彭康中南大学资源与安全工程学院长沙410083

The method of Random Forest （RF） was used to classify whether rockburst will happen and the intensity of rockburst in the underground rock projects. Some main control factors of rockburst, such as the values of in-situ stresses, uniaxial compressive strength and tensile strength of rock, and the elastic energy index of rock, were selected in the analysis. The traditional indicators were summarized and divided into indexes I and 1I. Random Forest model and criterion were obtained through training 36 sets of rockburst samples which come from underground rock projects in domestic and abroad. Another 10 samples were tested and evaluated with the model. The evaluated results agree well with the practical records. Comparing the results of support vector machine （SVM） method, and artificial neural network （ANN） method with random forest method, the corresponding misjudgment ratios are 10%, 20%, and 0, respectively. The misjudgment ratio using index I is smaller than that using index II. It is suggested that using the index I and RF model can accurately classify rockburst grade.

关键词： mining engineering tunnel engineering underground caverns rockburst Random Forest

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