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| ANALYSIS AND EVALUATION SYSTEM OF RAPID MONITORING OF LARGE-SCALE UNDERGROUND CAVERN GROUP DURING CONSTRUCTION PERIOD IN SOUTHWEST CHINA |
| CAI Dewen1，2，WANG Dikai1，2，CHEN Xiaopeng1，2 |
| (1. PowerChina Chengdu Engineering Corporation Limited，Chengdu，Sichuan 610072，China；2. Large-scale Underground Engineering Affiliation，National Hydropower Technology Research and Development Center，Chengdu，Sichuan 610072，China) |
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Abstract The typical geomorphic features of high mountains and deep canyons and the regional high geostress conditions lead to the engineering complexity and the difficulty of construction of the large-scale underground cavern groups for hydropower stations beyond the scope of present knowledge. Utilizing the massive monitored data from the safety monitoring with the scale in ever expansion，an analysis and evaluation system of the rapid monitoring of large-scale underground caverns in the Southwest China was established by means of the monitoring softwares. The evaluation of the stability of surrounding rock of caverns and the design of the construction process of cavern group of the underground powerhouse can be carried out with the system，so as to ensure the stability of surrounding rock and the safety operation of the underground caverns. Through the statistical analysis of a large number of monitored data，five displacement evaluation indices and five supporting load evaluation indices were proposed to monitor the stability of surrounding rock in the system. Relating the grading of evaluation indices for the grading of surrounding rock stability，the stability evaluation of the surrounding rock in underground caverns can be obtained rapidly. The evaluation system was applied to the subsequent construction of caverns and was proved appropriate.
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Received: 26 June 2014
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CHEN Weizhong1*, LIU Xinyu1, 2, YANG Jianping1, WANG Wei1, 2, ZANG Zhonghai3, DING Hongyuan3, ZHANG Zheyuan3, WANG Xiaogang3, SHI Zhengrong1. Development of a large-scale 3D physical model test system for underground energy storage caverns and its model experimental study[J]. , 2026, 45(6): 1615-1628. |
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2014, Vol. 33 
