2010-03期电子版文档 [复制链接]

Preliminary engineering application of microseismic monitoring technique to rockburst prediction in tunneling of Jinping II project

Chun’an Tang^1*, Jimin Wang², Jingjian Zhang^3
1 School of Civil Engineering, Dalian University of Technology, Dalian, 116024, China
² Jinping Construction Management Authority, Ertan Hydropower Development Co., Ltd., Xichang, 615012, China
³ School of Hydraulic Engineering, North China Institute of Water Conservancy and Hydroelectric Power, Zhengzhou, 450011, China
Received 2 August 2010; received in revised form 15 August 2010; accepted 20 August 2010
      

Abstract: Monitoring and prediction of rockburst remain to be worldwide challenges in geotechnical engineering. In hydropower, transportation and other engineering fields in China, more deep, long and large tunnels have been under construction in recent years and underground caverns are more evidently featured by “long, large, deep and in group”, which bring in many problems associated with rock mechanics problems at great depth, especially rockburst. Rockbursts lead to damages to not only underground structures and equipments but also personnel safety. It has been a major technical bottleneck in future deep underground engineering in China. In this paper, compared with earthquake prediction, the feasibility in principle of monitoring and prediction of rockbursts is discussed, considering the source zones, development cycle and scale. The authors think the feasibility of rockburst prediction can be understood in three aspects: (1) the heterogeneity of rock is the main reason for the existence of rockburst precursors; (2) deformation localization is the intrinsic cause of rockburst; and (3) the interaction between target rock mass and its surrounding rock mass is the external cause of rockburst. As an engineering practice, the application of microseismic monitoring techniques during tunnel construction of Jinping II Hydropower Station was reported. It is found that precursory microcracking exists prior to most rockbursts, which could be captured by the microseismic monitoring system. The stress concentration is evident near structural discontinuities (such as faults or joints), which shall be the focus of rockburst monitoring. It is concluded that, by integrating the microseismic monitoring and the rock failure process simulation, the feasibility of rockburst prediction is expected to be enhanced.

Key words: microseismic monitoring; numerical modeling; rockburst; prediction

2010-03-01.pdf (2341 K) 下载次数:341

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Stability of roadways in coalmines alias rock mechanics in practice

Richard Šňupárek, Petr Konečný
Institute of Geonics, Academy of Sciences of Czech Republic, Brno 60200, Czech Republic
Received 23 June 2010; received in revised form 28 August 2010; accepted 8 September 2010


Abstract: This paper describes the procedures for design of supports and stabilization measures in the roadways. The procedures are based on the system developed in Ostrava-Karvina coal basin in Czech Republic. The calculation of load bearing capacity of roadway supports contains the period of roadway construction and mining in the vicinities, based on the size of the natural rock arch. The loading of supports during mining comes from a stress wave in the rock mass in the forefront of coalface and the caving area of mined-out panel. The input data for the calculation method are deduced according to in-situ measurements of convergence and displacement in the roadways.
Key words: coalmines; roadways; stability; support; bolting


2010-03-09.pdf (1544 K) 下载次数:77

Sensitivity and inverse analysis methods for parameter intervals

Guojian Shao1, Jingbo Su2
1 Department of Engineering Mechanics, Hohai University, Nanjing, 210098, China
2 College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing, 210098, China
Received 30 April 2010; received in revised form 15 June 2010; accepted 25 June 2010


Abstract: This paper proposes a sensitivity analysis method for engineering parameters using interval analyses. This method substantially extends the application of interval analysis method. In this scheme, parameter intervals and decision-making target intervals are determined using the interval analysis method. As an example, an inverse analysis method for uncertainty is presented. The intervals of unknown parameters can be obtained by sampling measured data. Even for limited measured data, robust results can also be obtained with the inverse analysis method, which can be intuitively evaluated by the uncertainty expressed in terms of an interval. For complex nonlinear problems, an iteratively optimized inverse analysis model is proposed. In a given set of loose parameter intervals, all the unknown parameter intervals that satisfy the measured information can be obtained by an iteratively optimized inverse analysis model. The influences of measured precisions and the number of parameters on the results of the inverse analysis are evaluated. Finally, the uniqueness of the interval inverse analysis method is discussed.
Key words: interval analysis method; sensitivity analysis; reversible inverse analysis method; iteratively optimized inverse analysis method

2010-03-08.pdf (151 K) 下载次数:62