Rockburst Prediction From the Perspective of Energy Release: A Case Study of a Diversion Tunnel at Jinping II Hydropower Station

As a man-made engineering hazard, it is widely accepted that the rockbursts are the result of energy release. Previous studies have examined the unloading of in-situ stress resulting from deep tunnel excavation as a quasi-static process but the transient stress variation during excavation has received less attention. This research discusses rockbursts that happened during the construction of a diversion tunnel at Jinping II hydropower station. The brittle-ductile-plastic (BDP) transition property of Jinping marble was numerically described by the Hoek-Brown strength criterion, and the dynamic energy release process derived from the transient unloading of in-situ stress was studied using an index, local energy release rate. Studies have shown that, due to transient unloading, the strain energy of the surrounding rock mass goes through a dynamic process of decreasing at first, increasing second, then reducing before finally stabilizing. The first decrease of strain energy results from elastic unloading waves and does not cause brittle failure in rock masses, which is consistent with the elastic condition but the secondary reduction of strain energy is because the accumulated strain energy in rock masses exceeds the storage limit, which will inevitably trigger the brittle failure in the rock mass. Thus, the shorter the distance to the tunnel wall the bigger and more intense the energy release. Finally, a relationship between the average value of the local energy release rate and the rockburst intensity was established to assess the risk of rockburst induced by the blasting excavation of a deep tunnel.

Research on Evaluation Method of Rockburst Proneness Based on Energy Principles

The study of rockburst criterion is the key to predict whether rockburst occurs or not. First of all, based on the energy principle and taking the rock strength and overall failure criterion as the benchmark, the rockburst proneness criterion of rock mass unit under compression and tension was established. The criterion took into account the integrity factors, mechanical factors, brittleness factors and energy storage factors in the process of rockburst inoculation, and three rockburst classification thresholds (2, 11 and 110) for four grades of none, weak, moderate and severe rockburst were proposed. Second, Taking the typical rockburst disaster as examples, the rationality of the existing classical rockburst criterions and the rockburst proneness criterion proposed in this paper were tested, and the results showed that this criterion had good engineering applicability. Finally, the numerical simulation analysis of rockburst disaster in 2# diversion tunnel of Jinping II hydropower station was carried out by using this criterion. The results were basically consistent with the actual situation, which verified the accuracy and effectiveness of the rockburst proneness criterion proposed in this paper. The research results can provide reference for the evaluation and prediction of rockburst disaster in deep underground engineering.

Research on simulation and test analysis of hydraulic transient process in Jinping II Hydropower Station

The paper takes Jinping II Hydropower Station (8 × 600MW) as an example based on the internal mechanism and operating characteristics of the system, the complete simulation model of the super-long and large water diversion and power generation system has been completed finally.Besides, the method of parameter calibration and correction for the main elements of the system is proposed.By the simulation model, two typical hydraulic transient process test conditions are simulated, here the involved test conditions are as following: double load rejection and primary frequency regulation.At the same time, the calculated results are compared with the test results. The results show that the proposed simulation model can describe the dynamic response characteristic of the super-long and large water diversion and power generation system accurately.

A Microseismicity-Based Method of Rockburst Intensity Warning in Deep Tunnels in the Initial Period of Microseismic Monitoring

Rockburst disasters in deep tunnels cause serious casualties and economic losses. It is a great challenge to make a warning for rockbursts in geotechnical engineering. In this work, a microseismicity-based rockburst intensity warning method is proposed that is suitable for use in deep tunnels in the initial period of microseismic (MS) monitoring. The method first involves collecting information on a sample of no more than five cases. Then, the event to be analyzed is combined with the sample events and subjected to cluster analysis. Finally, a rockburst intensity warning is generated according to the results of the cluster analysis or after a second cluster analysis. It is a comprehensive, multi-parameter rockburst intensity warning method that only needs a few rockburst cases for input which makes it suitable in the initial period of MS monitoring. The method also incorporates the novel idea of a second cluster analysis. An engineering application based on deep tunnels in the Jinping II hydropower station in Sichuan Province, China, shows that the rockburst intensity warning results based on the proposed method agree well with the actual situations in four tests carried out. The method will enrich the techniques used to warn of rockbursts based on microseismicity.

The Prevention and Control Mechanism of Rockburst Hazards and Its Application in the Construction of a Deeply Buried Tunnel

Rockburst hazards induced by high geostress are particularly prominent during the construction of underground engineering. Prevention and control of rockburst is still a global challenge in the field of geotechnical engineering, which is of great significance. Based on the tunnel group of the Jinping II hydropower station of China, this paper analyzed the mechanical principle of support in the process of construction, and discussed in detail the active release and passive support by numerical simulation and field application. The results show that as two active measures, stress relieve holes and advanced stress relief blasting can release the energy of the microseismic source and transfer the high stress to the deeper surrounding rock, make the surface rock wall with a relatively low stress act as a protective barrier. Their stress release rate is about 12% and 33% in this project, respectively. In term of passive measure, the combined rapid support, which is mainly composed of water swelling anchor and nano-admixture shotcrete, is also an effective way to prevent and control the rockburst under high geostress.

A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model

Hydropower is one of the most important renewable energy sources. However, the safe construction of hydropower stations is seriously affected by disasters like rockburst, which, in turn, restricts the sustainable development of hydropower energy. In this paper, a method for rockburst prediction in the deep tunnels of hydropower stations based on the use of real-time microseismic (MS) monitoring information and an optimized probabilistic neural network (PNN) model is proposed. The model consists of the mean impact value algorithm (MIVA), the modified firefly algorithm (MFA), and PNN (MIVA-MFA-PNN model). The MIVA is used to reduce the interference from redundant information in the multiple MS parameters in the input layer of the PNN. The MFA is used to optimize the parameter smoothing factor in the PNN and reduce the error caused by artificial determination. Three improvements are made in the MFA compared to the standard firefly algorithm. The proposed rockburst prediction method is tested by 93 rockburst cases with different intensities that occurred in parts of the deep diversion and drainage tunnels of the Jinping II hydropower station, China (with a maximum depth of 2525 m). The results show that the rates of correct rockburst prediction of the test samples and learning samples are 100% and 86.75%, respectively. However, when a common PNN model combined with monitored microseismicity is used, the related rates are only 80.0% and 61.45%, respectively. The proposed method can provide a reference for rockburst prediction in MS monitored deep tunnels of hydropower projects.