Study On Rockbursts Prediction Of TBM Construction At Headrace Tunnels Of JinPing Ⅱ Hydropower Station

Since the seventies and eighties of last century, the TBM Boring Machine(tunnel boring machine) and tunneling technology is an unprecedented development,the excavation is fast, friendly work environment characteristics are more widelyrecognized. With the development and progress of the works of the tunnel, the TBMtunneling technology is quite mature, widely used in energy, transportation, water,defense and other departments in the world of underground construction projects.Under normal circumstances, when the tunnel length>6km or aspect ratio>600,should give priority to the TBM to dig. With the continuous expansion anddevelopment of the tunnel project, the underground cavern."Long, large, deep group"characteristics become more obvious, thus rockburst become increasingly prominent.Jinping hydropower tunnel project depth to stress Heights stress caused by rock burstproblem encountered in using the TBM method of construction, the constructionschedule, equipment and personnel security implications. Therefore, in order toovercome the strong rockburst serious accidents, TBM TBM construction to providetimely feedback and the rockburst hole TBM construction to prevent major accidentsto provide guidance for TBM construction of rock burst prediction is particularlyimportant.First Jinping hydropower diversion tunnel area geostress field regressionanalysis, come to the point of diversion tunnel to stress values found that the stressfield of the project area is basic to the gravitational field.Collected through field research, analysis of practical engineering TBMconstruction of rock burst phenomena, summarized Jinping hydropower tunnel TBMconstruction of the tunnel face rock burst pit rupture surface there is often a boardcracking phenomenon, and a strong rockburst dome-shaped and the destruction of thestrong rock burst to burst main. TBM construction rockburst damage encountered inthe wall position generally in the right shoulder and the left arch foot of the wall, thetype of rock burst and more sporadic rock burst-based, continuous rock burst.For rock burst prediction using support vector machine as a tool for predictionof rockburst support vector machine learning through less sample, and has a goodlearning performance and generalization ability, and has a strong nonlinear systemmodeling capacity. Impact on the the TBM actual construction of rock burst factoranalysis, the maximum principal stress, the elastic modulus, uniaxial compressive strength, rock brittleness index, number of volume of joints, the joints and the holeaxis angle and driving speed7indicators as rockburst evaluation of influencingfactors and prediction of rockburst. Jinping hydropower TBM construction rockburstsamples to study rock burst prediction model is established, and better accuracy inpredicting samples to prove this based support vector machine-based rock burstprediction accuracy of the model is high, you can use forecasting rock burst intensity.