1) prediction and classification of rockburst
岩爆预测与分级
2) classification prediction of rockburst
岩爆分级预测
3) prediction and prevention of rockburst
岩爆预测与防治
4) rock burst forecast
岩爆预测
1.
Making use of the artificial neural network method,a rock burst forecast of artificial neural network model was established and according to some underground engineering measured parameters,it validated the reliability of the forecasting model.
通过收集国内外多个地下工程和矿山实际岩爆资料,分析了影响岩爆发生的主要因素,运用人工神经网络方法,建立了岩爆预测的人工神经网络模型,并根据某地下工程的实测参数,验证了预测模型的可靠性。
5) Rock burst prediction
岩爆预测
1.
In the light of the non-linear character of rock burst and master factors of rock burst,a non-linear grey model of classification for rock burst prediction is established.
根据岩爆的非线性特征,选取岩爆的主控因子,建立岩爆预测的非线性灰色归类模型,把该模型应用于实例当中,并将分析结果与实际情况进行对比,结果表明,该模实用性强,具有良好的应用前景。
2.
There is a high tendency of rock burst in surrounding rock by analyzing the modified brittleness index and model test result,so it is essential to strengthen rock burst prediction and control.
为了从岩性角度评价竖井围岩的岩爆倾向性,开展了室内岩石单轴压缩变形试验和物理模型试验,根据改进脆性指数指标和模型试验的结果,表明竖井围岩有岩爆倾向性,必须加强岩爆预测和防治。
6) rockburst prediction
岩爆预测
1.
The rockburst prediction method based on grey whitenization weight function cluster theory was proposed.
选取岩石弹性能量指数、脆性系数和围岩最大切向应力与岩石单轴抗压强度的比值作为岩爆灾害预测的主要影响因子,并构造了适于岩爆预测的各聚类指标的白化权函数。
2.
The rockburst of an underground cavern directly menaces the execution and securily of an underground engineering, so it is very important to undertake the reasonable rockburst prediction before execution.
地下洞室岩爆的发生直接影响地下工程的施工和威胁施工人员的安全 ,因此 ,进行合理的岩爆预测就显得尤为重要。
3.
Based on the neural network theory, a new rockburst prediction model is established.
采用人工神经网络理论 ,将岩爆先验知识作为学习样本 ,建立了一种新的岩爆预测模型。
补充资料：发育进度预测法(见发生期预测)
发育进度预测法(见发生期预测)
发育进度预测法见发生期预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条