1) prediction of rock fragmentation
爆破块度预测
1.
Based on the experiments of engineering blasting, a neural network model for prediction of rock fragmentation in rock-fill material by blasting is established.
在简要阐述了神经网络BP模型及其反传算法一般原理的基础上 ,基于工程实例 ,建立了堆石坝料开采爆破块度预测的神经网络BP模型 ,并将网络预测值与R—R、G—G—S分布模型拟合值进行了比较 ,结果证明网络预测模型是可靠
2) forecasting equation of blasting fragmentation distribution
爆破块度分布预测模型
3) Blasting fragmentation
爆破块度
1.
Based on the fractal characteristics of the blasting fragmentation distribution,the paper derived a relationship between the explosive specific charge and the uniform exponent of the distribution,which is instructive for predicting the fragmentation.
基于爆破块度分布的分形特征，从理论上推导了炸药单耗与爆破块度分布均匀性指数的关系，对爆破块度分布的预测具有重要的指导意义。
2.
This paper verified with grey correlation analysis method that the explosives unit consumption is the main factor affecting the blasting fragmentation and also demonstrated that the adjustment of pattern parameters in blast design is still very important to the control of blasting fragmentation.
采用灰色关联分析方法,确定出申嘉湖高速公路路堑开挖爆破中爆破块度的主要因素为炸药单耗,同时指出,在爆破设计中调整孔网参数,对控制爆破块度仍有重要作用。
5) Blasting fragment
爆破块度
1.
Application of on artificial neural networks to blasting fragment prediction;
人工神经网络在爆破块度预测中的应用研究
2.
8 detonator as blasting source in deepwater condition which is simulated by explosion vessel,then the blasting fragments are collected and analyzed statistically.
利用爆炸容器模拟深水条件,以8号雷管作为爆源进行了水泥砂浆试件爆破试验,收集爆破碎块进行统计,根据分形理论计算了爆破块度在不同模拟水深条件下的分形维数。
3.
The instantaneous and millisecond blasting crater model experiment of frozen sand is conducted at -17℃, and the blasting fragments are collected and statistically analyzed.
对冻土进行了一次起爆、孔内微差爆破漏斗试验,收集爆破块度进行筛分统计,根据分形理论,计算了冻土爆破块度的分形维数。
6) fragment distribution of blasting
爆破块度分布
1.
The fragment distribution of blasting is the important index of estimating blasting.
爆破块度分布是评价爆破效果的重要指标。
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