1) Wall rock classification
围岩等级判别
2) the classification of rock masses
围岩判别
1.
Based on the artificial neural network theory, combining the factual project datum of highway tunnels classification of rock masses, using the BP neural networks model with teacher study and predicting function, the classification of rock masses in highway tunnel is studied.
基于人工神经元网络原理,结合公路隧道围岩判别的工程实际资料,通过BP神经网络模型的有师学习记忆和预测功能,对公路隧道围岩判别预测进行了研究。
3) surrounding rock level
围岩级别
1.
Meanwhile,this article gains the stable values analyzes of convergence displacement and vault crown settlement after lining completed,which are used as stability evaluation index of different surrounding rock levels.
基于几十座两车道NATM高速公路隧道的现场监测数据,通过对大量的围岩收敛变形和拱顶下沉数据的统计分析,探讨隧道Ⅱ、Ⅲ、Ⅳ、Ⅴ级围岩随时间的变形特性,分析不同级别围岩从开挖到变形稳定时的时间以及与围岩级别的关系;得到变形稳定时的收敛和拱顶下沉值,以此作为隧道不同级别围岩变形的稳定性评价指标;为隧道施工阶段围岩的稳定性评价以及二次衬砌的合理施作时机提供了依据。
2.
The deformation characteristics of different surrounding rock levels from II to V with time were also studied,and the stable time was proposed for surrounding rock from excavation to stabilization.
基于50多条两车道NATM高速公路隧道的现场监测数据,通过对围岩收敛变形和拱顶下沉数据的统计分析,探讨隧道II,III,IV,V级围岩随时间的变形特性,得到不同级别围岩隧道从开挖到变形稳定的时间,分析稳定时间与围岩级别的关系,为隧道施工阶段围岩的稳定性评价以及二次衬砌的合理施做时机提供依据。
4) grade identification
等级判别
1.
In view of the drawbacks of apple grade identification in China, which still relies on human sense organs, ths paper presents a processing method on the basis of the technology of computer vision and digital image.
采用改进后的LVQ网络算法,对苹果进行等级判别试验,取得了良好的试验结果,识别正确率达88。
5) rock mass grading
围岩等级数
1.
The quantitative relation between rock mass grading and the consumption of cutter used is proposed.
在此基础上,提出围岩等级数的概念,得出不同的等级数与其相应的耗刀量之间的定量关系。
6) discriminance of wall rocks balance
围岩平衡判别法
补充资料：里雅普诺夫判别定理
见运动稳定性。
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