1) 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。
2) Wall rock classification
围岩等级判别
3) classification of surrounding rock
围岩类别
1.
In this paper,the engineering geologic characteristics and classification of surrounding rock of the Cretaceous soft rock of Baichuan hydropower station tunnel are deeply expounded,the pattern of e.
该文深刻阐述了白川水电站隧洞白垩系软岩围岩工程地质特性、围岩类别划分,总结了施工中所采用开挖及支护型式、控制围岩稳定的措施和方法。
4) surrounding rock classification
围岩类别
1.
Comparison analysis of surrounding rock classification before and after excavation on powerhousre of Laxiwa hydropower station;
拉西瓦水电站主厂房施工前后围岩类别对比分析
5) 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。
补充资料：工业行业类别
工业行业类别：国家 1994 年制定的《国民经济行业分类与代码》（ GB /T4754 - 94 ），将现行的%26#8220;工业%26#8221;门类调整为%26#8220;采掘业%26#8221;%26#8220;制造业%26#8221;和%26#8220;电力、煤气和水的生产和供应业%26#8221;三个门类。依据产业活动单位其经济活动性质的同一性进行分类，即按企业生产的产品经济用途、使用的原材料、生产工艺性质来划分，而不是按其所属行政管理系统分类。
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