Simulation of Stress Distribution around Tunnels and Interaction between Tunnels Using an Elasto–plastic Model

This article presents a computer simulation of stress distribution around tunnels and interaction between tunnels using an elasto-plastic model. A finite element method using ANSYS software has been used for the analyses of one and two tunnels at different overburden depths with different separating distances between the tunnels. The results of numerical analyses indicate that stress distribution and stress concentration around the tunnels vary with the overburden depths. It is found that the coefficients of stress concentration for elasto-plastic medium are smaller than those for elastic one by 1.9%. Furthermore, the interaction between the two tunnels rapidly decreases with the increase of separation distance between them. In addition, for quantitatively describing the interaction between the two tunnels, a critical separation distance is introduced. The critical separation distances between the two tunnels at different overburden depths are 8 m, 12 m, and 14 m respectively. This fact is very important and essential for the design of mining tunnels and to ensure safety in tunnel engineering.     

NOAA IMS雪冰产品在青藏高原积雪监测中的适用性分析

在系统评估青藏高原积雪观测典型气象站历史定位坐标精度基础上,利用站点雪深资料对NOAA IMS 4 km和1 km分辨率雪冰产品在青藏高原的精度和适用性进行了验证和评估,定量分析了IMS 4 km到1 km空间分辨率提高和气象站历史定位与GPS定位坐标之间的差异对青藏高原IMS积雪监测精度的影响。结果表明：青藏高原个别气象站历史坐标与当前GPS接收机定位之间存在较大的差异,如安多气象站经度偏小0.6°,纬度偏大0.08°。IMS 4 km雪冰产品在青藏高原的总精度介于76.4%~83.2%,平均为80.1%,积雪分类精度介于35.8%~60.7%,平均为47.2%,平均误判率为17.1%,平均漏判率为45.5%,总体上呈现地面观测的积雪日数越多、平均雪深越大,其总体监测精度越低,而积雪分类精度越高的特点。IMS分辨率从4 km到1 km总体精度平均提高了2.9%,积雪分类精度平均提高了0.9%,主要是由于个别站点的精度提升较大引起的,对高原多数台站积雪监测精度的改进和提升很小。除个别台站外,目前气象站历史坐标和GPS定位坐标之间的差异,对IMS 4 km积雪监测精度验证结果没有影响。然而,今后随着卫星遥感技术的发展,更高时空分辨率的遥感积雪产品将用于积雪监测和研究,精确的地面观测站坐标信息是对这些遥感数据开展精度验证与实际应用的前提。     

局地强风暴:观念与认识

一.概述早期对于雷暴的认识主要是根据肉眼观测结果推导出来的。由于缺乏适合探测雷暴内部结构的工具,使雷暴发展机制的研究遇到重重困难。到了二十世纪四十年代,随着气象雷达和气象飞机的发展,获得了关于上升气流,下沉气流,降水类型以及风暴单体发展过程等的初步定量资料。在五十年代,由于广泛地应用雷达,发展雷暴在诱发机制不出现的情况下仍可出现较强的高空风切变,而且风暴向环境风的左方或右方移动,可以维持几个小时。于是,更加清楚地认识到雷暴与其周围环境相互作用的复杂性。六十年代,通过特殊的     

西澳大利亚皮尔巴拉地块诺思波勒地区太古宙燧石-重晶石矿床的沉积环境

在诺思波勒(North Pole)地区,有一个燧石-重晶石岩石单位,属皮尔巴拉地块东部瓦拉沃纳(Warrawoona)群(3500—3400Ma)的最下部沉积夹层,该群主要为超镁铁-镁铁火山岩岩系。该岩石单位包括四种沉积岩类型:硅化碳酸盐岩、硅化和重晶石化蒸发硫酸盐、原生硅质沉积物变质后的条带状燧石、以及硅化火山碎屑沉积岩。硅化碳酸盐岩,呈砂粗碎屑岩至细屑岩韵律,或呈含有少量层间碎屑砾岩的细屑岩     

纪念陈光远教授(英文)

仅以此文悼念陈光远教授。陈光远教授毕生献身于地质科学 ,是一位卓越的地质学家。本人与中国同行的交往始于 1 973年 ,当时我应邀参加了华北地台古老变质岩系的地质旅行。后来我又多次访问中国 ,并于 1 996年在北京成功地主持召开了第三十届国际地质大会。陈光远教授是我有幸结识的杰出的中国同行之一 ,在我访问中国期间他曾带我参观考察了他多年从事研究工作的矿区。陈光远教授毕业于西南联大 ,并在瑞典获得副博士学位 ,回到祖国后一直致力于地质学教学与研究工作。他为中国矿产资源的开发做出了不朽的贡献 ,发表的成果及著作涉及金矿、铁矿等许多领域 ,尤其突出的是他编著的胶东金矿省矿物学填图图集开拓了找矿矿物学的新篇章。陈光远教授生前曾多次获奖 ,他的成果得到国际同行的赞誉。他是挪威、瑞典、芬兰、丹麦地质学会的终身会员 ,并被推选为俄罗斯科学院外籍院士。陈光远教授去世前正在与我合作进行有关植树造林防止水土流失的中加合作项目 ,他的去世也是该项目的巨大损失。可喜的是孙岱生教授及其同事将负起重任 ,完成陈光远教授未竟的事业。     

利用纵波地震资料确定方位各向异性的主方向

在方位各向异性介质中,方位各向异性的主方向就是准纵波和准横汉传播的方向,即准纵波和准横波为纯纵波和纯横波方式的方向。如果方位各向异性是由作用于一个方位各向同性背景中的定向垂直裂缝引起的,则这些主方向中的其中两个对应平行和垂直于裂缝的方向。横波穿过一个方位各向异性介质时,对与主方向有关的传播方向很敏感。     

柳伊河水电站大坝右岸坝区研究

In the paper in order to ascertain the condition of the right-bank of the dam at the Vilyui hydroelectric power plant measurements are applied to obtain experimental date such as drilling borehole,hydrolocation and so forth. By finding out the distribution of the temperature field some corresponding measures are taken to improve the stability of the dam during the change of the ground temperature. And finally some conclusions are draw to the study.     

http://www.sciencedirect.com/science/article/pii/S1674987111001137

Integration of remote sensing data and the geographical information system(GIS) for the exploration of groundwater resources has become a breakthrough in the field of groundwater research, which assists in assessing,monitoring,and conserving groundwater resources.In the present paper, various groundwater potential zones for the assessment of groundwater availability in Theni district have been delineated using remote sensing and GIS techniques.Survey of India toposheets and IRS-1C satellite imageries are used to prepare various thematic layers viz.lithology,slope,land-use,lineament, drainage,soil,and rainfall were transformed to raster data using feature to raster converter tool in ArcGIS.The raster maps of these factors are allocated a fixed score and weight computed from multi influencing factor(MIF) technique.Moreover,each weighted thematic layer is statistically computed to get the groundwater potential zones.The groundwater potential zones thus obtained were divided into four categories,viz.,very poor,poor,good,and very good zones.The result depicts the groundwater potential zones in the study area and found to be helpful in better planning and management of groundwater resources.