都汶路恢复重建中的主要地震次生山地灾害与明洞工程

"5·12"汶川地震次生灾害中,有两类山地灾害至今仍对公路运营安全影响很大,其一是高高堆积在坡体之上且规模较大的松散(动)危岩体,其二是与岷江大致正交的泥石流沟泥石流.它们共同的特点是危险物源分布相对位置高、体量大、来势凶猛、冲击破坏性极强,加上它们发生的时间或空间的不确定性,致使常规防治措施无能为力并极易遭到破坏.复式明洞工程则是针对上述灾害专门提出的公路实用性防护工程.经过前期运行,已明显体现出这类工程的优越性,同时也凸显出一些尚需进一步改进的地方.     

金川低品位镍矿石工艺矿物学特性研究

金川低品位镍矿石属发生较强烈氧化的难处理型镍资源,矿石中镍的品位为0.51%,主要以紫硫镍矿形式存在。紫硫镍矿由磁黄铁矿、黄铁矿蚀变而来,呈现铁高、硫低、镍低的特点,选矿中可浮性较差。主要矿物的工艺嵌布粒度统计分析表明,矿石中镍矿物和黄铜矿均属微细粒嵌布的范畴,磁铁矿则具不均匀细粒嵌布的特点。单纯从粒度分布特征来看,选矿中欲使90%以上的镍矿物获得解离,以选择-0.026 mm的磨矿细度较为适宜,此时-600目部分约占95%左右。与磁铁矿嵌连关系密切的紫硫镍矿占其总量的95%以上,选矿过程中要提高镍的回收率,必须尽可能多地回收磁铁矿。矿石中脉石矿物主要为蛇纹石与绿泥石,易泥化,减少和消除脉石矿物对选别的影响至关重要。     

高速铁路路基动土压力测试信号的小波分析

阐述了小波分析的基本原理与方法,选用Daubechies小波对某高速铁路路基土压力的现场振动测试信号进行分析处理。由此对高速列车荷载作用下,路基动土压力产生的机理及其土压力的构成进行较深入的研究。     

高等级公路级配碎石柔性基层应用研究

通过室内试验,铺筑试验路,提出了2种级配碎石柔性基层路面结构和1种级配碎石级配,研究了级配碎石基层的施工,可供级配碎石柔性基层设计及施工参考。     

基于层次分析法的路段属性求权方法

最优路径搜索功能是车载导航系统的路径规划模块的核心功能之一。车辆在道路路段上的旅行支出是综合考虑道路的各种属性,通过加权、无量纲化等计算后的结果。利用层次分析法对道路的各种属性加权,给出了不同类型属性的无量纲化的标准函数。     

城市道路网的度相关性研究

目的 研究了基于stroke相交关系表示的道路网对偶图中的节点度的相关性。运用同配性系数,对美国40个城市道路网进行了实验,发现这些道路网既是同配的,又有异配的。同配性道路网具有易流通性和抗破坏性,这一点在城市规划领域具有潜在应用。本研究与前人的研究结论不同,对可能的原因进行了讨论。     

浅变质岩区非整体变形和滑动构造初探

在分析了陕西旬北浅变质沉积岩区、甘肃白银厂浅变质火山-沉积岩区的构造变形特征的基础上,得出了构造变形非整体性的认识,进而提出构造变形单元体的概念和划分依据,探讨了与非整体构造变形紧密相关的滑动构造的野外特征和鉴别方法.     

基于BJ-RUC模式预报产品的北京冬半年道面温度预报模型

选用2012年11月1日至2013年3月30日3 km分辨率BJ-RUC模式输出的气象要素与5个道面站数据(A1027,A1325,A1412,A1414,A1512)温度进行统计分析,按不同起报时次(08、14和05时)分别建立三类逐步回归统计模型预报未来24 h逐时道面温度,选出最优模型预报2013年11月至2014年3月道面温度。结果表明:道面温度与RUC输出的2 m温度、短波辐射显著相关,与长波辐射、湿度次相关;有显著气象因子参与的回归模型预报的道面温度好于仅加入前一天对应时刻道面温度的回归模型,预报准确度可提高25%以上,误差减少1℃以上;滚动筛选不同起报时次预报时段可将模型预报误差控制在±3℃以内,且预报早高峰温度好于晚高峰,白天好于夜间,晴天好于其他天气类型。     

GPS高程测量代替等级水准测量的应用研究

基于近年来我国发展迅速的CORS系统和cm级似大地水准面精化模型,进行了GPS高程应用研究,从理论上分析并推导了采用低等级GPS高程测量代替三、四等水准测量的技术指标,同时利用工程实践进行了应用验证。     

Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.