Evaluating accuracy for two empirical methods in predicting settlement of drilled shafts

Several theoretical, empirical and semi-empirical methods are available in the literature to predict settlement of drilled shafts in sandy soils. In the Arabian Gulf countries, specifically in the United Arab Emirates, equations and procedure from the rest of the world are being used in analysis and design of drilled shafts without proper validation. It is the aim of this study to assess the applicability and evaluate the accuracy of two well known, and commonly used methods for pile prediction in the United Arab Emirates (UAE), namely Vesic (1977) and Poulos (1979), via comparison with data from field pile load tests conducted on shafts drilled in the region. Some of these tests were conducted for the purpose of this study, while others were made available through the courtesy of International Piling Contractors who are active in the region (e.g. Bauer International and Swiss Borings). Pile load test data were analyzed to back-calculate the model parameters related to settlement under different loading stages. Geological data and soil properties were obtained from studies conducted at the relevant sites. An effort is made to correlate soil properties with the prediction models. Statistical analysis is conducted to assess the accuracy of the results obtained from the two methods at different stages of loading via those obtained from pile load tests. Moreover, a detailed parametric study is conducted to assess the effect of the related parameters on the predicted pile settlement and the estimated settlement at different stages of loading. The study concluded with a recommendation of the most appropriate models and procedures to be followed for predicting the settlement of drilled shafts in the UAE, together with useful charts and correlation relations. Results showed that settlement values predicted by Vesic (1977) and Poulos (1979) overestimates the true values. This revised version was published online in July 2006 with corrections to the Cover Date.     

Design of backfilled thin-seam coal pillars using earth pressure theory

In underground coal mining any increase in coal recovery rate is dependent on a decrease in pillar size. Backfilling is one way of reducing the required size of pillars and hence the volume of coal left underground. Therefore any comparisons made between a self-supported mine layout and backfill supported mine layout are based directly on pillar design. The most effective way to examine the effect of backfill on pillar support, and subsequently the rate of recovery, would be to incorporate the mechanisms of backfill support directly into the current design procedure for coal pillars. This paper presents a review of the mechanics of backfill support, a method of estimating the magnitude of that support based on earth pressure theory, and an example that incorporates backfill support into current coal pillar design.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

London and Frankfurt in Europe's evolving financial centre network

The launch of the Euro and the location of the European Central Bank in Frankfurt initially was seen as a threat to London's pre-eminent position in European financial geographies. This paper explains why in fact this was not the case. To do this, the paper is divided in two. Firstly, it reviews the literatures that help to explain financial geographies. It is argued that we need to move away from investigating attribute properties such as financial turnover and instead examine the role of networks and interdependencies in producing financial geographies. Secondly, it identifies London's dominance and Frankfurt's growth as a complementary centre through quantitative analysis and then explains how European networks and interdependencies produce this, based on insights from interviews with investment bankers and insurance institution workers in the two cities.     

概率神经网络与BP网络模型在遥感图像分类中的对比研究

通过分析概率神经网络(以下称PNN)的基本结构及其训练算法,建立了卫星图像分类的概率神经网络模型,并通过实例对比分析了概率神经网络与BP网络分类模型的分类效果。实验表明,PNN图像分类方法在分类精度上优于误差反向传播神经网络模型,且分类时间相当,是一种有效的图像分类方法。     

我国国家大地网的地壳形变改正探讨

以2000网为例，根据1991～2001年累积的GPS重复观测资料，利用GIPSY软件建立具有1115个测点的地壳运动模型。在此基础上，采用双三次样条函数数值拟合法，模拟出2000网网点相对西安基准站的地壳形变改正值，并给出了效果图。     

基于三角网渐进式简化的等高线多尺度综合

提出了一种基于三角网渐进式简化的等高线多尺度综合方法,实验结果表明,其不仅可以较好地保留地形特征,而且可以完全避免等高线相交现象的产生.     

GPS高程转换的神经元网络方法分析

GPS高程转换是GPS高程应用的关键问题之一，试图应用神经元网络方法来进行GPS高程的转换，并在网络的拓扑结构与网络结构的改进等方面作了分析，通过GPS高程转换的二次曲面法和神经元网络法进行了比较试验，结果表明了神经元网络转换GPS高程的可行性和可靠性，对于应用神经网络转换GPS高程具有实际的指导意义。     

基于图论的树状河系结构化绘制模型研究

结合树状河系自身的结构特点和图论的思想，提出了基于图论的河系结构化绘制模型的建立，利用图论的思想描述了河系的结构并建立其自动符号化模型，阐述了主流、流向自动确定和结构化绘制实现的详细算法，并对通用图形数据格式DXF文件记录的河系数据进行了测试。     

Black Holes and Loose Connections in a Global Urban Network

This paper introduces the concept of black holes and loose connections in a global urban hierarchy. Black holes are defined as large cities, with a population of over 3 million, that are not classified as world cities. The paper draws upon a classification that uses advanced producer services as an indicator of world city status. Large, nonworld cities are identified, and provisional ideas about explaining their position are outlined. Connectivity and population data are used in a simple regression analysis to identify loose connections: cities whose connectivity is less than that predicted by their population.