扁平大跨度公路隧道松动荷载计算方法探讨

确定松动荷载的方法多为规范法和理论公式法,这些方法都建立在对以往隧道松动荷载统计和分析的基础上。双洞八车道扁平大跨度公路隧道是近几年发展起来的一种隧道形式。由于其跨度增加、扁平率降低,现有松动荷载的算方法是否适用于双洞八车道公路隧道,已成为隧道建设者面对的重要问题之一。以广州龙头山双洞八车道公路隧道为研究背景,概括和总结了各种松动荷载的计算方法;应用这些方法计算龙头山隧道的松动压力,分析和对比计算结果,并通过数值计算和实测围岩压力对各种方法的合理性进行了分析;根据各种方法的产生背景和内容,分析其各自的适用性;根据研究结果,对扁平大跨度公路隧道进行了适应性分析和讨论。     

季冻区土质路堑边坡浅层含水率变化研究

采用现场实测和室内试验相结合的方法,研究季节性气温变化、边坡深度、坡面植物种类和坡面朝向等因素对边坡土体含水率变化的影响规律。结果证明：边坡土体冻结过程中,水分向冻结区迁移;随着冻结深度增加,含水率的变化也会向深处发展;坡面木本植物比草本植物有更明显的吸水作用;边坡含水率和土体温度的变化影响土体的强度,边坡土体温度在0℃以上时,土体强度主要受含水率变化的影响,当土体温度在0℃以下时,强度主要受温度变化的影响;三轴试验证明：边坡土体的黏聚力首先随着含水率的增加而增大,含水率达到15 %以后,边坡土体的黏聚力随含水率的增大而减小;边坡土体的内摩擦角基本上是随着含水率的增大而减小。研究成果可为季节性冻土地区高等级公路路堑边坡冻融失稳的预警提供判据。     

人工神经网络在爆破块度预测中的应用研究

利用人工神经网络模型对爆破块度进行预测,实验结果表明,该方法是完全可行的。通过对实验样本数据进行归一化处理后再对人工神经网络模型进行训练和预测,其预测精度会得到大大提高。     

利用多目标地球化学数据识别第四纪沉积物类型——基于概率神经网络方法

针对利用多目标地球化学数据研究第四纪沉积物类型问题,提出了基于概率神经网络的分类识别模型,并给出地球化学特征指标选取、指标归一化、神经网络设置和训练的具体方法、步骤。在吉林省中西部松嫩平原应用表明,该方法识别出8类不同成因的第四纪沉积物,较好地解决了该区第四纪沉积物成因归属问题。概率神经网络模型对第四纪沉积物类型的识别能力远高于常规多元统计方法,且结构简单、训练快捷。     

南平市公路运输危险化学品污染事件的研究

对2004-2007年在福建省南平市行政区域内发生的公路运输危险品污染事件进行统计分析,研究结果表明：南平市4年间共发生26起公路运输危险化学品事故,每年平均6．5起．其中夜间发生事故的比率为57．7％,夏季发生事故的比率为34．6％,35～45岁驾驶员年龄段事故发生率高达59．1％,驾龄在11～15年的为事故多发驾龄段,其发生率为45．5％,发生在弯道的事故占61．5％,超载发生事故的可能性为44．0％,占总数65．4％的事故对环境产生污染．205国道南平段发生危险化学品运输事故的概率为0．0124次／（a·km）,发生危险化学品泄漏事件的可能性为0．0142次／（a·km）,发生危险化学品1t以上大规模连续泄漏事件的概率为0．0098次／（a·km）．     

城市复杂道路网的Stroke生成方法

传统连接stroke的方法在处理表达较为详细的城市道路网数据时,会产生双行道分离和环岛处截断等错误.为能够在城市复杂道路网中正确生成stroke,针对道路网中的双行道和道路交叉口模式提出一种新的stroke生成方法.该方法采用道路街区几何形态分析、基于连通关系的层次聚类等算法自动识别出道路网中的双行道和道路交叉口模式,并使用穷举stroke配对组合算法连接截断的stroke,从而保证stroke的连续性.以武汉市NAVIN-FOTM导航电子地图数据为例验证该方法的实用性,生成的stroke符合stroke感知归组原则中平滑连贯的要求.     

Ethnography, space and politics: interrogating the process of protest in the Tibetan Freedom Movement

This paper examines the ability of ethnographic research methods to effectively study spatially extensive political activity. It argues that traditional ethnographic methods of sustained engagement with spatially bounded sites are not adequately suited to dealing with contemporary spatially extensive political movements. It argues that contemporary attempts to bridge this impasse have emphasised a dichotomy between global and local that ignores the connections, disconnections and process that occur between places. The paper argues for a critical ethnography that is based on a relational understanding of space emphasising Gillian Hart's conception of the interconnected nature of the 'site'. Taking a single demonstration against the Beijing 2008 Olympics conducted by UK-based Tibet supporters, it examines how the site of protest was closely linked to a variety of other places. Interrogating the processes of connection and disconnection brought about through the protest creates an ethnographic account that, while partial, develops an engagement with the heterogeneity of contemporary political action.     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.     

Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting

The accuracy of the wavelet regression (WR) model in monthly streamflow forecasting is investigated in the study. The WR model is improved combining the two methods—the discrete wavelet transform (DWT) model and the linear regression (LR) model—for 1‐month‐ahead streamflow forecasting. In the first part of the study, the results of the WR model are compared with those of the single LR model. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in Eastern Black Sea region of Turkey are used in the study. The comparison results reveal that the WR model could increase the forecast accuracy of the LR model. In the second part of the study, the accuracy of the WR model is compared with those of the artificial neural networks (ANN) and auto‐regressive (AR) models. On the basis of the results, the WR is found to be better than the ANN and AR models in monthly streamflow forecasting. Copyright © 2009 John Wiley & Sons, Ltd.