巷道围岩动态工程分类技术研究

基于围岩质量计算、柱状图数字化和重组、结构面诊断、围岩结构装配、采动应力作用后围岩结构演化等研究成果,采用工程要求、基本结构、演化结构为一级指标,提出了围岩的动态工程分类技术、分类结果的表式表达技术,并指出了分类结果的应用方法。     

三峡库区低等级土质道路侵蚀研究

以三峡库区界垭小流域内不同交通荷载土质道路为研究对象,通过自然降雨观测研究其侵蚀规律和污染物流失特征。研究结果表明,交通荷载大的干道产流率和侵蚀率均高于交通荷载低的支道,对产流率影响显著的因子主要为降雨量,对产沙率影响显著的因子为降雨量、雨强。雨型对污染物的流失特征影响显著,前期集中型降雨下,污染物流失过程线与降雨过程线同步性较好,泥沙、总氮、铵态氮、泥沙结合态磷和溶解态磷均发生了较强的初始冲刷效应;中期集中型降雨下,污染物流失浓度峰值优先于雨强峰值,且氮和磷等初始冲刷强度低于前期集中型降雨,泥沙无初始冲刷效应。     

Artificial neural network study of observed pattern of scour depth around bridge piers

An artificial neural networks (ANN) model is developed to study the observed pattern of local scour at bridge piers using an FHWA (Federal Highway Administration) data set composed of 380 measurements at 56 bridges in 13 states. Various ANN estimates of observed pier scour depth on different choices of input variables are examined. Reducing the number of variables from 14 to 9 has negligible effect on the coefficient of determination, R², (0.73 vs. 0.72). Further sensitivity analysis indicates that pier scour depth can be estimated using only four variables: pier shape and skew, flow depth and velocity with a coefficient of determination of 0.81, suggesting that inclusion of some variables actually diminishes the quality of ANN predictions of short term observed pattern of scour. The ANN estimates indicate that flow depth and flow velocity make up 66% of the coefficient of determination.     

Studies on Construction Pre-control of a Connection Aisle Between Two Neighbouring Tunnels in Shanghai by Means of 3D FEM,Neural Networks and Fuzzy Logic

The ground freezing construction technique is one of the most effective and widely applied site construction methods in soft soil areas, like Shanghai. Some elevation-inclined refrigeration pipes are arranged for the artificial freezing excavation of the Pudong-side first-storey connection aisle, which is designed to connect two adjacent tunnel lanes of Shanghai East-Fuxing-Road tunnel project. No advanced research results could be found for computing the temperature field of tunnels and aisles frozen with inclined refrigeration pipes. Anyhow the computation of the relevant temperature field is of high importance for the safe and economical excavation of the above-mentioned aisle. In this paper, a method for computing the aisle temperature field using 3D FEM is given, and the computation accuracy is verified by contrasting the computed and site measured results. The back propagation neural networks are also applied to the temperature prediction using self-developed Neural Network-Expert System software, the predicted results are also very satisfactory. The mechanism during freezing and aisle excavation will be discussed on the basis of 3D FEM simulation. The authors believe that studying the parameter-sensitivity of temperature field is very important for the optimum selection of parameter values. So, in this paper, the parameter-sensitivity of temperature field is also discussed. In order to obtain the optimum frozen wall thickness, the relation between the frozen wall thickness and the initial freezing brine temperature is studied. At the end, an excavation pre-control plan is proposed by means of fuzzy logic theory for improving the excavation safety. The research result of the current paper is very helpful for projects that will be excavated by freezing construction technique.     

Using global node-based velocity in random walk particle tracking in variably saturated porous media: application to contaminant leaching from road constructions

Precise and efficient numerical simulation of transport processes in subsurface systems is a prerequisite for many site investigation or remediation studies. Random walk particle tracking (RWPT) methods have been introduced in the past to overcome numerical difficulties when simulating propagation processes in porous media such as advection-dominated mass transport. Crucial for the precision of RWPT methods is the accuracy of the numerically calculated ground water velocity field. In this paper, a global node-based method for velocity calculation is used, which was originally proposed by Yeh (Water Resour Res 7:1216–1225, 1981). This method is improved in three ways: (1) extension to unstructured grids, (2) significant enhancement of computational efficiency, and (3) extension to saturated (groundwater) as well as unsaturated systems (soil water). The novel RWPT method is tested with numerical benchmark examples from the literature and used in two field scale applications of contaminant transport in saturated and unsaturated ground water. To evaluate advective transport of the model, the accuracy of the velocity field is demonstrated by comparing several published results of particle pathlines or streamlines. Given the chosen test problem, the global node-based velocity estimation is found to be as accurate as the CK method (Cordes and Kinzelbach in Water Resour Res 28(11):2903–2911, 1992) but less accurate than the mixed or mixed-hybrid finite element methods for flow in highly heterogeneous media. To evaluate advective–diffusive transport, a transport problem studied by Hassan and Mohamed (J Hydrol 275(3–4):242–260, 2003) is investigated here and evaluated using different numbers of particles. The results indicate that the number of particles required for the given problem is decreased using the proposed method by about two orders of magnitude without losing accuracy of the concentration contours as compared to the published numbers.     

Moving players,traversing perspectives: Global value chains,production networks and Ghanaian football labour migration

This article examines the production and transnational export of Ghanaian football labour. It does so via a cross-disciplinary approach that utilises perspectives rooted in the sociology of development (global value chains) and economic geography (global production networks). The article is underpinned by two central arguments. Firstly, it contends that the GVC framework is useful in accounting for how Ghanaian players are produced and prepared for the international market, identifying the key agents and agencies involved, mapping the geography of production and export and assessing the institutional context within which the trade operates locally, nationally and internationally. The second draws on the GPN perspective to argue that while Ghanaian football labour migration remains a process contoured by uneven asymmetries of power that favour actors, stakeholders and entities in the global North, there are currently segments of the production–export chain where power is much more diffuse and some benefits are captured in the global South. The paper draws on interview data and observations gleaned from four periods of multi-sited ethnographic fieldwork in Ghana between January 2008 and June 2011.     

高填方残积土路堤降雨滑塌机理与治理对策

结合发生于京福高速公路南平段土地洋大桥邻近的高填方残积土路堤滑塌灾害,着重分析了高填方残积土路堤滑塌与降雨的关系。对强降雨入渗导致高填方残积土路堤滑塌机理进行了相关参数分析,结果表明,渗流主要分布在靠近原地面处,此处容易形成软弱滑动面,造成滑塌。还探讨了引起滑塌的其他潜在因素如高填方路堤填土的土质特性、压实度以及湿化变形等,并提出了整治方案,为高填方路堤的填筑设计、控制施工质量提供了依据。     

Long-range forecasting of suspended sediment

This study examines the employment of two methods, multiple linear regression (MLR) and an artificial neural network (ANN), for multistep ahead forecasting of suspended sediment. The autoregressive integrated moving average (ARIMA) model is considered for one-step ahead forecasting of sediment series in order to provide a comparison with the MLR and ANN methods. For one- and two-step ahead forecasting, the ANN model performance is superior to that of the MLR model. For longer ranges, MLR models provide better accuracy, but there is an important assumption violation. The Durbin-Watson statistics of the MLR models show a noticeable decrease from 1.3 to 0.5, indicating that the residuals are not dependent over time. The scatterplots of the three methods (MLR, ARIMA and ANN) for one-step ahead forecasting for the validation period illustrate close fits with the regression line, with the ANN configuration having a slightly higher R² value.     

Methodology for the development of bridge‐specific fragility curves

A new methodology for the development of bridge‐specific fragility curves is proposed with a view to improving the reliability of loss assessment in road networks and prioritising retrofit of the bridge stock. The key features of the proposed methodology are the explicit definition of critical limit state thresholds for individual bridge components, with consideration of the effect of varying geometry, material properties, reinforcement and loading patterns on the component capacity; the methodology also includes the quantification of uncertainty in capacity, demand and damage state definition. Advanced analysis methods and tools (nonlinear static analysis and incremental dynamic response history analysis) are used for bridge component capacity and demand estimation, while reduced sampling techniques are used for uncertainty treatment. Whereas uncertainty in both capacity and demand is estimated from nonlinear analysis of detailed inelastic models, in practical application to bridge stocks, the demand is estimated through a standard response spectrum analysis of a simplified elastic model of the bridge. The simplified methodology can be efficiently applied to a large number of bridges (with different characteristics) within a road network, by means of an ad hoc developed software involving the use of a generic (elastic) bridge model, which derives bridge‐specific fragility curves. Copyright © 2016 John Wiley & Sons, Ltd.     

地震数据正交投影分解方法的研究与应用

地震记录正交分解是值得研究的重要问题.本文将人工神经网络自组织学习算法引入到地震数据处理中,提出地震数据神经网络正交分解方法.在分解后的特征子空间中进行特征提取来消除相干和随机噪声,提高地震记录的信噪比.研究结果表明,这种实现方法是可行的,因特征滤波或数据压缩一般只需为数不多的主分量,本文的方法可根据滤波需要自适应提取所需主分量,可免去大量无用的计算,因此,本方法可降低滤波的计算成本,参与分解的道数越多效果越明显.最后,给出了对实际资料进行特征滤波的一个应用实例.