北京地铁深基坑支护结构设计优化与施工

北京地铁9号线军事博物馆站北换乘厅南侧紧邻复兴路和既有1号线,附近管线众多,周边环境复杂,对地层扰动影响较高,地层中下部以黏土岩、砾岩为主,整体稳定性较好,原设计方案采取咬合桩止水及基坑底部换撑等加强措施。在保证施工安全的前提下,为了合理缩短工期、减少造价,结合实际工程经验对原设计方案进行了优化。和原设计方案相比,优化设计方案取消了咬合桩及换撑,对第4道支撑位置进行了调整,并更换了防水材料。同时,采用北京理正深基坑分析软件对优化设计前后的基坑开挖情况进行了对比分析。最后,在按优化设计方案施工的过程中进行严密的监控量测,并对监测数据进行了分析。最终给出以下建议:1)深基坑支护尽量不采取换撑的方式;2)设计方案要结合实际情况进行动态设计,要以软件计算作为参考依据;3)在基坑开挖过程中要进行严密的监控量测,并重视数据反馈。     

基于UML的引俄潜艇技术保障信息系统分析

应用先进保障机制和信息化保障手段有机结合的方法,通过深入调查当前引俄潜艇技术保障存在的实际问题,对引俄潜艇技术保障中的主要要素进行简要分析,运用统一建模语言(UML)完成了信息系统的分析。通过系统应用提高了引俄潜艇的技术保障能力,提升了该类装备作战能力,并呈现军事经济效益。     

洋流作用下悬浮隧道动力学行为试验研究

本研究根据相似准则和试验条件设计试验模型。在试验模拟的1节悬浮隧道管段上分别布置2组和3组锚索支撑,测试在不同流速条件下,悬浮隧道管体结构的环向应变、轴向应变及张力腿锚索的轴力。根据试验结果,初步分析悬浮隧道应力的空间分布特征和张力腿的轴力情况;给出在相同环境条件下,张力腿锚索数量对结构应力的影响、洋流速度对悬浮隧道管段结构变形和张力腿轴力的影响关系等。     

《公路工程国内招标文件范本》有关条款在公路工程中的应用

《公路工程国内招标文件范本》技术规范中的计量与支付条款是业主编制工程量清单时的分项规则,是计算工程量、编制标底的依据,是投标人提供合理报价的基础,是监理工程师实施工程计量与支付、控制工程造价的重要手段。故应符合工程实际,明确计量支付范围,表述词语要严谨准确,避免产生误解和歧议,要能够促进工程的顺利实施。     

基于区内出行比例的城市交通小区半径计算方法

针对国内城市交通小区划分多靠定性分析的情况,分析了国内外交通小区划分方法,提出了基于区内出行比例的交通小区半径定量计算方法。基于交通小区半径和出行距离的三种不同组合,推导出了相应的区内出行比例计算模型,利用Excel软件的VBA编程功能,进行了三者之间的数量关系计算,通过对区内出行比例的控制,得出交通小区半径的合理范围。实例计算结果表明:当区内出行比例范围为5%～10%时,西安中心市区交通小区的合理半径在0.7～1.1km之间,而实际调查区内出行比例为9.28%,交通小区半径为0.9km,调查值与计算值接近,小区半径定量计算方法可行。     

大跨径弯桥圆心角对其内力、位移及稳定性的影响

为提高高墩大跨径弯桥的安全性,对不同圆心角的典型弯桥在考虑大变形和材料非线性情况下,利用有限元法对刚构桥的墩梁内力与位移进行计算,分析了桥跨的内力、位移和非线性稳定荷载系数与弯桥圆心角的关系.分析结果显示:最大悬臂阶段主梁根部的弯矩随曲线圆心角增大而略有减小,但扭矩会快速增大;曲线圆心角越大,悬臂端竖向、横向位移和墩顶横桥向位移越大,在圆心角大于38°,非线性已很明显,悬臂端和墩顶位移会急剧增大;非线性稳定系数约为稳定特征系数的35%,随着弯桥圆心角的增大,其稳定系数会迅速变小;综合考虑,大跨径弯桥圆心角不宜大于38°.     

A new composite decision support framework for strategic and sustainable transport appraisals

This paper concerns the development of a new decision support framework for the appraisal of transport infrastructure projects. In such appraisals there will often be a need for including both conventional transport impacts as well as criteria of a more strategic and/or sustainable character. The proposed framework is based on the use of cost-benefit analysis featuring feasibility risk assessment in combination with multi-criteria decision analysis and is supported by the concept of decision conferencing. The framework is applied for a transport related case study dealing with the complex decision problem of determining the most attractive alternative for a new fixed link between Denmark and Sweden – the so-called HH-connection. Applying the framework to the case study made it possible to address the decision problem from an economic, a strategic, and a sustainable point of view simultaneously. The outcome of the case study demonstrates the decision making framework as a valuable decision support system (DSS), and it is concluded that appraisals of transport projects can be effectively supported by the use of the DSS. Finally, perspectives of the future modelling work are given.     

船坞超高型管柱支撑设计

阐述了船坞超高型管柱支撑的设计。提出了船坞区域搭载时管子支撑与外板3种连接形式下的支撑设计允许承载力的理论计算，并对目前船坞使用的φ426mm×14mm和φ500mm×18mm两种管子进行计算分析。改进了撑管的底部支撑，提高了超高型管子支撑的使用效果和安全性。     

舰船物资转运方案计算机辅助决策算法研究

瞬息万变的现代战场对大中型水面战斗舰船甲板物资转运的效率提出越来越高的要求。引入计算机辅助决策技术,在考虑多种牵制因素的情况下,快速获得最优的物资转运方案。详细分析和阐述舰船物资转运线路规划、时间计算、作业排序以及资源配班等4个主要问题的计算机解决方法,为利用计算机辅助制定最优物资转运方案提供可行途径。     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.