暗挖隧道装配式管片拼装技术研究

为解决目前暗挖隧道二次衬砌工序繁杂、施工进度慢、成形质量差等问题,结合盾构矿山法施工工艺及土压平衡盾构管片拼装原理,阐述暗挖隧道装配式管片拼装在施工中需重点解决的关键技术,并介绍配套的拼装设备结构组成及功能特点。     

基于BIM的公路隧道运维管理系统设计与开发

近年来,我国公路隧道建设里程快速增长,但在隧道运维管理中存在技术落后、管理人员配置不足等问题。针对管理中存在的问题,本文提出将BIM(building information modeling,建筑信息模型)引入公路隧道的运维管理,通过制定具体实施规划及设计系统架构,实现可视化的公路隧道运维管理。以浙江省41省道岭下隧道工程为依托,建立基于BIM的公路隧道运维管理平台,为公路隧道运维管理提供一种直观、便捷的管理方式,提高公路隧道的信息化管理水平。     

南昌红谷沉管隧道短管节干坞内拉合对接施工技术

沉管隧道最终接头短管节在牵移和拉合过程中易发生偏移与倾斜,且GINA止水带压缩到设计值难度较大。以南昌红谷沉管隧道为例,从准备、牵移、拉合及后处理4个阶段介绍短管节对接拉合施工工艺。通过对底钢板打磨、涂抹黄油等,减小底部摩擦阻力。采用计算机控制液压同步提升系统、液压数据量测、钢端壳间距测量等措施,对短管节轴线及平面控制。实践表明:GINA止水带压缩量误差仅为+4 mm,短管节轴线及平面偏差均在可控范围内,短管节干坞内拉合对接操作可行、质量可靠。     

我国城市防洪排涝地下深隧规划设计与施工方法

为了建设符合中国国情的地下排水深隧,解决城市内涝问题,消除"城市看海"现象,采用调研分析的研究方法,通过调研国外城市排水深隧建设的成功案例,借鉴成熟经验并结合我国国情探索出我国城市排水深隧在规划设计、施工技术等方面的建设模式。主要结论如下:1)深隧规划必须坚持雨污分流原则,防涝标准应设为百年一遇;2)深隧层位应建于地下30~50 m的位置,隧道管线应布设在城市易涝区,并且将修建深隧、改造浅管和清淤河道衔接起来,形成一套完整的防洪排涝系统,让深隧的作用得以充分发挥,才能解决城市内涝问题;3)排水深隧要注重防水设计,防止雨水污染地下水;4)为提高深隧的经济效益,应将深隧与地下快速路的规划结合起来,功能上多样化。     

城市地下道路分(合)流匝道通风阻力特性

为分析多点进出结构的城市地下道路空气流动特性,以长沙市营盘路湘江隧道为原型,通过现场实测、缩尺模型试验以及CFD软件数值模拟方法,对分(合)匝道通风阻力特性进行了研究,考察了雷诺数、风量比、分岔角度对分(合)流匝道阻力特性的影响规律。研究结果表明:1)主隧道与匝道风量比、主隧道与匝道夹角是影响分(合)流匝道局部阻力系数的关键因素;2)城市地下道路分(合)流匝道局部阻力变化特性,不宜简单套用通风管道的三通构件的参数;3)结合最小二乘法和Matlab软件对计算结果进行多因素回归分析,给出了基于长沙营盘路湘江隧道的分(合)流匝道主隧道和匝道的局部阻力系数关联式。研究结果可为复杂结构城市地下道路通风系统阻力特性分析及通风工程优化设计提供方法参考。     

“中—尼—印铁路通道”线路设计及建设可行性分析

随着我国"一带一路"倡议的推进,跨国运输通道的建设成为倡议实施的关键环节之一,而我国建设复杂地质条件下跨国基础设施的经验尚不丰富。"中—尼—印铁路通道"是一条途经尼泊尔,连接中国和印度2个大国的运输通道,基于实地考察,分析该通道建设的必要性及建成后的效益,对线路进行初步规划,并总结该通道建设的难点:铁路轨道坡度大;沿线区域地质构造复杂;周边基础设施落后,施工条件恶劣;大量深长隧道以及大跨径高桥梁;环境以及气候条件复杂。同时对沿线隧道以及桥梁的建设可行性进行分析,并给出施工建议:沿线隧道采用以TBM法为主、钻爆法为辅的施工方法;桥梁建设则因地制宜,根据不同区间的地质特点,采用相应的建设方法。     

拱架式预切槽施工机械在黄土隧道施工中的应用

隧道预切槽施工技术在国外应用比较广泛,但在我国应用还较少。针对黄土地质专项研制了国内首台拱架式预切槽机,并在黄土隧道进行了施工试验。通过施工试验,掌握设备定位、分区切灌、设备行走、施工作业组织等关键技术,并提出应用改进及优化措施。试验结果表明切槽系统工作状态和设备切削、喷灌功能满足设计及施工试验要求,为预切槽同步切削喷灌一体化研究提供了数据支撑和研究方向。     

Modelling,simulation and applications of longitudinal train dynamics

ABSTRACT

Significant developments in longitudinal train simulation and an overview of the approaches to train models and modelling vehicle force inputs are firstly presented. The most important modelling task, that of the wagon connection, consisting of energy absorption devices such as draft gears and buffers, draw gear stiffness, coupler slack and structural stiffness is then presented. Detailed attention is given to the modelling approaches for friction wedge damped and polymer draft gears. A significant issue in longitudinal train dynamics is the modelling and calculation of the input forces – the co-dimensional problem. The need to push traction performances higher has led to research and improvement in the accuracy of traction modelling which is discussed. A co-simulation method that combines longitudinal train simulation, locomotive traction control and locomotive vehicle dynamics is presented. The modelling of other forces, braking propulsion resistance, curve drag and grade forces are also discussed. As extensions to conventional longitudinal train dynamics, lateral forces and coupler impacts are examined in regards to interaction with wagon lateral and vertical dynamics. Various applications of longitudinal train dynamics are then presented. As an alternative to the tradition single wagon mass approach to longitudinal train dynamics, an example incorporating fully detailed wagon dynamics is presented for a crash analysis problem. Further applications of starting traction, air braking, distributed power, energy analysis and tippler operation are also presented.     

A signal-based fault detection and classification method for heavy haul wagons

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.     

State estimation in roll dynamics for commercial vehicles

Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.