深圳地铁2号线轨道减振降噪技术的应用

本文对深圳地铁2号线轨道减振降噪技术的应用情况进行了详细的介绍。其中,对国内地铁无碴轨道上首次试铺应用的隔离式减振垫轨道技术的减振原理、设计方案、施工方法及减振效果实测等进行了分析和探讨。通过实测,隔离式减振垫轨道结构的减振效果满足设计理论分析的要求。     

城市轨交减振轨道结构技术经济分析

采用减振轨道结构是有效控制城市轨交振动和噪声的重要途径。概述了国内城市轨交常见的减振轨道结构类型,分析了影响轨道结构造价的几个因素。采用费用分解的方式计算特定工程条件下各类减振轨道结构的综合造价指标,对减振轨道结构的技术经济进行分析比较,提出在工程前期阶段明确落实轨道减振措施对工程投资控制的重要性。     

小半径曲线下聚氨酯减振垫浮置板轨道减振性能的测试分析

目前,对于减振垫浮置板轨道的动力性能评价,主要选取直线段轨道进行研究.对于曲线段(尤其是小半径曲线段)减振垫浮置板轨道的减振性能鲜有涉及.本文以深圳地铁小半径(R=450 m)曲线段聚氨酯减振垫浮置板轨道为例,对聚氨酯减振垫浮置板轨道和普通整体道床进行了现场振动测试,在时域和频域内分析了隧道壁垂向振动加速度.结果表明:在小半径曲线段,聚氨酯减振垫浮置板轨道的减振效果仍旧明显.较于普通整体道床,聚氨酯减振垫浮置板轨道减振频段主要为4～8 Hz以及25～200 Hz,且在25～200 Hz频段内更为显著,减振量值范围为3.2～23.9 dB.但是,钢轨垂向最大位移约为普通整体道床的2倍,这主要由于降低轨下支撑刚度所致.     

轨道结构新技术在上海轨道交通2号线西延伸段工程中的应用

介绍上海轨道交通2号线西延伸段轨道工程设计中在钢轨扣件、道岔、道床、轨道减振降噪、附属设施,以及在无缝轨道施工等方面开发和应用的一系列新技术、新工艺和新设备,并取得了少维修和减振降噪的实际效果,可为同类轨道工程应用提供参考。     

深圳地铁塘朗车辆段上盖物业开发轨道减振降噪措施研究

针对深圳地铁5号线塘朗车辆段上盖物业开发的减振降噪问题，根据车辆段轨道振动和噪声源、以及振动和噪声值的预测成果，对各种减振与降噪措施进行综合比较，因地制宜地提出车辆段轨道的减振降噪措施，使列车运行的振动和噪声对上盖物业的影响达到环保要求，可供类似工程参考和借鉴。     

双块式无砟轨道路桥过渡段处温度力上拱整治技术研究

以某高速铁路路桥结合部双块式无砟轨道道床板上拱病害为研究对象,根据现场调研情况分析病害原因,提出开挖路基换填混凝土的整治方案,理论计算结果表明,整治技术可有效限制道床板上拱病害。经实践验证,该方案整治效果良好,形成的技术经验可为我国高速铁路双块式无砟轨道类似病害的养护维修提供参考。     

城市轨道交通特大跨钢桥轨道结构选型分析

针对城市轨道交通特大跨钢桥轨道结构选型问题,结合国内外特大跨钢桥轨道应用情况,分析了特大跨钢桥对轨道结构的特殊要求,并对有砟轨道和无砟轨道应用于特大跨钢桥上的适用性进行比较。以重庆环线鹅公岩大桥轨道专用桥为例,分析了有砟轨道和减振垫浮置板无砟轨道在钢桥上应用的影响因素。通过比较得出特大跨钢桥适宜采用无砟轨道型式的结论。     

隔离式电能变换器的电流采样电路

权利要求 1.隔离式电能变换器的电流采样电路,包括隔离式电能变换器,所述隔离式电能变换器至少包括一隔离变压器,所述隔离变压器的原边绕组并联一原边电路,所述隔离变压器的副边绕组并联一副边电路,其特征在于,所述电流采样电路包括耦合电感、辅助绕组和采样电容,所述耦合电感的原边绕组串联于所述原边电路和所述隔离变压器的原边绕组之间,所述耦合电感的副边绕组经过一第一电阻与所述采样电容并联,所述辅助绕组与所述隔离变压器的副边耦接,所述辅助绕组经过一第二电阻与所述采样电容并联.     

输油管道动火作业油气隔离技术研究

文中介绍了输油管道动火作业安全隔离措施,并对动火隔离措施进行对比分析,分析施工方法、适用工况、优缺点等。针对目前管道动火隔离方法的优缺点,提出止推器加橡胶球隔离法,通过橡胶球和止推器进行两道密封隔离,隔离结束通油后止推器带橡胶球到收球筒内进行回收。     

中船重工725所首创可更换式轨交减振道床系统

2013年6月9日，由国家千人计划专家、中船重工725所王安斌博士负责的“轨交高性能减振道床系统关键技术研究特高端项目”在河南洛阳通过了技术鉴定。专家一致认为，该项目打破国外技术垄断，属世界首创，其减振降噪水平国际领先。同日，该课题中的高等减振项目谐振式浮轨扣件也通过了成果鉴定。     

Non-exhaust particle emissions under various driving conditions: Implications for sustainable mobility

Non-exhaust particle (NEP) emissions from road traffic contribute significantly to Particulate Matter (PM) pollution in urban areas. The primary objective herein is to develop the knowledge required to move toward more sustainable mobility. NEP emissions are studied by means of complementary experiments on chassis dynamometers, on test tracks and at the roadside. Laboratory tests demonstrate that brake wear particles (BWP) emissions can change with braking force and frequency. A brake pad temperature threshold exists, above which the rate of ultrafine particle emissions is quite high. Below this threshold, the BWP emissions are dominant in the accumulation and coarse modes. Test track measurements have demonstrated that tire-road contact particle (TRCP) emissions considerably modify the atmospheric PM background especially for the supermicron fraction. Their number size distribution highlighted an ultrafine and accumulation modes centered at about 40 nm and 200 nm, respectively. The TRCP level increases with vehicle speed and during the acceleration and deceleration phases. Roadside measurements in the urban environment confirm the presence of NEP in significant proportions, in both the accumulation and coarse modes. The chemical composition of NEP differs depending on the size mode: BWP mainly stem from the degradation of brake pad lining materials, while TRCP are a mixture of tire tread wear and re-suspended dust. The presence of Fe-rich particles nevertheless serves as a good indicator of the NEP contribution to PM at the roadside. Lastly, in considering the parameters influencing NEP emissions, a series of recommendations are offered in order to achieve a more sustainable mobility.     

Field investigation and parametric study of greenhouse gas emissions from railway plain-line renewals

Railway transportation is becoming increasingly important in many parts of the world for mass transport of passengers and freight. This study was prompted by the industry’s need to systemically estimate greenhouse gas emissions from railway construction and maintenance activities. In this paper, the emphasis is placed on plain-line railway maintenance and renewal projects. The objective of this study was to reduce the uncertainties and assumptions of previous studies based on ballasted track maintenance and renewal projects. A field-based data collection was carried out on plain-line ballasted track renewals. The results reveal that the emissions from the materials contribute more than nine times the CO₂-e emissions than the machines used in the renewal projects. The results show that extending the lifespan of rail infrastructure assets through maintenance is beneficial in terms of reducing CO₂-e emissions. Analysis was then carried out using the field data. Then the results were compared to two ballastless track alternatives. The results show that CO₂-e emissions per metre from ballasted track were the least overall, however, the maintenance CO₂-e emissions are greater than those of ballastless tracks over the infrastructure lifespan, with ballasted track maintenance emitting more CO₂-e emissions at the 30 and 60 year intervals and the end of life when compared to the ballastless track types. The outcome of the study can provide decision makers, construction schedulers, environmental planners and project planners with reasonably accurate GHG emission estimates that can be used to plan, forecast and reduce emissions for plain-line renewal projects.     

Train scheduling simulation that minimises operational conflicts due to service constraints

This paper presents an attempt made to facilitate re‐scheduling of trains to minimize operational delays and accommodate uniform headways for off peak sub urban services subject to resource constraints such as locomotive availability, poor track conditions and stations without siding facilities. The paper describes the computer simulation model designed to optimize train schedules on single‐track rail lines. Using this simulation program it is possible to plan and optimize timetables for railway networks with train runs within short time periods for both single track and double track conditions. The paper describes the capabilities of presenting the results of the simulation runs. These include the time‐distance graph, the network with train movements, dialog boxes with information about selected trains. The programme is capable of changing the starting point, departure time, train destinations and adding or deleting a stop etc. from the user interface. Four objects of array variables are used in the simulation process to keep train and station data. Two object arrays are used for the train movements in up and down directions. The stations' data are stored in the other two object arrays. One of these arrays of stations contains all the stations of the line while the other one contains only the stations with siding facilities. A case study that covers a 61 km long single‐track line with 14 stations is presented to highlight the model capabilities.     

Principal components analysis and track quality index: A machine learning approach

Track geometry data exhibits classical big data attributes: value, volume, velocity, veracity and variety. Track Quality Indices-TQI are used to obtain average-based assessment of track segments and schedule track maintenance. TQI is expressed in terms of track parameters like gage, cross-level, etc. Though each of these parameters is objectively important but understanding what they collectively convey for a given track segment often becomes challenging. Several railways including passenger and freight have developed single indices that combines different track parameters to assess overall track quality. Some of these railways have selected certain parameters whilst dropping others. Using track geometry data from a sample mile track, we demonstrate how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led us to principal components. To validate the use of principal components as TQI, we employed a two-phase approach. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance.     

Evaluation of CO2 emissions from railway resurfacing maintenance activities

This paper is the world first to investigate the CO₂ impact of railway resurfacing in ballasted track bed maintenance. Railway resurfacing is an important routine maintenance activity that restores track geometry to ensure safety, reliability and utility of the asset. This study consisted of an extensive field data collection from resurfacing machineries (diesel-engine tamping machines, ballast regulators and ballast stabilisers) including travel distances, working distances, fuel consumption and construction methodologies. Fuel consumption was converted to a kg CO₂/m using the embodied energies of diesel. Analyses showed that tamping machines emitted the highest CO₂ emissions of the resurfacing machineries, followed by ballast regulators and ballast stabilisers respectively. Tamping machines processed 4.25 m of track per litre of diesel, ballast regulators processed 6.51 m of track per litre of diesel and ballast stabilisers processed 10.61 m of track per litre of diesel. The results were then compared to previous studies and a rigorous parametric study was carried out to consider long-term resurfacing CO₂ emissions on Australian railway track. The outcome of this study is unprecedented and it enables track engineers and construction managers to critically plan strategic rail maintenance and to develop environmental-friendly policies for track geometry and alignment restoration.     

圆柱壳开孔补强的接触应力分析

在压力容器开孔补强的薄壳理论分析中,通常假设在补强圈与容器壳体之间没有接触,接触应力对于结构应力分布的意义和影响尚不清楚.应用ANSYS软件对内压作用下补强圈与壳体间接触行为进行有限元分析.考察了接触变形和接触压力,探讨了补强圈与壳体间隙变化、刚度因子以及不同直径的接管对接触行为的影响.相对于开孔补强的薄壳理论,文中更好地预测了补强圈与容器壳体间的应力场分布,得到了一些有参考价值的结论.     

Data-driven optimization of railway maintenance for track geometry

Railway big data technologies are transforming the existing track inspection and maintenance policy deployed for railroads in North America. This paper develops a data-driven condition-based policy for the inspection and maintenance of track geometry. Both preventive maintenance and spot corrective maintenance are taken into account in the investigation of a 33-month inspection dataset that contains a variety of geometry measurements for every foot of track. First, this study separates the data based on the time interval of the inspection run, calculates the aggregate track quality index (TQI) for each track section, and predicts the track spot geo-defect occurrence probability using random forests. Then, a Markov chain is built to model aggregated track deterioration, and the spot geo-defects are modeled by a Bernoulli process. Finally, a Markov decision process (MDP) is developed for track maintenance decision making, and it is optimized by using a value iteration algorithm. Compared with the existing maintenance policy using Markov chain Monte Carlo (MCMC) simulation, the maintenance policy developed in this paper results in an approximately 10% savings in the total maintenance costs for every 1 mile of track.     

隧道爆破减震技术措施研究

文章以重庆市轨道交通三号线一期工程华新街-观音桥区间隧道爆破开挖工程为例,从爆破振动效应的产生及其影响因素的理论分析入手,结合工程实践中爆破振动效应实际测试结果,对隧道爆破减震问题进行系统的综合研究,得出一系列相应的隧道爆破施工减震技术措施。     

Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.