盾构大数据预处理方法研究

在盾构法隧道施工过程中,合理设置盾构机运行参数,确保盾构机能够按照一定速度掘进,是整个工程流程控制中非常重要的工作。针对现有的盾构参数预测方法缺少对数据系统性的分析和处理,导致预测效果不及预期的情况,文章提出了一整套新的数据预处理流程。该流程分为数据分析和数据处理两个阶段,第一阶段通过主成分分析、皮尔森相关性系数分析进行特征筛选,第二阶段通过插值平滑、卷积平滑进行数据平滑。经过这两个阶段的原始数据处理后,能够在盾构产生的海量数据中提取出特征更明显、价值更高的数据。通过选取真实盾构机数据集进行对比实验,结果表明,所提出的数据预处理流程,能够有效提高盾构机参数预测模型的准确率。     

列控中心驱采不一致分析及改进方案

杭深线厦深段开通初期,经常出现列控中心驱采不一致报警,影响信号设备可靠运用。本文针对LKD2-HS型列控中心驱采不一致的报警.对列控中心驱采过程和报警代码进行分析,明确采集不良信息点的位置;并针对点灯切换时继电器前后接点产生“拉弧”,及列控采集电路可靠性不高问题进行了分析,提出了相应改进方案及措施。     

基于高程监测的智能调高支座设计与试验研究

高速铁路对线路的平顺性要求非常高。然而,处于自然界中的高铁桥梁结构受温度、混凝土收缩徐变和地基不均匀沉降等影响,不可避免地会发生沉降变化,直接影响到行车安全性和舒适性。针对高速铁路运营过程中产生的问题,对目前已有的调高技术进行全面分析,就目前调高支座只能单向调高和作业效率低下等问题,采用结构设计、试验验证及样品试制等方法,研发一种智能调高支座,详细介绍各系统的组成构件、功能以及安装维护要求,并对支座基本性能、自动调高功能、多台联动性能及安全可靠性能等进行试验。研究结果表明:该智能调高支座各项指标均达到了预期目标值,有效解决了现有调高支座被动、手工的操作模式。     

Implications of driving style and road grade for accurate vehicle activity data and emissions estimates

Real-world vehicle operating mode data (2.5 million 1 Hz records), collected by instrumenting the vehicles of 82 volunteer drivers with OBD datalogger and GPS while they drove their routine travel routes, were analyzed to quantify vehicle emissions estimate errors due to road grade and driving style in rural, hilly Vermont. Data were collected in winter and summer for MY 1996 and newer passenger cars and trucks only. EPA MOVES2010b was used to estimate running exhaust emissions associated with measured vehicle activity. Changes in vehicle specific power (VSP) and MOVES operating mode (OpMode) due to proper accounting for real-world road grade indicated emission rate errors between 10% and 48%, depending on pollutant, chiefly because grade-related changes in VSP could shift activity by as many as six OpModes, depending on road type. The correct MOVES OpMode assignment was made only 33–55% of the time when road grade was not included in the VSP calculation. Driving style of individual drivers was difficult to assess due to unknown traffic operations data, but the largest differences between individual drivers were observed on rural restricted roads, where traffic conditions and control have minimal impact. The results suggest the importance of (1) measuring and incorporating real-world road grade in order to correctly assign MOVES emission rates; and (2) developing a driving style typology to account for differences in the MOVES emissions estimates due to driver variability.     

盾构隧道穿越高强度强富水灰岩上浮段工程风险分析及应对措施

针对盾构穿越土岩交界面、高强度富水灰岩段及灰岩岩溶区所存在的风险进行分析,合理做好盾构选型,通过工程类比法并结合工程实际提出相应处理措施。采取全土压掘进模式、合理的掘进参数及渣土改良等手段控制土岩交界面上方管线及地表变形;穿越高强度强富水灰岩段,为防止掘进过程中产生"喷涌"现象,采取盾构半敞开式掘进模式、洞内补偿注浆封堵后方来水、地表注射聚氨酯和渣土改良等措施;针对高强度硬岩,通过合理选用刀具、控制盾构掘进参数减少刀具的磨损和破坏。穿越石灰岩岩溶区,针对溶洞的大小、与隧道相对位置关系和充填类型采取地面袖阀管注浆预处理,并结合管片预留孔进行注浆处理,通过采取上述综合技术措施,保证了盾构机在高强度富水灰岩上浮段的顺利掘进。     

Comparing the self-assessed and examiner-assessed driving skills of Japanese driving school students

Previous research about subjective driving skills has revealed that drivers, especially younger males, tend to rate their driving skills more highly than those of the average driver. This study examines the accuracy of Japanese novice drivers' self-evaluation of their driving skills by comparing their self-assessments with assessments made by a driving examiner. We also examined the effects of gender and age on the accuracy of driver self-evaluation of their skills.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Commuters’ exposure to PM2.5 and CO2 in metro carriages of Shanghai metro system

Air quality inside transportation carriages has become a public concern. A comprehensive measurement campaign was conducted to examine the commuters’ exposure to PM_2.5 (d_p ⩽ 2.5 μm) and CO₂ in Shanghai metro system under different conditions. The PM_2.5 and CO₂ concentrations inside all the measured metro lines were observed at 84 ± 42 μg/m³ and 1253.1 ± 449.1 ppm, respectively. The factors that determine the in-carriage PM_2.5 and CO₂ concentrations were quantitatively investigated. The metro in-carriage PM_2.5 concentrations were significantly affected by the ventilation systems, out-carriage PM_2.5 concentrations and the passenger numbers. The largest in-carriage PM_2.5 and CO₂ concentrations were observed at 132 μg/m³ and 1855.0 ppm inside the carriages equipped with the oldest ventilation systems. The average PM_2.5 and CO₂ concentrations increased by 24.14% and 9.93% as the metro was driven from underground to overground. The average in-carriage PM_2.5 concentrations increased by 17.19% and CO₂ concentration decreased by 16.97% as the metro was driven from urban to the suburban area. It was found that PM_2.5 concentration is proportional to the on-board passenger number at a ratio of 0.4 μg/m³·passenger. A mass-balance model was developed to estimate the in-carriage PM_2.5 concentration under different driving conditions.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Personalised assistance for fuel-efficient driving

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.