超大直径土压平衡盾构在中心城区公路隧道中的应用技术探讨

在上海外滩公路隧道施工中,首次采用了直径为14.27 m的超大直径土压平衡盾构.文章以此为工程背景,围绕超大断面的开挖面稳定、浅覆土施工、近距离穿越运营中地铁和历史保护建筑群等诸多难题,从盾构的针对性设计、关键施工技术、构筑物保护措施等方面对超大直径土压平衡盾构的施工技术进行了详细的论述;分析了超大直径土压平衡盾构推进对周边环境的影响规律,并与常规的地铁盾构、同级别的超大直径泥水平衡盾构进行了比较,指出了超大直径土压平衡盾构的优劣;结合中国城市交通建设的发展趋势,对超大直径土压平衡盾构在城市中心区域的应用前景和进一步的研究方向进行了展望.     

土压平衡盾构施工中泡沫改良圆砾地层试验研究

随着我国城市地铁的讯速发展,盾构法隧道开挖得到了广泛的应用。土压平衡盾构在圆砾、卵石地层中施工出现了许多问题,如动态土压平衡难以形成、刀盘扭矩过大、刀盘和刀具磨损过快等,最有效的解决方法是土体改良。文章通过研制可以改变泡沫性能参数的新型发泡装置,全面研究泡沫的性能,为科学使用泡沫提供依据。在盾构开挖的圆砾地层中钻井取样,利用泡沫进行改良,通过塌落度试验获得泡沫对圆砾塑流性(塑性流动性)影响的实验规律,得到适合土压平衡盾构施工的最优泡沫注入比。     

土压平衡盾构电气监控系统研制技术

介绍了国产土压平衡盾构电气监控系统研究、设计和应用的经验，其目的是指导国产盾构电气监控技术的开发和应用。     

北京地铁十号线土压平衡盾构土体改良技术应用研究

结合北京地铁十号线一期盾构隧道工程,对土压平衡盾构土体改良技术的应用进行了系统的研究、总结,提出了土压平衡盾构施工的主要问题和施工中采取的主要措施和关键技术,分析了新型泥浆土体改良技术、气泡土体改良技术、泡沫和膨胀土泥浆相结合的土体改良技术等的作用机理和特点,指出了土压平衡盾构土体改良的重要性和目前的研究现状.     

φ800mm土压平衡式模型盾构试验系统的研制

文章针对自主研发的φ800 mm土压平衡式模型盾构掘进系统,详细介绍了盾构机壳体、掘削系统、螺旋出土器、推进系统和控制台5个重要部件各自的结构形式、布局特点、参数确定及具体功能等,并指出该模型盾构试验系统可根据不同的需求开展原样土体掘进试验、相似模型试验,以及盾构近接施工等复杂条件下的模拟试验;通过在原样砂土地层中的掘进试验结果,分析了地表沉降形态与发展过程及地层损失率与地层位移间的关系;运用Peck公式对地表沉降进行了计算并与试验值进行了对比,验证了该土压平衡式模型盾构掘进系统的可靠性。     

超大直径土压平衡盾构施工土体改良试验研究

盾构推进时开挖面的稳定对控制沉降起着决定性作用,因此要求作为开挖面支撑介质的土砂具有良好的塑性变形、软稠度、内摩擦角小及渗透率小等特点,但是一般土壤不能完全满足这些特性,为此需要对开挖面土体进行改良。目前土体改良技术主要是在土体内加入膨润土、泡沫等添加剂来改善土体的性能,但是该技术对于超大直径、工程环境敏感的上海外滩通道工程是否适应值得探讨。为此,以上海外滩通道工程为背景,通过室内试验、模拟试验和现场试验研究了土体改良技术对超大直径土压平衡盾构隧道的适应性问题;通过室内试验验证了泡沫和膨润土对上海典型土体的改良效果;根据模拟推进试验结果确定了添加剂加量、发泡率等参数;最后通过现场试验探讨了改良效果。通过在超大直径土压平衡盾构施工中对土体进行改良,有效地保持了开挖面的稳定,减少了盾构推力与扭矩,刀盘磨损和机械负荷也都得到了很好的改善,盾构在土体改良后出土流畅、推进匀速,从而验证了土体改良技术对超大直径盾构隧道的适用性。     

直径7650 mm土压平衡盾构计算机施工管理系统研制

上海浦江人行隧道施工采用的盾构是根据上海市市政建设需要从法国引进的二手设备,此设备代表了土压平衡盾构90年代初的设计、制造技术水平.其中计算机监控、施工管理系统是根据国内盾构隧道施工管理要求和盾构技术参数指标自行开发的,在浦江人行隧道工程中被成功应用.文章主要介绍了该系统的设计、开发和应用原理.     

上海外滩通道超大直径土压平衡盾构施工技术

针对上海外滩通道圆隧道工程的特点及施工难点，介绍了通过采取隔离桩、土体改良以及控制盾构正面土压力等一系列有效的施工技术措施，使直径14．27m土压平衡盾构顺利下穿外滩万国建筑群及外白渡桥、轨道交通2号线、南京东路人行地道和北京东路人行地道等建（构）筑物，为今后超大直径土压盾构施工提供借鉴。     

6m直径土压平衡盾构机长距离下穿浅覆土河道施工技术

结合苏州轨道交通4号线溪秀路站—溪江路站区间隧道工程盾构下穿溪江河施工实例,论证土压平衡盾构机在软土地层中长距离下穿浅覆土河道施工技术的可行性。     

土压平衡盾构长距离穿越砂性地层施工技术

土压平衡盾构长距离穿越砂性地层,往往会产生刀盘刀具磨损严重、砂土液化引起地面沉降甚至塌陷、螺旋机喷涌致使盾构掘进困难等问题。本文结合郑州轨道交通1号线一期工程紫金山站-东明路站区间隧道工程,详细介绍了盾构在砂性地层中施工所采用的各项针对性措施,分析了这些措施的实际效果,可供类似工程参考。     

主动悬架系统模式切换控制策略研究

针对在不同工况下车辆行驶时对于主动悬架系统的性能需求,本文设计出一种可切换悬架工作模式的悬架控制策略,可以提高车辆在不同工况下行驶时的动力学性能。通过对控制策略进行建模仿真,结果得出相较于未进行模式切换的悬架,所设计的模式切换控制策略可以有效改善悬架工作时的性能,为后续控制参数的优化提供了研究基础。     

Development and testing of a fully Adaptive Cruise Control system

Adaptive Cruise Control systems have been developed and introduced into the consumer market for over a decade. Among these systems, fully-adaptive ones are required to adapt their behaviour not only to traffic conditions but also to drivers’ preferences and attitudes, as well as to the way such preferences change for the same driver in different driving sessions. This would ideally lead towards a system where an on-board electronic control unit can be asked by the driver to calibrate its own parameters while he/she manually drives for a few minutes (learning mode). After calibration, the control unit switches to the running mode where the learned driving style is applied. The learning mode can be activated by any driver of the car, for any driving session and each time he/she wishes to change the current driving behaviour of the cruise control system.The modelling framework which we propose to implement comprises four layers (sampler, profiler, tutor, performer). The sampler is responsible for human likeness and can be calibrated while in learning mode. Then, while in running mode, it works together with the other modelling layers to implement the logic. This paper presents the overall framework, with particular emphasis on the sampler and the profiler that are explained in full mathematical detail. Specification and calibration of the proposed framework are supported by the observed data, collected by means of an instrumented vehicle. The data are also used to assess the proposed framework, confirming human-like and fully-adaptive characteristics.     

Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework

The objective of this paper is to quantify and characterize driver behavior under different roadway geometries and weather conditions. In order to explore how a driver perceives the rapidly changing driving surrounding (i.e. different weather conditions and road geometry configurations) and executes acceleration maneuvers accordingly, this paper extends a Prospect Theory based acceleration modeling framework. A driving simulator is utilized to conduct 76 driving experiments. Foggy weather, icy and wet roadway surfaces, horizontal and vertical curves, and different lane and shoulder widths are simulated while having participants driving behind a yellow cab at speeds/headways of their choice. After studying the driving trends observed in the different driving experiments, the extended Prospect Theory based acceleration model is calibrated using the produced trajectory data. The extended Prospect Theory based model parameters are able to reflect a change in risk-perception and acceleration maneuvering when receiving different parameterized exogenous information. The results indicate that drivers invest more attention and effort to deal with the roadway challenges compared to the effort to deal with the weather conditions. Moreover, the calibrated model is used to simulate a highway segment and observe the produced fundamental diagram. The preliminary results suggest that the model is capable of capturing driver behavior under different roadway and weather conditions leading to changes in capacity and traffic disruptions.     

Field tests of a dynamic green driving strategy based on inter-vehicle communication

This paper presents the design and results for field tests regarding the environmental benefits in stop-and-go traffic of an algorithmic green driving strategy based on inter-vehicle communication (IVC), which was proposed in Yang and Jin (2014). The green driving strategy dynamically calculates advisory speed limits for vehicles equipped with IVC devices so as to smooth their speed profiles and reduce their emissions and fuel consumption. For the field tests, we develop a smartphone-based IVC system, in which vehicles’ speeds and locations are collected by GPS and accelerometer sensors embedded in smartphones, and communications among vehicles are enabled by specially designed smartphone applications, a central server, and 4G cellular networks. Six field tests are carried out on an uninterrupted ring road under slow or fast stop-and-go traffic conditions. We compare the performances of three alternatives: no green driving, heuristic green driving, and the IVC-based algorithmic green driving. Results show that heuristic green driving has better smoothing and environmental effects than no green driving, but the IVC-based algorithmic green driving outperforms both. In the future, we are interested in field tests under more realistic traffic conditions.     

Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance.The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string.Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance of the adaptive driving strategy under V2I cooperation is tested in simulation. Results show that even the estimation of control parameters of non-connected automated vehicles are imperfect and there is mismatch between the model used in analytical derivation and that in simulation, the proposed adaptive driving strategy suppresses disturbances in a wide range of situations.     

Analysis of real driving gaseous emissions from light-duty diesel vehicles

Increasingly strict emissions standards are providing a major impetus to vehicle manufactures for developing advanced powertrain and after-treatment systems that can significantly reduce real driving emissions. The knowledge of the gaseous emissions from diesel engines under steady-state operation and under transient operation provides substantial information to analyze real driving emissions of diesel vehicles. While there are noteworthy advances in the assessment of road vehicle emissions from real driving and laboratory measurements, detailed information on real driving gaseous emissions are required in order to predict effectively the real-time gaseous emissions from a diesel vehicle under realistic driving conditions. In this work, experiments were performed to characterize the behavior of NOx, unburned HC, CO, and CO₂ emitted from light-duty diesel vehicles that comply with Euro 6 emissions standards. The driving route fully reflected various real-world driving conditions such as urban, rural, and highway. The real-time emission measurements were conducted with a Portable Emissions Measurement System (PEMS) including a Global Positioning System (GPS). To investigate the gaseous emission characteristics, authors determined the road load coefficients of vehicle specific power (VSP) and regression coefficient between fuel use rate and VSP. Furthermore, this work revealed the correlation between the rates of average fuel use and each gaseous emission.     

大直径泥水盾构施工引起的地表沉降分析和对策

鉴于武汉长江隧道工程复杂边界条件,为了确保盾构施工安全,保护周边建筑物,因此在施工过程中,必须根据隧道覆土厚度、地质条件、周边环境条件,合理设置与控制泥水压力,确定合适的同步注浆量等,以控制非正常的地层损失、避免灾害性地层损失、控制地表沉降、避免开挖面坍塌。本文结合武汉长江隧道盾构施工经验,对大直径泥水盾构推进中对周边环境的影响因素进行分析,并就地表沉降控制和预防灾害事故及事故处理的一些体会进行总结。     

Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions

This research intends to explore external factors affecting driving safety and fuel consumption, and build a risk and fuel consumption prediction model for individual drivers based on natural driving data. Based on 120 taxi drivers’ natural driving data during 4 months, driving behavior data under various conditions of the roadway, traffic, weather, and time of day are extracted. The driver's fuel consumption is directly collected by the on-board diagnostics (OBD) unit, and safety index is calculated based on Data Threshold Violations (DTV) and Phase Plane Analysis with Limits (PPAL) considering speed, longitudinal and lateral acceleration. By using a linear mixed model explaining the fixed effect of the external conditions and the random effect of the driver, the influences of various external factors on fuel consumption and safety are analyzed and discussed. The prediction model lays a foundation for drivers' fuel consumption and risk prediction in different external conditions, which could help improve individual driving behavior for the benefit of both fuel consumption and safety.     

On-road emissions from urban buses with SCR + Urea and EGR + DPF systems using diesel and biodiesel

Due to growing concerns about NO_x and particulate matter (PM) emissions from diesel engines, stricter regulations are being introduced requiring advanced emission control technology. In response the diesel industry has begun testing various emission control technologies and applying them. To assess vehicle renewal policies of bus companies, two exhaust after-treatment technologies are compared: the combination of a diesel particulate filter and an exhaust gas re-circulation system and the combination of a selective catalytic reduction and urea. On-board emission measurements were conducted under real-world driving conditions on a specific bus route in the city of Madrid.     

Impact of traveler advisory systems on driving speed: some new evidence

This paper explores the effects of driving behavior using in-vehicle and out-of-vehicle traffic advisory information relating to adverse weather and incident conditions. A full-size, fixed-based driving simulator is used to collect data on drivers’ speed behavior under four different advisory-information conditions: in-vehicle messages, out-of-vehicle messages, both types of messages, and no messages. The findings of this study suggest an interesting phenomenon in that, while messages are significant in reducing speeds in the area of adverse conditions, drivers tend to compensate for this speed reduction by increasing speeds downstream when such adverse conditions do not exist. As a result, the net safety effects of such message systems are ambiguous.