下坡盾构到达段上浮原因及措施

在盾构到达施工过程中,盾构机及管片在接近洞门时会出现上浮.从上浮空间、盾构机受力和管片受力三个方面进行的分析结果表明:盾构在下坡段的过量超挖为上浮提供了空间;盾构机在下坡线路中掘进受到向上的合力作用,为盾构机提供了上浮力.通过弹性地基梁法简化计算,分析了管片在浆液中的上浮趋势,提出了盾构、管片上浮的控制措施,保证了盾构的顺利进洞.     

施工用盾构机管理技术的吸收与改进

通过对广州两个盾构区间的盾构施工,分析总结如何加强盾构机设备管理、如何在施工中学习吸收盾构技术并根据施工的实际情况对部分系统进行改进,以适应施工区间地质条件,达到良好的使用效果。     

盾构机刀盘驱动系统动力计算分析

通过对广州地铁西草区间两台盾构机现场实测刀盘转速、刀盘扭矩数据,结合盾构配置的电机、液压泵、马达、减速箱的铭牌标定参数,计算分析得出制造商配置的减速箱并不能满足招投标时的产品承诺和现场实际需要。     

海底盾构隧道孤石爆破预处理关键技术

文章针对基于孤石形状、分布位置的随机性及其高强度,盾构机无法直接掘进通过这一工程难题,在前期物探及钻孔验证的基础上,对钻孔参数和爆破方案设计进行了分析;通过采用对临近隧洞爆破震动跟踪监测和爆破后炮孔取芯验证的方法,总结出了一套针对海底盾构区间孤石爆破预处理的施工方法.     

盾构机部件状态监测与性能预测

传统的盾构机检测手段不仅影响盾构机施工质量和进度,而且风险大、效果不佳、精度不高。结合上海沿江通道工程、上海长江西路隧道工程盾构机刀盘关键施工数据,运用大数据分析方法寻求导致盾构机故障的关键特征。对性能衰退征兆进行研究,并深入分析盾构机系统运行过程的各个参数,提取特征以准确地确定盾构机关键部件性能衰退的部位和原因;根据性能急剧衰退的征兆,在微弱状态时就能作出有效预测。     

大直径铁路盾构隧道设计及选型技术研究

文章依托太原市铁路枢纽西南环线东晋隧道盾构设计与施工实例,从工程地质、水文地质、周边环境及风险控制等方面,对盾构隧道的设计与选型进行了适应性分析与探讨。结果表明,盾构选型是盾构隧道施工的关键,应掌握详尽的地质勘察资料,有针对性地选择盾构机类型和确定盾构机相关技术参数;盾构施工中加强碴土改良及注浆系统的配置,能较好地控制地层沉降;采用大直径土压平衡盾构机在周边环境复杂的地层中施工是可行的,能够最大程度减小对城市居民生活的影响。     

海相复合地层超大直径泥水加压平衡盾构施工关键技术

研究海相复合地层下超大直径盾构法隧道施工关键技术,成果可直接应用于珠海等地大直径盾构法隧道的建设,也可为国内其他类似地层的跨江海隧道起到示范作用。针对大直径泥水加压平衡盾构机穿越复合地层施工,介绍了先探测、预处理基岩,后盾构机穿越的整体思路,分析了超大直径隧道复合地层的准确探测和处理技术;结合地层超前预报、盾构刀盘及刀具、土仓防堵塞冲刷系统、排浆管路捕石器、泥水循环系统等针对性设计和施工方法,实现了泥水加压平衡盾构装备在复合地层中掘进的适应性,整体改善了盾构装备的施工效率。     

盾构机姿态参数的测量及计算方法研究

根据三点决定一个平面的原理,通过在盾构机中体上布置测量控制点,对其三维坐标进行测量;根据空间解析几何原理,推导出盾构机刀盘中心三维坐标以及俯仰角、横摆角、扭转角的计算方法.文章利用计算机的伪随机函数对盾构机姿态参数的测量精度进行了模拟评价,探讨了提高测量精度的方法.结果表明,盾构姿态参数的测量误差均服从正态分析;采用精度为3 mm的激光经纬仪测量控制点坐标,得到的盾构姿态参数的误差范围比规范要求小得多.     

无水漂卵砾石地层土压平衡盾构施工关键技术

文章以北京地铁九号线丰台东大街站—丰台北路站区间全断面无水漂卵砾石地层盾构掘进工程为背景,基于地质勘查资料和试验探井的分析结果,掌握了该漂卵砾石地层的空间分布规律和力学性质。在合理选用辐条式土压平衡盾构的基础上,通过对盾构机关键部件进行适应性改造,降低了刀盘刀具磨损。施工中通过掘进参数的有效选择,实现了该类型盾构机在全断面卵砾漂石地层中的长距离掘进。     

基于Web的盾构机远程监控系统开发

基于Web的盾构机远程监控系统实现了多台盾构设备集中监控、多用户通过互联网访问盾构机信息和远程诊断预测等功能,使得系统使用更加科学合理。从系统的总体结构、硬件布置和软件开发等3方面对系统进行研究,实现了用户或技术人员远程通过浏览器访问多台盾构机、掌握运行状况的目的。监控系统提高了盾构机管理的便利性和有效性。     

A computationally efficient two-stage method for short-term traffic prediction on urban roads

Abstract

Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads.     

VMT自动导向系统在盾构法施工中的应用和研究

利用自动仿真技术,结合应用广泛的测量仪器,针对盾构法地铁施工,采用无线数据传输功能,方便、快捷地将盾构机掘进姿态以图形和文字的双重效果实时显示在计算机屏幕上,指导盾构机操作手调整盾构掘进参数,可真正实现操作可视化、同步化。     

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

This paper presents a dynamic network‐based approach for short‐term air traffic flow prediction in en route airspace. A dynamic network characterizing both the topological structure of airspace and the dynamics of air traffic flow is developed, based on which the continuity equation in fluid mechanics is adopted to describe the continuous behaviour of the en route traffic. Building on the network‐based continuity equation, the space division concept in cell transmission model is introduced to discretize the proposed model both in space and time. The model parameters are sequentially updated based on the statistical properties of the recent radar data and the new predicting results. The proposed method is applied to a real data set from Shanghai Area Control Center for the short‐term air traffic flow prediction both at flight path and en route sector level. The analysis of the case study shows that the developed method can characterize well the dynamics of the en route traffic flow, thereby providing satisfactory prediction results with appropriate uncertainty limits. The mean relative prediction errors are less than 0.10 and 0.14, and the absolute errors fall in the range of 0 to 1 and 0 to 3 in more than 95% time intervals respectively, for the flight path and en route sector level. Copyright © 2017 John Wiley & Sons, Ltd.     

TBM掘进数据标准化预处理方法研究北大核心CSCD

TBM信息化采集了海量数据,对TBM数据的标准化预处理是进行诸多研究的前提。基于此,提出了一种TBM掘进数据标准化处理方法,依托TBM现场施工掘进大数据,以破岩特征为依据选取基本掘进参数(刀盘转速、推进速度、刀盘推力及刀盘扭矩)分析掘进过程TBM数据特点,提出循环掘进过程空推段、上升段、稳定段及下降段起点的判别方法,对稳定段起点提出了标准差法判别方法、均值判别方法、直方图判别方法,满足实时和非实时的数据划分需求。最后对两个TBM工程的数据进行标准化预处理,实现施工大数据的标准化。结果表明,提出的标准化预处理方法可实现循环掘进过程数据的有效划分。研究成果可推广应用于众多TBM工程的数据标准化处理,有效实现机器学习数据库的建立。     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

A novel passenger flow prediction model using deep learning methods

Currently, deep learning has been successfully applied in many fields and achieved amazing results. Meanwhile, big data has revolutionized the transportation industry over the past several years. These two hot topics have inspired us to reconsider the traditional issue of passenger flow prediction. As a special structure of deep neural network (DNN), an autoencoder can deeply and abstractly extract the nonlinear features embedded in the input without any labels. By exploiting its remarkable capabilities, a novel hourly passenger flow prediction model using deep learning methods is proposed in this paper. Temporal features including the day of a week, the hour of a day, and holidays, the scenario features including inbound and outbound, and tickets and cards, and the passenger flow features including the previous average passenger flow and real-time passenger flow, are defined as the input features. These features are combined and trained as different stacked autoencoders (SAE) in the first stage. Then, the pre-trained SAE are further used to initialize the supervised DNN with the real-time passenger flow as the label data in the second stage. The hybrid model (SAE-DNN) is applied and evaluated with a case study of passenger flow prediction for four bus rapid transit (BRT) stations of Xiamen in the third stage. The experimental results show that the proposed method has the capability to provide a more accurate and universal passenger flow prediction model for different BRT stations with different passenger flow profiles.     

地铁盾构机传感器应用技术

盾构机上应用传感器技术，可以为盾构操作、PLC控制、计算机数据采集等提供及时、准确的设备信息和施工信息。结合工程实践，介绍了盾构机传感器的基本配置、设计选型、安装调试、维护保养和现场技术服务的概况，并对其工作原理进行了分析，以供同行参考。     

Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information

Short‐term traffic flow prediction is fundamental for the intelligent transportation system and is proved to be a challenge. This paper proposed a hybrid strategy that is general and can make use of a large number of underlying machine learning or time‐series prediction models to capture the complex patterns beneath the traffic flow. With the strategy, four different combinations were implemented. To consider the spatial features of traffic phenomenon, several different state vectors including different observations were built. The performance of the proposed strategy was investigated using the traffic flow measurements from the Traffic Operation and Safety Laboratory in Wisconsin, USA. The results show the overall performance of hybrid strategy is better than a single model. Also, incorporating observations from adjacent junctions can improve prediction accuracy. Copyright © 2017 John Wiley & Sons, Ltd.     

Variable speed limit control for severe non-recurrent freeway bottlenecks

Variable speed limit (VSL) schemes are developed based on the Kinematic Wave theory to increase discharge rates at severe freeway bottlenecks induced by non-recurrent road events such as incidents or work zones while smoothing speed transition. The main control principle is to restrict upstream demand (in free-flow) progressively to achieve three important objectives: (i) to provide gradual speed transition at the tail of an event-induced queue, (ii) to clear the queue around the bottleneck, and (iii) to discharge traffic at the stable maximum flow that can be sustained at the bottleneck without breakdown. These control objectives are accomplished without imposing overly restrictive speed limits. We further provide remedies for (a) underutilized bottleneck capacity due to underestimated stable maximum flow and (b) a re-emergent queue at the bottleneck due to an overestimated stable maximum flow. We analytically formulate the reductions in total delay in terms of control parameters to provide an insight into the system performance and sensitivity. The results from the parameter analysis suggest that significant delay savings can be realized with the proposed VSL control strategies.     

A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow

Systematic lane changes can seriously deteriorate traffic safety and efficiency inside lane-drop, merge, and other bottleneck areas. In our previous studies (Jin, 2010a, Jin, 2010b), a phenomenological model of lane-changing traffic flow was proposed, calibrated, and analyzed based on a new concept of lane-changing intensity. In this study, we further consider weaving and non-weaving vehicles as two commodities and develop a multi-commodity, behavioral Lighthill–Whitham–Richards (LWR) model of lane-changing traffic flow. Based on a macroscopic model of lane-changing behaviors, we derive a fundamental diagram with parameters determined by car-following and lane-changing characteristics as well as road geometry and traffic composition. We further calibrate and validate fundamental diagrams corresponding to a triangular car-following fundamental diagram with NGSIM data. We introduce an entropy condition for the multi-commodity LWR model and solve the Riemann problem inside a homogeneous lane-changing area. From the Riemann solutions, we derive a flux function in terms of traffic demand and supply. Then we apply the model to study lane-changing traffic dynamics inside a lane-drop area and show that the smoothing effect of HOV lanes is consistent with observations in existing studies. The new theory of lane-changing traffic flow can be readily incorporated into Cell Transmission Model, and this study could lead to better strategies for mitigating bottleneck effects of lane-changing traffic flow.