基于分布式光纤光栅传感器的PCCP断丝监测技术

预应力钢筒混凝土管(PCCP)运行中会产生断丝现象,进而导致泄漏等事故。文中提出了基于光纤光栅传感器的PCCP断丝实时监测与定位技术,预应力钢丝断裂瞬间会释放应力波,沿管壁和管内水介质进行传播,在管内壁设置光纤光栅传感器进行应力波监测,设计悬臂梁装置用于微应变的增敏,提高断丝监测概率,并分析相邻两传感器对断丝监测信号的捕获时间差实现断丝位置的定位。测试结果表明:断丝监测概率100%,最大定位误差0.11 m,满足工程需求。     

分布式光纤布拉格光栅在油气管道检测中的应用

阐述了光纤布拉格光栅温度应力传感器的工作原理及光栅中心波长的移动探测解调原理,分析了光纤布拉格光栅传感器在油气管道监测中的应用和前景。     

传感器与沥青混合料的变形协调室内试验研究

传感器与沥青混合料变形协调一致,是准确测试沥青路面结构内部应变的基础。本文通过采取一定的技术手段,将光纤光栅传感器埋设与车辙板和圆柱形试件中,在室内开展了车辙试验和抗压试验,并且采用相应不埋设传感器的试件进行对照,对比分析了在荷载作用下应变传感器测试结果的可靠性。结果表明,光纤光栅应变传感器可以应用于道路智能测试中。     

预应力孔道摩阻试验与分析

随着预应力桥梁跨径增大,预应力筋曲线布束,但设计、施工中往往对长束、曲然束力筋张拉应力损失欠重视或欠处理,从而导致实际施加的预应力达不到要求,影响结构物质量。本文通过孔道摩阻试验分析,总结伸长量偏差的原因,并提出相应的解决措施。     

钢绞线和精轧螺纹钢筋的竖向预应力对比研究

文章对箱梁腹板竖向预应力钢绞线和精轧螺纹钢筋进行对比试验,研究钢绞线和精轧螺纹钢筋竖向预应力的规律,分析了两种不同竖向预应力体系的特征和预应力损失,结果表明钢绞线和精轧螺纹钢筋的第一次张拉预应力损失率和两者长期损失率基本相同,竖向预应力钢绞线的第二次张拉效果明显,预应力损失率较第一次张拉减小近1/2,对于控制主拉应力较大的高腹板的竖向预应力可靠度较大,证明了钢绞线作为竖向预应力筋的实际意义,为工程设计提供参考.     

基于应力波反射法的桥梁竖向预应力筋管道注浆效果检测

文章结合某连续刚构桥,通过分析应力波反射法的检测原理,试将该检测方法用于桥梁竖向预应力筋波纹管注浆效果的评价,结果表明采用该方法检测桥梁单根预应力筋管道注浆密实度是一种高效、便捷、经济的检测方法。     

大跨径PC箱梁桥腹板开裂与处治综述

文章在归纳总结国内外关于大跨径PC箱梁桥腹板裂缝研究的基础上,从混凝土和竖向预应力筋的施工性能两个方面分析他们对腹板裂缝的影响。结论显示:高强混凝土较大的自收缩和较高的脆性是造成腹板出现早期裂缝的重要原因;根部腹板大体积混凝土施工时过大的水化热会造成腹板在施工阶段出现贯通裂缝;竖向预应力筋采用性能优良的CFRP筋能有效降低竖向预应力损失;竖向预应力筋采用滞后张拉并保证竖向预应力筋间距与腹板半高比s/c0.5,腹板截面不同高度处预应力效率均较高。     

斜拉桥索塔环向预应力摩阻损失试验研究

文章依托江苏省如皋市长青沙二桥(斜拉桥)工程项目,通过预应力张拉试验,现场实测了U形预应力筋的摩阻损失,并采用最小二乘法拟合求得孔道摩阻系数和偏差系数,验证了此种预应力结构的合理性。     

无粘结预应力技术在厦门太古飞机维修库的应用

常规的后张预应力混凝土是在预应力筋张拉后的预留孔道中压注水泥浆，使预应力筋与结构混凝土粘结，既保护预应力筋免于锈蚀，又改善了锚具以及锚端混凝土负荷情况，但其特点是摩阻损失大，结构内预应力分不均衡，无粘结预应力混凝土技术是在预应力筋表面刷涂料并用塑料软管包裹，直接铺设在混凝土浇筑前的模板内，使预应力筋不与结构混凝土产生粘结，待混凝土达到设计强度后张拉锚固，有效解决了常规后张预应力混凝土的缺点，有较大的应用空间。     

基于扰动应力场的平面钢筋混凝土非线性分析

钢筋混凝土结构很多情况下是带裂缝工作的,裂缝会引起应力集中和刚度降低,结构会表现出诸多非线性特征。为准确分析裂缝出现后钢筋混凝土刚度降低引起的应力重分布效应,控制应力集中产生的混凝土开裂或钢筋屈服对结构耐久性的影响,文章基于扰动应力场理论,依托一个两跨连续梁算例,建立模型对受拉强化、受压软化、剪切滑移变形等非线性效应和预应力筋、纵向主筋的应力重分析状态进行精确非线性力学比对分析。结论表明,钢筋混凝土结构非线性效应影响显著,其可能加剧结构局部失效,继而影响整体受力状态。     

Pedestrian crossing detection based on evidential fusion of video-sensors

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.     

Vehicle identification sensor models for origin–destination estimation

The traditional approach to origin–destination (OD) estimation based on data surveys is highly expensive. Therefore, researchers have attempted to develop reasonable low-cost approaches to estimating the OD vector, such as OD estimation based on traffic sensor data. In this estimation approach, the location problem for the sensors is critical. One type of sensor that can be used for this purpose, on which this paper focuses, is vehicle identification sensors. The information collected by these sensors that can be employed for OD estimation is discussed in this paper. We use data gathered by vehicle identification sensors that include an ID for each vehicle and the time at which the sensor detected it. Based on these data, the subset of sensors that detected a given vehicle and the order in which they detected it are available. In this paper, four location models are proposed, all of which consider the order of the sensors. The first model always yields the minimum number of sensors to ensure the uniqueness of path flows. The second model yields the maximum number of uniquely observed paths given a budget constraint on the sensors. The third model always yields the minimum number of sensors to ensure the uniqueness of OD flows. Finally, the fourth model yields the maximum number of uniquely observed OD flows given a budget constraint on the sensors. For several numerical examples, these four models were solved using the GAMS software. These numerical examples include several medium-sized examples, including an example of a real-world large-scale transportation network in Mashhad.     

Density/Flow reconstruction via heterogeneous sources and Optimal Sensor Placement in road networks

This paper addresses the two problems of flow and density reconstruction in Road Transportation Networks with heterogeneous information sources and cost effective sensor placement. Following a standard modeling approach, the network is partitioned in cells, whose vehicle densities change dynamically in time according to first order conservation laws. The first problem is to estimate flow and the density of vehicles using as sources of information standard fixed sensors, precise but expensive, and Floating Car Data, less precise due to low penetration rates, but already available on most of main roads. A data fusion algorithm is proposed to merge the two sources of information to estimate the network state. The second problem is to place sensors by trading off between cost and performance. A relaxation of the problem, based on the concept of Virtual Variances, is proposed and solved using convex optimization tools. The efficiency of the designed strategies is shown on a regular grid and in the real world scenario of Rocade Sud in Grenoble, France, a ring road 10.5 km long.     

A graphical approach to identify sensor locations for link flow inference

Information of link flows in a traffic network becomes increasingly critical in contemporary transportation practice and researches. The network sensor installation is carried out to supply such information. In this paper, we present a graphical approach to determine the smallest subset of links in a traffic network for counting sensor installation, so as to infer the flows on all remaining links. The elegant assumption-free character of the problem introduced by Hu, Peeta and Chu is still kept in this approach. This study points out the topological tree feature of solutions that makes it possible for traffic management agencies to easily and flexibly select links for sensor installation in practice. Addressing from the same graphical perspective, we provide solutions to four other important problems about sensor locations. The preceding two problems are, in traffic networks that already have sensors installed on some links, to identify the subset of links on which link flows can be inferred from sensor measurements and to determine the smallest subset of links on which counting sensors also need to be installed so as to infer link flows on all remaining non-equipped links. The third is to identify the optimal locations for a given number of sensors so as to infer flows on as many links as possible by gradually enlarging the number of links included in circuits. The last one is to determine the smallest subset of links on which to install sensors, in such a way that it becomes possible at the same time to satisfy prior requirements and infer the flows on all remaining links, through building a minimum spanning tree. These methods can be applied to all kinds of long-term planning and link-based applications in traffic networks.     

Development of a sensor system for traffic data collection

Although many types of traffic sensors are currently in use, all have some drawbacks, and widespread deployment of such sensor systems has been difficult due to high costs. Due to these deficiencies, there is a need to design and evaluate a low cost sensor system that measures both vehicle speed and counts. Fulfilling this need is the primary objective of this research. Compared to the many existing infrared-based concepts that have been developed for traffic data collection, the proposed method uses a transmission-based type of optical sensor rather than a reflection-based type. Vehicles passing between sensors block transmission of the infrared signal, thus indicating the presence of a vehicle. Vehicle speeds are then determined using the known distance between multiple pairs of sensors. A prototype of the sensor system, which uses laser diode and photo detector pairs with the laser directly projected onto the photo detector, was first developed and tested in the laboratory. Subsequently this experimental prototype was implemented for field testing. The traffic flow data collected were compared to manually collected vehicle speed and traffic counts and a statistical analysis was done to evaluate the accuracy of the sensor system. The analysis found no significant difference between the data generated by the sensor system and the data collected manually at a 95% confidence interval. However, the testing scenarios were limited and so further analysis is necessary to determine the applicability in more congested urban areas. The proposed sensor system, with its simple technology and low cost, will be suitable for saturated deployment to form a densely distributed sensor network and can provide unique support for efficient traffic incident management. Additionally, because it may be quickly installed in the field without the need of elaborate fixtures, it may be deployed for use in temporary traffic management applications such as traffic management in road work zones or during special events.     

Network sensor health problem

Many existing studies on the sensor health problem determine an individual sensor’s health status based on the statistical characteristics of collected data by the sensor. In this research, we study the sensor health problem at the network level, which is referred to as the network sensor health problem. First, based on the conservation principle of daily flows in a network, we separate all links into base links and non-base links, such that the flows on the latter can be calculated from those on the former. In reality, the network flow conservation principle can be violated due to the existence of unhealthy sensors. Then we define the least inconsistent base set of links as those that minimize the sum of squares of the differences between observed and calculated flows on non-base links. But such least inconsistent base sets may not be unique in a general road network. Finally we define the health index of an individual sensor as the frequency that it appears in all of the least inconsistent base sets. Intuitively, a lower health index suggests that the corresponding sensor is more likely to be unhealthy. We present the brute force method to find all least inconsistent base sets and calculate the health indices. We also propose a greedy search algorithm to calculate the approximate health indices more efficiently. We solve the network sensor health problem for a real-world example with 16 nodes and 30 links, among which 18 links are monitored with loop detectors. Using daily traffic count data from the Caltrans Performance Measurement System (PeMS) database, we use both the brute-force and greedy search methods to calculate the health indices for all the sensors. We find that all the four sensors flagged as unhealthy (high value) by PeMS have the lowest health indices. This confirms that a sensor with a lower health index is more likely to be unhealthy. Therefore, we can use such health indices to determine the relative reliability of different sensors’ data and prioritize the maintenance of sensors.     

Analysis of factors that influence the sensor location problem for freeway corridors

The use of traffic sensors to acquire real‐time traffic information for intelligent transportation systems is becoming increasingly common. It is a challenge to determine where these sensors should be located to maximize the benefit of their use. This paper aims to illuminate the interaction of the sensor location problem (SLP) and its influencing factors, and to reveal the influencing mechanisms between those factors and the optimal sensor numbers. Firstly, we sum up the factors that influence the SLP for freeway corridors in detail and present the mathematical formulation of each factor. Then, given the parameters, which are derived from those influencing factors, the maximum integration value model (MIVM) and simplified MIVM are proposed for addressing the SLP. Finally, a real world case study, in which the simplified MIVM is used, is presented to illustrate how these factors influence the optimal sensor numbers and the maximum integration value, and also leads to the typical influencing patterns of those factors for freeway corridors. The results of the case study also demonstrate the effectiveness of the model and problem solving scheme. What is more, the suggestions for using the findings hereof in practical applications are put forward. Copyright © 2013 John Wiley & Sons, Ltd.     

Virtual 3D city model as a priori information source for vehicle localization system

This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on–board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches.     

Heterogeneous sensor location model for path reconstruction

A new traffic sensor location problem is developed and solved by strategically placing both passive and active sensors in a transportation network for path reconstruction. Passive sensors simply count vehicles, while active sensors can recognize vehicle plates but are more expensive. We developed a two-stage heterogeneous sensor location model to determine the most cost-effective strategies for sensor deployment. The first stage of the model adopts the path reconstruction model defined by Castillo et al. (2008b) to determine the optimal locations of active sensors in the network. In the second stage, an algebraic framework is developed to strategically replace active sensors so that the total installation cost can be reduced while maintaining path flow observation quality. Within the algebraic framework, a scalar product operator is introduced to calculate path flows. An extension matrix is generated and used to determine if a replacement scheme is able to reconstruct all path flows. A graph model is then constructed to determine feasible replacement schemes. The problem of finding the optimal replacement scheme is addressed by utilizing the theory of maximum clique to obtain the upper bound of the number of replaced sensors and then revising this upper bound to generate the optimal replacement scheme. A polynomial-time algorithm is proposed to solve the maximum clique problem, and the optimal replacement scheme can be obtained accordingly. Three numerical experiments show that our proposed two-stage method can reduce the total costs of transportation surveillance systems without affecting the system monitor quality. The locations of the active sensors play a more critical role than the locations of the passive sensors in the number of reconstructed paths.     

A generalized sensor location model for the estimation of network origin–destination matrices

This study examined the network sensor location problem by using heterogeneous sensor information to estimate link-based network origin–destination (O–D) demands. The proposed generalized sensor location model enables different sensors’ traffic monitoring capabilities to be used efficiently and the optimal number and deployment locations of both passive- and active-type sensors to be determined simultaneously without path enumeration. The proposed sensor location model was applied to solve the network O–D demand estimation problem. One unique aspect of the proposed model and solution algorithms is that they provide satisfactory network O–D demand estimates without requiring unreasonable assumptions of known prior information on O–D demands, turning proportions, or route choice probabilities. Therefore, the proposed model and solution algorithms can be practically used in numerous offline transportation planning and online traffic operation applications.