基于混合算法的大跨度斜拉桥可靠度评估

为研究大跨度顸应力混凝土(PC)斜拉桥的可靠度评估问题,提出了适用于大跨度PC斜拉桥这类复杂结构可靠性分析的混合算法.该方法综合运用了有限元分析(FEA)、径向基函数(RBF)神经网络、遗传算法(GA)和Monte Carlo重要抽样(MCIS)方法,并对算法中的关键步骤(RBF神经网络的初始样本点设计方法、MCIS的抽样中心点位置等)进行了改进,使结构分析模块与可靠度计算模块智能结合.利用数值算例的可靠度分析对该算法的有效性进行了验证.最后,以一座主跨为420 m的双塔PC斜拉桥为工程背景,进行了正常使用极限状态下的可靠度分析.参数分析表明:在汽车荷载作用下,该斜拉桥的主梁跨中位移超限失效概率比最长斜拉索强度失效概率高;汽车荷载的均值和标准差是影响斜拉桥可靠度的重要因素;随着汽车荷载均值系数的增大,主梁跨中位移超限失效的可靠指标下降的趋势较为显著.     

高速公路场景下的电子稳像技术研究

为了去除高速公路监控视频中自身位置不稳定的事物给全局运动矢量估计带来误差,先通过Canny算子检测图像中的直线,并根据车道边界的特征筛选出道路边界,确定道路区域。再检测道路区域内的Harris角点信息,并与背景图中道路区域内的角点位置进行匹配,从而得到全局运动矢量,并计算出稳定的图像。实验表明,该算法能够满足高速公路场景下的稳像需求。     

基于LabVIEW管道泄漏检测系统的研究

介绍了管道泄漏检测系统的设计原理.利用LabVIEW中的小波分析函数对采集到的信号进行处理,并利用小波变换模极大值捕捉压力信号的突变点,从而得出负压波传播到上下游监测点的时间差,进而利用负压波定位原理进行泄漏点的定位.另外,对于优化程序内存和提高CPU的利用率提出了一些有效的解决方法.     

采油工艺管柱自动导向器

设计了一种新型采油工艺管柱导向器,通过应用该导向器,在作业中实现管柱铰接,可有效解决由套管套变引起的封隔器损坏、失效现象,起到保护封隔器胶筒的作用.并可解决大斜度井、定向井工艺管柱偏磨与导向问题,减少对管柱接箍的损坏及对套管的刮磨破坏,提高其使用寿命.可广泛应用于油水井增产增注工艺管柱上.     

城市燃气管道第三方损坏的评价方法

第三方损坏评价是指对由于各种外力作用而使管道受损的可能性进行分级.根据影响第三方损坏因素的情况,采用故障树分析法和模糊综合评判法相结合的数学模型进行评价.通过故障树分析,得到引起第三方损坏的各种因素的重要度.再将之作为模糊综合评判法的权重向量,求得第三方损坏的综合评判向量,最后按照最大隶属度原则确定各管段第三方损坏可能性的等级.     

管道内防腐层补口技术研究进展

管道内衬或内防腐涂层现场施焊时,焊缝两侧的防腐层会由于高温作用而破坏,使环焊缝处不能得到有效的保护,内补口技术也成为影响管道质量的主要瓶颈.文中介绍了国内外主要的内补口技术,如内补口机法、内衬保护套(管)焊接法、不锈钢接头法、机械连接法等,并指出各方法的特点.文中还就内补口技术的质量控制和研究方向等提出了建议.     

西山特长隧道斜井进入正洞交叉口施工技术

太古高速公路西山特长隧道全长13.654 km,是国内目前在建最长的公路隧道。在斜井进入正洞交叉口的施工中,成功采用台阶法矩形断面垂直挑顶直接转入正洞施工方案,通过优化调整设计参数,采取加强支护、减震爆破等技术措施,实现了斜井安全、简便、快速地转入正洞的施工。     

基于管片错台的通缝拼装盾构隧道纵向变形安全评估

以上海软土地区通缝拼装地铁盾构隧道为研究对象,根据隧道管片环缝接头处的详细构造,对管片错台模式下的隧道纵向变形进行了研究,建立了隧道纵向结构变形安全评估方法,确定了相应的控制准则和评估流程,形成了一套隧道结构纵向变形的安全评估体系.     

Approximate network loading and dual-time-scale dynamic user equilibrium

In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that Pang and Stewart (2008) refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of Friesz et al. (1993) may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to Daganzo (1995). We provide a numerical example based on the much studied Sioux Falls network.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.