“国有民营”一符合地铁本质特征的运营模式

指出地铁运营模式的选择应充分考虑到地铁的本质特性,分析地铁具有线路部分的自然垄断性与运营部分的非自然垄断性、公共性与企业性并存、极强的外部效应等特点,得出“国有民营”是一种符合地铁长质特征的运营模式的结论,并推出新加坡地铁的例证。     

上海市现代有轨电车系统发展策略研究

现代有轨电车是一种舒适度高、低碳环保的中运量轨道交通系统。通过对其特征研究和发展必要性分析,提出其在上海市的发展策略及发展规模,以弥补本市公共交通结构缺失,引导新城地区发展,提升其在公共交通出行结构中的比例。     

大跨度高铁斜拉桥钢-混结合主梁收缩徐变研究

为研究钢混结合主梁混凝土桥面板的收缩徐变对大跨度高铁无砟轨道斜拉桥的影响,以昌吉赣客专赣江特大桥为工程背景,采用Midas Civil软件建立全桥精细化数值分析模型,考虑钢混结合梁混凝土桥面板不同的加载龄期,分析结合梁斜拉桥在收缩徐变效应下变形及受力的变化。结果表明:赣江特大桥结合梁在施工成桥初期至运营5年后,钢混结合梁混凝土桥面板收缩徐变引起面板及钢箱梁的应力变化情况均满足规范要求,桥面板及钢箱梁在施工成桥1年后收缩徐变完成50%以上,3年后完成80%左右;桥面板混凝土的加载龄期越长,混凝土收缩徐变对桥梁结构变形和受力的影响越小,并在混凝土加载龄期达到180 d后对桥梁结构的影响呈稳定趋势,将结合梁桥面板预制存放180 d后再进行吊装,可有效降低混凝土收缩徐变对此种结构正常使用期间力学行为的影响。     

关于空气弹簧内部间隙的研究

对橡胶滞回、蠕变、静态刚度、动态刚度、内压等影响空气弹簧间隙的因素进行了分析,阐述了各种因素影响空气弹簧间隙的原理,给出了选择空气弹簧间隙的依据,并结合实例进行了试验验证。     

新线开通对线网客流特征的影响

随着北京市轨道交通的快速发展,每年的新线开通都将对线网各线的客流分布产生较大的影响,但新线可研多注重新线自身的预测,缺乏线网整体变化的预测分析.结合线网客流变化的关键因素,探索利用新线可研和现有OD数据预测新线开通后线网客流的方法,并以北京地铁4号线开通为例进行验证,从一定程度上证明该方法的可行性和适用性.     

单边索斜塔钢-混凝土结合梁斜拉桥塔梁根部应力分析

采用板 (壳 )、梁及三维实体单元相结合 ,对长沙市浏阳河洪山庙大桥塔梁根部区域进行空间有限元分析。分析表明 :主梁根部过渡段钢箱梁及混凝土的应力满足规范的限值要求 ,行车道板的剪力滞效应明显 ,徐变对主梁应力的分布影响显著 ;塔根部混凝土在施工过程及运营阶段均处于全截面受压状态 ,与试验结果相比较 ,吻合良好     

长期荷载作用对FRP约束混凝土轴心受压构件力学性能的影响

进行了12个FRP约束混凝土轴心受压短试件在长期荷载作用下的变形性能以及长期荷载作用后构件承载力的试验研究。基于ACI(1992)方法,提出一种适合于长期荷载作用下FRP约束混凝土轴心受压短构件变形的计算方法,理论结果和试验结果基本吻合。最后,分析了加载龄期和持荷时间、轴压比、含FRP率、FRP和混凝土强度等因素对长期荷载作用下FRP约束混凝土轴压构件变形性能的影响规律。     

关于换算弹性模量法中的老化系数

用两个简单的桥例阐明了换算弹性模量法中老化系数公式的由来。它将有助于读者对混凝土徐变和收缩次内力计算原理的理解。     

软岩边坡加固中锚索预应力变化影响因素分析

预应力锚索被广泛应用于边坡加固,并获得了良好的加固效果和经济效益,但针对锚索预应力的影响因素及变化规律的研究还不多见。本文简要分析了预应力锚索的受力形式,结合实际软岩边坡工程,比较分析施工期和雨季中大量的监测数据,详细探讨了边坡在最不利时期中影响预应力变化的主要因素及变化特点。主要包括锚索材料、施工影响及外部因素等,对进一步综合归纳预应力变化的规律及计算模型的确定有一定的参考价值。     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.