钢筋混凝土拱桥加固技术

拱桥的加固方法较多,以一座拱桥加固为例,重点介绍拱桥的裂缝修补、粘贴钢板、浇筑混凝土的施工工艺,该加固技术集多种加固方法于一体,加固效果好,并以此来说明拱桥加固要根据桥梁现状和桥梁的拟加固荷载等级采取可行的加固方案,以保证加固后的桥梁正常使用。在施工时不要仅仅采取单一的加固方法,应采取复合式加固方法,要集多种加固方法于一体。     

公路货物运输抽样调查中样本量的确定

在分析我国现行公路货物运输抽样调查方案的基础上,结合分层随机抽样中样本量的确定方法,以广东省为例,对公路货物运输抽样中的样本量进行测算,可进一步验证公路货物运输抽样调查中样本量确定的科学性,同时能为交通运输行业或其他行业类似的抽样调查提供有益的实践经验.     

碳纤维布在金华桥加固中的应用

碳纤维布加固段结构桥梁是一项新技术,碳纤维片材轻,现场粘贴无需重型设备,设施简便,便于桥下高空作业,可在公路桥梁上推广应用.     

交通调查者行为效应分析

交通调查者的行为对获取真实、有效的交通运输系统原始数据起着关键性作用.通过对调查者行为的影响因素进行分析,依据行为学理论解释交通调查者的行为机理,并提出交通调查者的行为效应模型,具有十分重要的意义.     

注浆法在公路基层加固中的应用

鉴于当前公路基层的严重破坏,基层加固是必要的.从注浆法的优点、机理以及关键控制技术进行阐述,并从实践中得出了注浆法加固公路基层还在探索阶段,有待于在今后施工中不断完善这一结论.     

高铁湿陷性黄土隧道地基处理浅析

根据湿陷性黄土隧道工程特点,结合宝兰客专客运专线王家岔湿陷性黄土隧道地基加固处理的工程实例,阐述了洞口和洞内两种施工工艺不同的水泥土挤密桩施工方案,分析了隧道内水泥土挤密桩施工对其它工序的干扰和对工期的影响,以及施工振动对初支结构和围岩稳定的危害,提出了有效的控制措施.     

Enhancing stated preference surveys in transportation research: the contribution of geographic information systems

In this paper we argue that visualization, data management and computational capabilities of geographic information systems (GIS) can assist transportation stated preference research in capturing the contextual complexity of many transportation decision environments by providing respondents with maps and other spatial and non-spatial information in graphical form that enhance respondents' understanding of decision scenarios. We explore the multiple inherent contributions of GIS to transportation stated preference data collection and propose a framework for a GIS-based stated preference survey instrument. We also present the design concepts of two survey prototypes and their GIS implementation for a sample travel mode choice problem.     

兰州市五人制足球运动开展现状调查分析

本文运用文献资料法、专家访谈法、问卷调查法、数理统计法对兰州市五人制足球运动的开展规模、教练员情况、场地设施、社会参与人群状况等问题进行调查分析,提出了针对性建议,旨在为进一步推动兰州市五人制足球的健康快速发展,同时促进兰州市足球运动的普及与提高尽一份力。     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.