废食用油预脱硫胶粉改性沥青组分与黏弹性研究（双语出版）

为研究废食用油预脱硫胶粉（WRO）对沥青组分及黏弹性的影响,在不同掺量、加工温度和加工时间下分别制备了15组废食用油预脱硫胶粉改性沥青（WROMA）。基于四组分试验、DSR时间扫描试验、多应力蠕变回复试验分析了沥青组分及黏弹性变化的规律。结果表明：在组分方面,WRO中的橡胶烃主要补充沥青质,油分大部分补充饱和分,少部分补充芳香分;加工工艺与改性沥青中饱和分和胶质的含量线性相关性较弱,与芳香分及沥青质的含量线性相关性很强,且芳香分与沥青质含量主要受WRO掺量影响。在黏弹性方面,WRO掺量增加,改性沥青高温黏性降低,弹性增加。为保证改性沥青获得足够的高温弹性,WRO最佳掺量为30%,加工温度为160℃、加工时间为2 h。综合组分与黏弹性的分析结果可知：芳香分是影响WROMA高温黏性的主导因素,其含量越高沥青黏性越明显;沥青质和WRO中的橡胶烃是影响WROMA高温弹性的主导因素,两者含量越高,改性沥青弹性越明显。     

基于砾钢渣+橡胶颗粒的新型填料动剪切模量与阻尼比特性试验

为了解决固体废弃物循环利用问题,提出将废旧轮胎橡胶颗粒掺入废弃钢渣中形成新型土工填料。为了了解该新型填料的动力特性,采用共振柱试验对基于钢渣+橡胶颗粒的新型土工填料的动剪切模量和阻尼比特性开展研究。首先分析围压与橡胶颗粒含量对新型填料动剪切模量和阻尼比的影响,试验结果表明：动剪切模量和阻尼比与剪应变的关系类似传统土类,动剪切模量随着剪应变的增大而减小,阻尼比则随着剪应变的增加而增大,变化曲线趋势基本一致;新型填料的动剪切模量随着钢渣含量的减少和橡胶颗粒含量的增加而逐渐减小,橡胶颗粒含量达到20%时,动剪切模量与剪应变的关系曲线低缓显著,新型填料中橡胶颗粒不宜掺入太多。然后,基于Hardin-drnevich双曲线模型建立新型填料的动力特性理论模型,Hardin-drnevich双曲线模型能够较好地模拟新型填料动剪切模量归一化的数据,并对新型填料参考剪应变随橡胶含量的变化趋势进行了预测。最后,将新型填料的最大动剪切模量与纯钢渣、南京砂和福建标准砂的最大动剪切模量进行比较。分析结果表明：新型填料的橡胶颗粒含量不宜超出15%,这种新型填料动剪切模量适中,阻尼比较大,具有较好的抗震减震能力,能够替代砂土成为一种回填材料。     

基于体能消耗规律的游憩道路规划建设

随着当前社会经济的进步,我国旅游行业发展极为迅速,人们休闲意识的提升,旅游以及游憩活动在人们日常生活中逐步增多,而游憩作为人们旅游的重要组成内容,游憩道路必须结合人们体能消耗规律进行实时规划建设,虽然目前人类交通方式的多元化使得游憩活动空间障碍得以有效减少,但徒步游览仍是大部分观光型景区及游憩区的主要活动方式,这个过程中人们游憩道路过长,体能消耗所带来的疲劳感往往会导致其自身游览满意度降低,甚至对自身健康带来一定安全隐患,且老人和幼儿等特殊人群活动时间及活动方式有限,也使其对游憩道路要求更高。对基于体能消耗规律的游憩道路规划建设,进行分析探讨,并对其做相应整理和总结。     

城市道路网可行性研究中CIVIL 3D应用浅谈

以武汉航空企业总部区域市政配套工程(四期)为例,采用AutoCAD Civil 3D计算道路和场地土石方,并与传统设计软件EICAD进行了比较。对Civil 3D的运用进行了简单的探索。     

宜昌市夜明珠路总体方案研究

结合宜昌BRT(快速公交系统)建设对宜昌市夜明珠路进行改造,受众多因素控制,道路改造难度较高。介绍了工程建设条件及总体设计方案。总体方案结合路线两侧现状地形、控制因素等不同特点,将路线分为沿河线段和合流线段两个典型路段,并进行方案比选,综合各方面因素,得出了推荐方案。     

浅埋暗挖地铁隧道施工监测方法

文章以重庆市地铁一号线大坪车站临时施工便道为例,对在浅埋、软岩条件下既有隧道拆除采用工字钢支顶、风镐破岩,新建隧道采用台阶法环形开挖预留核心土、人工非爆破开挖的施工方法,通过现场监控量测,得出最大地表沉降仅为3.82mm,表明采用合理的施工方法可以减少地表沉降。     

扎（木）墨（脱）公路病害分析与处治

文章结合扎墨简易公路便道整修工程实践,介绍了扎墨公路沿线地质的工程特性、分类以及该路线上的公路病害情况,提出了相应的病害处治措施及在扎墨公路沿线区域进行公路工程施工应注意的问题。     

旧路加宽路面纵向开裂原因分析与防治

针对公路改建工程中出现的沥青路面纵向开裂这一常见现象,结合省道改建工程的设计特点与施工要点,重点就加宽路基部位与路面各结构层的施工质量控制方面进行分析和阐述,提出施工控制方法及相应的处理措施。     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.