沥青路面破损程度分级与破损换算系数取值的探讨

本文分析现行规范中对沥青路面破损程度、密度分级以及破损换算系数取值中存在问题,通过分析沥青路面破损程度与路面使用状况关系,结合沥青路面养护措施,提出沥青路面裂缝破损密度分级的方法,并对车辙破损严重程度分级标准、裂缝和车辙破损换算系数的修正值提出建议。     

低剂量水稳基层防治薄面层沥青路面反射裂缝研究

采用低剂量水泥稳定碎石基层作为防治薄面层沥青路面反射裂缝的主要方案,利用室内试验和试验路工程分别得到不同剂量水泥稳定碎石基层的收缩特性以及强度发展规律。力学分析和观测结果都证明该方法可以有效防治薄面层沥青路面反射裂缝。     

基于实测数据评估交通事件检测中神经网络应用性的研究

神经网络为交通事件自动检测技术摆脱传统方法探测率低、误报率高的状况提供了新的解决思路。在以往局限于仿真数据研究的基础上,本文利用I-880数据库实测交通事件数据对神经网络在交通事件自动检测中的实际应用进行了研究,结果表明:神经网络应用于交通事件自动检测技术中,具有较高的探测率和较低的误报率,但该算法可移植性较差,在实际应用中要予以考虑。     

基于视觉的前方车辆探测技术研究方法综述

对国内外关于车道上本车前方车辆检测方法进行了综述。根据探测车辆所采用的传感器不同,分别对采用单目视觉、双目视觉、视觉与其他传感器融合以及非视觉传感器的几种探测方法进行了比较与分析。同时,针对每种探测方法,进一步阐述了目前车辆识别的一些具体算法。最后,指出了每种方法存在的优点和一些不足。     

水泥混凝土路面质量病害问题探讨

根据目前水泥混凝土路面建设比较普遍的情况,针对施工中出现比较多的质量病害进行了分析,分别从水泥混凝土路面的裂缝产生的过程,材料、配合比、水灰比、施工操作等对质量的影响,以及产生外观质量缺陷的原因进行了评述.     

灌注混凝土桩施工方法及工艺的探讨

通过对钻孔灌注桩与沉入桩的对比分析,得出了钻孔灌注桩的优点,并且详细介绍了钻孔灌注桩的施工工艺和方法,总结了提高灌注混凝土桩承载力的方法和灌注桩的检测手段及一般事故处理方法,以供今后类似工程实践参考.     

沥青路面填封裂缝和修补坑槽再破损原因分析

为了检验沥青路面裂缝填封和坑槽修补的效果,对填封裂缝和修补坑槽较常出现的再破损形式进行了归纳,并深入地分析了已填封裂缝和已修补坑槽再次出现破损的原因.     

大直径盾构管片水平拼装试验方法探究

大直径盾构管片水平拼装试验存在效率低下甚至失败的情况,不能真实全面反映产品尺寸精度。分析其原因除大直径管片体积大、重量大的因素外,主要原因在于现有的试验设备工装简陋、试验方法粗放、规范要求低等。结合当前水平拼装的规范要求,分析现有试验方法存在的不足,对现有拼装设备工装进行改造,制定高标准的、更加严格的工艺措施,创造性地提出在每层管片拼装时增加标高检测的新思路。将新方法应用于南京长江第五大桥夹江隧道工程管片水平拼装试验,结果良好,证明了此试验方法的科学性与有效性。     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.     

Connectivity analysis of the global shipping network by eigenvalue decomposition

ABSTRACT

Maritime shipping necessitates flexible and cost-effective port access worldwide through the global shipping network. This paper presents an efficient method to identify major port communities, and analyses the network connectivity of the global shipping network based on community structure. The global shipping network is represented by a signless Laplacian matrix which can be decomposed to generate its eigenvectors and corresponding eigenvalues. The largest gaps between the eigenvalues were then used to determine the optimal number of communities within the network. The eigenvalue decomposition method offers the advantage of detecting port communities without relying on a priori assumption about the number of communities and the size of each community. By applying this method to a dataset collected from seven world leading liner shipping companies, we found that the ports are clustered into three communities in the global container shipping network, which is consistent with the major container trade routes. The sparse linkages between port communities indicate where access is relatively poor.