极端冰雪条件下的顺层岩质边坡滑移稳定性分析

推导了典型岩石边坡在极端冰雪条件下的滑移稳定系数的表达式;通过计算分析,揭示了岩石边坡滑移稳定系数随裂隙内水深、坡高、坡角、滑面倾角等因素变化的规律及与冻深的关系,并绘制岩石边坡滑移稳定系数与边坡几何要素之间的关系图.研究表明,当考虑极端冰雪灾害影响时,岩石边坡滑移稳定系数发生较为明显的变化:随裂隙饱水程度、坡面角、主...     

建筑抗震设计地震作用折减系数的取值方法

地震作用折减系数在建筑抗震设计中是政策性、技术性和经济性的综合指标。本文以钢筋混凝土框架结构为例,从技术上介绍了地震作用折减系数的确定原则。在简要介绍我国建筑抗震设计规范设计地震作用—延性等级组合运用中存在不足的基础上,论述了地震作用折减系数三个分量的基本特点,并分四个步骤介绍了其计算取值方法。推荐方法可为建筑抗震设计确定合理的地震作用折减系数提供有益的参考。     

受侧向土体位移斜桩的特性

通过三维有限单元法分析了斜桩受侧向土体位移的特性。变动柱和土体参数进行敏感性分析,得出桩的柔度、侧向土体位移的大小、桩顶约束条件、土体位移形状和土体移动层厚度等对斜桩的影响。刚性桩的挠度小于柔性桩,但弯矩和剪力大于柔性桩。斜桩非线性弹簧的土抗力—位移关系(p-y曲线)表现为双曲线特征,可基于直桩的Winkler地基反力法对斜桩进行简化计算分析,但应对直桩的极限土抗力值进行修正,以期更好的反映斜桩的特性。     

家庭部门生活能源消费碳排放:测度与驱动因素研究

本文运用IPCC的二氧化碳排放量测算方法,在省际层面测度了我国家庭部门直接能源消费碳排放,并基于扩展的STIRPAT和Kaya模型,构建家庭部门直接能源消费碳排放影响因子动态面板数据模型,对我国2003-2012年分省面板数据样本及城乡子样本进行系统GMM估计。本文研究表明,第一,我国城乡家庭部门碳排放总量和人均碳排放在近十年都呈快速上升趋势,家庭部门碳排放的空间分布具有明显的地域差异特征,高碳排放地区主要集中在东、中部地区,西部地区的碳排放水平较低;第二,城乡家庭部门生活能源消费的上期碳排放量对本期碳排放产生重要的正向影响,这反映出我国家庭部门碳排放具有显著的惯性特征和路径依赖性,是一种动态自适应机制;人口规模、居民消费水平、能源消费结构、碳排放强度、能源消费强度和城镇化因素,都对我国居民能源消费碳排放总量及人均碳排放具有显著的影响,城乡之间的家庭能源消费碳排放驱动因素存在差异。本文研究得到如下启示及政策含义:家庭部门碳减排将是一个有步骤、分区域的渐进过程,我国碳减排政策应当兼顾消费升级和碳排放的双重目标,努力构建分层次碳减排的适应性预期机制。具体而言,一方面应着眼于引导和激励居民低碳消费,缓解人口规模增加、消费水平提升和城镇化对家庭部门碳减排的压力;另一方面要通过能源价格改革、财政政策和环境规制政策等优化我国能源消费结构,不断降低煤炭消费比重,提高清洁能源的消费。同时,通过技术创新、设备改造等科技手段提高煤炭利用效率,降低碳排放强度,这些政策将更有利于城镇家庭部门碳减排。     

2000~2014年武汉市城市扩展时空特征分析

基于不同时相的高分辨率遥感影像,采用面向对象的解译方法提取了武汉市2000、2005、2010和2014年的土地覆被信息,从城市扩展强度指数,城市中心坐标迁移和分形维数等方面分析了武汉市2000～2014年城市扩展时空特征。结果表明：2000～2014年间,武汉市城市扩展强度指数为1.41,各主城区城市扩展强度不一,洪山区建设用地的增加对主城区城市扩展的贡献最大;在扩展过程中,武汉市城市分形维数增加,城市空间形态变复杂;其扩展呈现核心-放射扩展模式,并逐渐转向圈层式;自然条件、经济、人口、交通、政策和城市规划是武汉市城市扩展的主要驱动力,但随着交通的发展,自然条件和经济对武汉市城市扩展的驱动作用正逐渐减弱。     

南方丘陵地区河谷城市用地时空演变与驱动分析——以上饶市城区为例

以揭示中国南方丘陵地区河谷城市的空间演变过程和特征为目的,以上饶市城区为例,在遥感与GIS技术的支持下,对1988~2013年间上饶市城区城市用地的时空演变过程、特征及驱动影响因素进行了研究。结果表明,1988~2013年的25 a间,上饶市城区城市用地呈现出快速增长的态势,城市形态的演变具有明显的时空特征:时间序列上,不论扩展幅度还是扩展速度,2001~2013年间的值均高于1988~2001年间的值,前后两个时段的扩展特征呈现出明显的差异性;空间格局上,上饶市城区以老城区的中心广场为中心向四周扩展,先后经历了放射性扩展、组团式分散扩展两个阶段,同时呈现出明显的方向分异性,西南和西为其主要扩展方向,而其东南方向的扩展速度最为缓慢。自然环境中的地貌对上饶市城区城市用地的扩展起到很大的限制作用,经济社会的发展、政策规划等因素成为城市用地扩张与时空演化的外在驱动因素。     

智能手机应用程序使用与操作的驾驶分心风险影响研究

为研究智能手机应用程序操作方式与使用行为对驾驶分心影响的问题,探讨智能手机程序操作方式对驾驶分心影响的优劣关系,基于结构方程模型提出4个因果关系假设,构建涵盖程序使用、驾驶分心、驾驶绩效等潜变量的智能手机使用行为影响的结构方程模型。通过收集线下问卷的方式进行调查,搭建模拟场景,设计6项实验方案,通过驾驶模拟实验方式收集相关数据,建立相对偏差矩阵。研究结果表明：手机通话功能的使用(路径系数0.472)对驾驶分心影响显著,手机导航功能使用(路径系数0.256)、手机音乐功能使用(路径系数0.249)对驾驶分心影响不显著。语音交互方式均优于手动操作方式,其中语音交互启动导航方式(F₃=0.019)的影响最小。研究结果可对道路驾驶情况下智能手机应用使用与操作方式的研究起推动作用。     

A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.     

Introducing a multi-variate classification method: Risky driving acceptance among different heterogeneous driver sub-cultures

Introduction: Heterogeneous driving populations with many different origins are likely to have various sub-cultures that comprise of drivers with shared driver characteristics, most likely with dissimilar traffic safety cultures. An innovative methodology in traffic safety research is introduced which is beneficial for large datasets with multiple variables, making it useful for the multi-variate classification of drivers, driving attitudes and/or (risky) driving behaviours. Method: With the application of multidimensional scaling analysis (MDS), this study explores traffic safety culture in the State of Qatar using a questionnaire and investigates the similarity patterns between the questionnaire items, aiming to classify attitudes towards risky driving behaviours into themes. MDS is subsequently applied to classify drivers within a heterogeneous driving sample into sub-cultures with shared driver characteristics and different risky driving attitudes. Results: Results show that acceptance of speeding is highest among the young Arabic students and acceptance of distraction and drivers’ negligence such as phone use and not wearing a seatbelt is highest among male Arab drivers. Acceptance of extreme risk-taking like intoxicated driving and red-light running is highest among South-Asian business drivers. Conclusion: It is important and practical to understand risky behavioural habits among sub-cultures and thereby focussing on groups of drivers instead of individuals, because groups are easier to approach and drivers within sub-cultures are found to influence each other. By indicating which groups of drivers are most likely to perform specific risky driving themes, it is possible to target these groups and effectively emphasise certain subsets of risky driving behaviours during training or traffic safety education. Practical Applications: This study provides guidance for the improvement of driver education and targeted traffic safety awareness campaigns, intending to make changes to attitudes and habits within specific driver sub-cultures with the aim to improve traffic safety on the longer term.     

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.