马尔柯夫灰色残差模型在剐构桥控制中的应用

文章结合罗天乐特大桥的马尔柯夫灰色残差模型预测实例,介绍马尔柯夫灰色残差模型在线性控制中的原理、计算及应用过程,并根据实际监控经验提出了建议。罗天乐特大桥的具体监控应用实践表明,马尔柯夫灰色残差模型预测精度高,能较好地应用于大跨连续刚构桥的施工线形控制。     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

A model for the visualization and analysis of elevator traffic

ABSTRACT

Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.     

基于FLAC3D的高填方路堤通道涵洞受力特性数值分析

文章以厦蓉高速公路广西境内灌阳至全州段K13＋710通道涵洞为工程实例,利用FLAC3D（快速拉格朗日分析）软件对高填方路堤中的顶板通道进行数值仿真分析,总结了高填方路堤通道涵洞的受力特性,并提出了通道涵洞设计施工中应注意的问题。     

往复式压缩机气阀密封面失效分析

气阀是压缩机中发生故障最频繁的部件,故障会直接导致机组排气压比失调、排温高等现象,很大程度上影响企业安全生产及设备的使用寿命.文中对压缩机气阀结构及其故障特征进行了简要阐述,并通过实例分析气阀的故障特征,结合HYSYS计算出工艺气压缩过程中的各组分相图,分析了压缩工艺中气带液的产生过程,总结概括了压缩机气阀密封面失效的...     

岩溶隧道瞬变电磁三维超前探测技术研究

针对岩溶发育地区隧道开挖时现有地质预报技术对岩溶突水较难做出准确预测的问题,设计出大断面三维多匝小回线瞬变电磁探测技术。依据岩溶发育特点,采用数值模拟方法对隧道掌子面前方25 m和50 m处不同体积富水溶洞进行模拟,研究掌子面前方不同距离和规模的低阻异常体瞬变电磁响应特征。基于数值模拟结果,在张吉怀铁路古丈隧道岩溶发育段进行探测验证,结果表明： 1）溶洞模型在二维视电阻率等值线断面图上25 m和50 m处形成闭合、视电阻值小于60 Ω·m的低阻区,低阻区与设计的模拟体的位置和范围一致; 2）大断面三维多匝小回线瞬变电磁探测三维显示结果与现场实际揭露情况基本吻合,采用该技术能够准确预报掌子面前方17~30 m、隧道两侧20 m到隧道中轴线的充泥含水溶洞。     

管线敷设与风机室布置对综合管廊通风阻力影响研究

为准确估算管线敷设与风机室内气流相互作用对压降损失的影响,基于武汉市江夏区谭鑫培路地下综合管廊项目建立典型数值分析模型与分析流程。通过建立典型管线布置的GIL舱、高压舱与综合舱的计算流体动力学三维模型,分别对入口段与稳定段进行分析,并使用循环边界条件来考虑稳定段流体的充分发展,得到阻力系数随流量的变化规律;通过建立GIL舱典型排风口三维模型,分析风机室中不同风机的压降损失,并与规范估算值进行比较。研究结果表明： 管线敷设对压降损失的影响不可忽视;风机室风机相互作用显著,规范不能准确估算该相互作用导致的压降损失。     

丽香铁路黄山哨隧道下穿岩堆段设计施工关键技术

以下穿岩堆段的丽香铁路黄山哨隧道为工程依托,对岩堆段地表开裂及洞内初期支护边墙严重变形的问题进行研究。地表埋设6根测斜管监测地表位移情况,洞内布置3个断面进行围岩压力、钢架内力、二次衬砌内力、初期支护与二次衬砌间的接触压力、锚杆轴力量测。在分析现场岩堆段洞内外受力机制及原因的基础上,根据数值计算结果优化二次衬砌断面型式及进一步加大二次衬砌厚度及配筋。采取以下措施控制隧道岩堆段变形： 1）地表岩堆土石接触面开裂处增设截排水措施; 2）加大隧道初期支护钢架型号及加长岩堆侧边墙径向系统锚杆; 3）加大隧道边墙轮廓曲率并优化隧道二次衬砌型式为圆顺型; 4）隧道预留变形量加大至30 cm; 5）隧道二次衬砌内净空预留50 cm补强空间; 6）隧道拱部设置42小导管超前支护。现场岩堆段采取以上措施后已顺利施工通过,根据洞内外监测结果显示,结构在安全可控范围内。     

Daily activity and travel sequences of students,faculty and staff at a large Canadian university

This paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.     

Simulating ad-hoc demand-responsive transportation: a comparison of three approaches

Transport models are used to evaluate new infrastructure and public transport services, varied levels of demand, and new ideas for demand management. Exploring these proposals virtually is easier than implementation and testing in situ. However, existing models are based around traditional forms of transportation. As part of a feature analysis using a case study approach, three different simulation packages (a simple custom-developed package, traffic microsimulation, and agent-based simulation) are used to develop and demonstrate simulations of demand-responsive transportation (DRT) and analyze the advantages and disadvantages of each simulation approach for evaluating DRT. While the simulations display some relational replication (meaning they produce similar relational patterns with respect to certain variables), they do not show distributional replication (that is, the value of the results is not statistically similar), meaning that under- or over-estimation of predicted travel could occur. Recommendations for the application of each modeling approach are made.