基于专家系统和证据理论的路面破损原因评价方法研究

路面破损原因评价是一项经验性很强的工作,通过采用专家系统技术,利用路面养护专家经验和知识形成产生式规则,并对单处路面破损原因进行评价。根据对单处路面破损原因的评价结果中也包含了导致整个路段路面破损主要原因的信息这一特点,介绍了引入证据理论将这方面信息提取出来,合成得出对整个路段的主要破损原因评价的方法。     

路堤荷载下管桩复合地基设计理论的试验研究

介绍了管桩复合地基在某高速公路软基试验工程中的应用,并简单分析了其加固效果,进而结合已有的研究成果,提出了一套路堤下刚性桩复合地基的设计理论。通过工程实践表明,该设计理论具有在工程实践中推广运用的价值。     

A regression and beam theory based approach for fatigue assessment of containership structures including bending and torsion contributions

Container shipping has been expanding dramatically during the last decade. Due to their special structural characteristics, such as the wide breadth and large hatch openings, horizontal bending and torsion play an important role to the fatigue safety of containerships. In this study the fatigue contributions from vertical bending, horizontal bending and torsion are investigated using full-scale measurements of strain records on two containerships. Further, these contributions are compared to results from direct calculations where a nonlinear 3D panel method is used to compute wave loads in time domain. It is concluded that both bending and torsion have significant impacts on the fatigue assessment of containerships. The stresses caused by these loads could be correctly computed by full-ship finite element analysis. However, this requires large computational effort, since for fatigue assessment purposes the FE analysis needs to be carried out for all encountered sea states and operational conditions with sufficient time steps for each condition. In this paper, a new procedure is proposed to run the structure finite element analysis under only one sea condition for only a few time steps. Then, these results are used to obtain a relationship between wave loads and structural stresses through a linear regression analysis. This relation can be further used to compute stresses for arbitrary sea states and operational conditions using the computed wave loads (bending and torsion moments) as input. Based on this proposed method for structure stress analysis, an efficient procedure is formulated and found to be in very good agreement with the full-ship finite element analysis. In addition it is several orders of magnitude more time efficient for fatigue assessment of containership structures.     

Closed-form multiclass cell transmission model enhanced with overtaking,lane-changing,and first-in first-out properties

A novel multiclass macroscopic model is proposed in this article. In order to enhance first-in, first-out property (FIFO) and transmission function in the multiclass traffic modeling, a new multiclass cell transmission model with FIFO property (herein called FM-CTM) is extended from its prior multiclass cell transmission model (M-CTM). Also, to enhance its analytical compactness and resultant computational convenience, FM-CTM is formulated in this paper as a set of closed-form matrix equations. The objective is to improve the accuracy of traffic state estimation by enforcing FIFO property when a fast vehicle cannot overtake a slow vehicle due to a limitation of a single-lane road. Moreover, the proposed model takes into account a different priority for vehicles of each class to move forward through congested road conditions, and that makes the flow calculation independent from their free-flow speeds. Some hypothetical and real-world freeway networks with a constant or varying number of lanes are selected to verify FM-CTM by comparing with M-CTM and the conventional CTM. Observed densities of VISSIM and real-world dataset of I-80 are selected to compare with the simulated densities from the three CTMs. The numerical results show that FM-CTM outperforms the other two models by 15% of accuracy measures in most cases. Therefore, the proposed model is expected to be well applicable to the road network with a mixed traffic and varying number of lanes.     

地下工程注浆技术研究新进展

注浆是土木、水利和矿山等工程建设中重要的防渗和加固技术。文章基于国内外注浆技术研究文献,总结了水泥等颗粒性浆液的“渗滤效应”及“黏度时空变异性”等理论研究成果,论述了非水反应高聚物、CW环氧树脂和微生物菌液等注浆新材料的应用现状及发展前景,介绍了细观力学数值模拟研究的突破性进展,可为地下工程注浆技术发展提供借鉴和启发作用。     

基于家庭属性差异的大学生出行方式选择行为研究

为剖析家庭属性差异对大学生出行方式选择行为的影响，基于非集计理论，构建家庭属性差异的大学生出行选择多元Logit 模型. 根据四川省2 571 份大学生出行行为调查问卷，运用SPSS 软件标定模型参数，获取影响大学生出行选择的主要家庭属性因素，并进行敏感性分析. 结果表明：家庭平均年收入、经济净流对大学生出行方式选择有显著的影响；以航空运输为参考，家庭平均年收入、经济净流对公路运输方式选择的影响大于铁路运输；“祖辈替孙辈购买机票”的折扣票务形式可提高大学生选择航空出行的概率.     

非收放式减摇鳍鳍轴校核算法研究

非收放式减摇鳍作为减摇鳍产品中非常重要的一个组成部分,而鳍轴也是其减摇力矩传力链中非常重要的一环,对于鳍轴综合应力的校核也就成了非收放式减摇鳍设计中的关键。通过比较考虑剪力的新算法以及仅做弯扭综合应力校核的2种算法,及它们在不同强度理论下的状态,综合讨论得出能使鳍轴应力值更加精确的算法。     

基于自主学习模式的精品课程共享资源后续建设研究

从当前我国高等学校精品课程的建设及应用现状出发，分析了不同自主学习模式对教育机构提供的精品课程或资源共享课程的需求，提出了基于自主学习模式的精品课程网络共享资源后续建设项目与标准建议。     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.