基于ASP.NET技术的围岩分级系统开发

文章介绍了开发的基于互联网的围岩分级系统。该系统以ASP.NET技术为基础,结合了数据库技术、支持向量机技术、MATLAB接口等技术,实现了掌子面围岩信息的编录存储、围岩级别的智能判别等功能,并能使设计、专家、业主等人员通过互联网就可以及时了解到掌子面的围岩状况,对围岩级别进行综合判定,提高了隧道施工的信息化程度。     

硫酸盐侵蚀环境下隧道结构病害等级研究

针对我国西南地区硫酸盐侵蚀环境下隧道结构病害的具体特征,结合国内外既有的隧道病害等级划分方法,将硫酸盐侵蚀环境下隧道结构病害等级划分为四级,对各级区间的划分界限进行了具体描述,并给出了相应的定量划分方法.     

基于高斯过程机器学习方法的隧道围岩分类模型

针对现有围岩分类方法的局限性,基于工程实例,利用分类性能优异的高斯过程机器学习模型建立围岩类别与其主要影响因素之间的非线性映射关系,进而提出一种基于高斯过程的隧道围岩分类模型,实现不同情况下围岩分类的合理识别.将该模型应用于川藏公路二郎山隧道围岩分类,研究结果表明,隧道围岩分类的高斯过程机器学习模型是科学可行的,与人工神经网络模型、支持向量机模型相比较,该模型具有参数自适应化的优点,能方便快捷地给出合理可靠且具有概率意义的围岩分类评价结果,可对围岩分类结果的不确定性或可信度进行定量化评价.     

特殊隧道围岩的分级方法探讨

为探讨黄土、膨胀土、断层破碎带等特殊隧道围岩的分级方法,通过对目前国内外的研究进行总结,得到能反应特殊隧道围岩的特性的指标,采取不同的指标组合进行综合研究对其进行分级。研究认为：黄土可按地质年代、天然含水量、干密度进行分级;膨胀土可按塑性指数、自由膨胀率、标准吸湿含水率进行分级;断层破碎带可按地质力学特性进行分级。     

内河航道定级与船舶大型化发展

随着我国经济的进一步发展和可持续发展战略的实施,运能大、占地面积小、能耗低、污染少的内河航运必将发挥出更大的作用。此外,内河船舶标准化、大型化的发展趋势日趋明显,小吨位船舶的运能及经济性劣势迫使其快速 淘汰,不少地区早年的航道定级已然不适应内河航运的发展需求。若城市的开发建设按不合理的航道定级标准进行规划控制,将极大地限制内河航运的发展,并对船舶的航行构成重大安全隐患。结合实际情况,探讨长三角地区内河航道定级调整的若干设想,并提出相关建议。     

基于分类投资建设的铁路分类标准探讨

国发〔2013〕33号文提出对新建铁路实行分类投资建设,明确了具体放开的铁路类型,但缺乏具体判别标准。本文结合铁路项目公益性或经营性属性,在对既有铁路根据运输性质进行分类统计研究的基础上,从利于分类投资建设出发,对铁路划分类别及其标准进行初步探讨,重点量化了干、支线分类和经营性公益性分类,提出经营性公益性分类以经济评价作为主要指标,干、支线铁路以在路网中作用、运量、速度目标值、线路连接、线路长度等5个主要指标进行界定。     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

Suspension system state estimation using adaptive Kalman filtering based on road classification

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.     

Fault classification method for the driving safety of electrified vehicles

A fault classification method is proposed which has been applied to an electric vehicle. Potential faults in the different subsystems that can affect the vehicle directional stability were collected in a failure mode and effect analysis. Similar driveline faults were grouped together if they resembled each other with respect to their influence on the vehicle dynamic behaviour. The faults were physically modelled in a simulation environment before they were induced in a detailed vehicle model under normal driving conditions. A special focus was placed on faults in the driveline of electric vehicles employing in-wheel motors of the permanent magnet type. Several failures caused by mechanical and other faults were analysed as well. The fault classification method consists of a controllability ranking developed according to the functional safety standard ISO 26262. The controllability of a fault was determined with three parameters covering the influence of the longitudinal, lateral and yaw motion of the vehicle. The simulation results were analysed and the faults were classified according to their controllability using the proposed method. It was shown that the controllability decreased specifically with increasing lateral acceleration and increasing speed. The results for the electric driveline faults show that this trend cannot be generalised for all the faults, as the controllability deteriorated for some faults during manoeuvres with low lateral acceleration and low speed. The proposed method is generic and can be applied to various other types of road vehicles and faults.     

高分辨率罗兰C天波延迟估计新技术

提出了一种基于特征分解的多信号分类算法估计罗兰C接收机天波延迟的信号处理新技术。它为接收机基准点的实时设置提供了一种新方法。常规接收机为了防止天波干扰，将基准点设置在一个固定位置上，导致基准点处信噪比受包络限制而较低，从而大大增加了对准基准点的时间。利用这项技术，我们在低信噪比条件下分离出了地波和天波的到达时刻。这就使得接收机能根据天波延迟变化实时选择基准点最佳位置，并能利用地波到达时刻进行周期选择。由于此法有助于增加基准点处的信噪比，减少对准基准点的时间，因而能极大地提高现有罗兰C接收机的性能。还显示了新方法较IFFF方法有更高的分辨率。