西南红层软岩地区铁路滑坡形成机理分析及治理措施

红层软岩地区铁路工程建设中滑坡问题突出。本文结合西南地区一铁路红层滑坡治理工程,利用该地区红层滑坡的现场调查结果,从红层软岩特性、地层岩性、地质地貌、水文地质条件、深孔位移监测数据等多方面对该地区铁路滑坡形成机理进行分析,并论述红层滑坡与一般滑坡的不同点,分析红层软岩岩体力学特性、地层主要成分以及红层软岩中的粉砂质泥岩在不同风化程度下的力学特性。根据分析结果提出治理措施,并对治理措施进行评价。总结得出地层岩性、地质水文、人为因素是导致红层滑坡病害发生的主要因素。     

船舶企业农民工稳定措施研究

分析当前船舶企业用工短缺现象,认为企业应当改变思路,完善机制,充分了解尊重并充分考虑当前农民工的需求,采取稳定农民工的措施。     

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.     

F型与直壁型护栏在客车撞击下的仿真研究

为了对比研究A类防撞等级中F型混凝土护栏和直壁型混凝土护栏的防护性能,建立两种护栏与客车的有限元模型,运用ANSYS Workbench软件中显式动力学模块,控制仿真碰撞的试验参数,将大客车模型以规定的初速度和碰撞角度进行计算机仿真碰撞试验,从车辆的加速度、车辆运行轨迹、护栏的最大动态变形量等方面综合对比两种护栏的防护性能。结果表明:相比直壁式护栏,F型混凝土护栏防撞性能、安全性能、导向性能较好。     

Towards a model of hydrodynamic damping for a circular cylinder with helical strakes at low KC

This paper investigates the hydrodynamic damping of a circular cylinder with helical strakes at Keulegan-Carpenter (KC) number from 0.07 to 3 in the presence of steady currents. Experiments were performed with a straked cylinder oscillating in either in-line or cross currents over Reynolds number (based on the oscillating velocity amplitude) varying from 1260 to 54,000. With in-line current being present, the measured drag coefficients of the straked cylinder are found to depend on the ratio between the oscillating velocity amplitude and the steady current velocity. This phenomenon is further confirmed by computational fluid dynamics using large-eddy simulations. The drag coefficients obtained from the numerical simulations agree well with the experimentally determined values. Similar phenomenon is observed for the cases with cross background current. Based on the experimental data, empirical formulae are proposed to evaluate drag coefficients. These results are of importance in estimating the resonant motion and the fatigue life of risers, e.g. water intake risers, in the flow regime of low KC. Finally, recommendations are provided for fatigue analysis of risers with helical strakes from the perspective of engineering practice.     

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.     

基于AR模型的并靠两船相对运动预报研究

根据两船相对运动的特点,利用两船模相对运动的测量数据,运用基于自回归模型的时间序列分析法,建立了两船相对运动的数学模型,并给出了运动姿态的预报值.通过本方法的研究,可以得到满意的相对运动预报精度,为两船补给波浪、补偿装置的开发打下了理论基础.     

软弱富水地层浅埋暗挖隧道地表沉降和支护结构受力的模型试验

以莞惠城际轨道交通工程项目GZH-5标段中DK32+300~DK32+927.303段为背景,通过2组模型试验,其中一组无超前注浆,另一组进行超前注浆,研究软弱富水地层浅埋暗挖隧道地表沉降和支护结构受力规律及超前注浆对其受力变形造成的影响,最后,针对软弱富水地层浅埋暗挖隧道施工,提出几点隧道工程设计和施工的相关建议。     

Experimental study and modeling of car-following behavior under high speed situation

To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.