公路地基真空排水预压法探讨

真空排水预压法与堆载预压法相比具有加载快、无需堆载材料、不会失稳等优势,适用于加固软土特别是含水量较高的超软土地基。真空-堆载联合预压法的应用也越来越多。经过国内外十几年的探索,真空排水预压法日趋成熟,已经成为大面积应用的加固软土地基的行之有效的方法之一。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

桥梁单板受力的原因及解决措施

随着交通量的迅猛增长,桥梁单板受力情况越来越频繁,通过对单板受力现象产生原因进行分析,提出解决措施,以供同行参考和借鉴。     

高职院校公共《计算机基础》课程实践教学研究

对高职院校公共《计算机基础》实践教学效果及其存在的问题进行剖析,针对这些问题所采取的应对措施进行了探索和研究。提出加强实践教学改革的方案和具体方法,使高职院校的计算机基础教育能更好地与市场接轨。     

浅埋顶管沉井周边土体的三维破坏形状及沉降研究

为研究顶管沉井及周边土体在顶进力作用下的形变范围与大小，以加固旋喷桩作为沉井周围加固体建立ABAQUS软件计算模型，并将模拟结果与工程实例、PLAXIS 3D软件所得结果进行对比分析。同时，采用广州地区花岗岩残积土进行室内沉井模型试验，并通过试验结果对数值模拟结论进行验证，得出结论如下： 1）在开挖基坑前达到设计强度的旋喷桩能有效减小基坑周边土体塌陷变形、坑底隆起。2）在施加工作顶推力下，前侧主动区首先出现贯通裂缝，竖向位移斜率增大出现明显拐点；继续增大顶推力，土体位移会急剧增大导致地表沉降严重。3）对于后背土体，在顶推力作用下，地表破裂线的切线角度从0°逐渐增大至45°+φ/2，导致破裂范围也不断扩大；达到45°+φ/2处后，继续施加顶推力会导致短轴方向破坏范围扩大的速度较长轴方向的速度更快；三维空间中被动区破坏土体在地表处产生形状为椭圆的破坏面，椭圆长轴方向为顶推轴线方向，被动破坏体呈现为牛角状椭圆楔体。     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.     

Estimation of delay variability at signalized intersections for urban arterial performance evaluation

Urban arterial performance evaluation has been broadly studied, with the major focus on average travel time estimation. However, in view of the stochastic nature of interrupted flow, the ability to capture the characteristics of travel time variability has become a critical step in determining arterial level of service (LOS). This article first presents a stochastic approach that integrates classic cumulative curves and probability theories in order to investigate delay variability at signalized intersections, as a dominant part of the link travel time variability. This serves as a basis for arterial travel time estimation, which can be obtained through a convolution of individual link travel time distributions. The proposed approach is then applied in the estimation of travel time along one arterial in Shanghai, China, with abundant automatic vehicle identification (AVI) data sources. The travel time variability is evaluated thoroughly at 30-min intervals, with promising results achieved in comparison to the field measurements. In addition, the estimated travel time distributions are utilized to illustrate the probability of multiple LOS ranges, namely, reliability LOS. The results provide insights into how we might achieve a more reliable and informative understanding of arterial performance.     

基于OWA与灰色聚类的城市轨道交通PPP融资风险评价方法研究

针对城市轨道交通PPP融资成本高、投资回收期长、不确定性因素多等特点导致整个融资过程充满风险,且难以进行科学的评价,进而增加风险评价难度的问题,提出基于OWA与灰色聚类的城市轨道交通PPP融资风险评价方法。首先从全寿命周期角度构建城市轨道交通PPP融资风险指标体系。然后基于OWA算子对专家决策数据按照降序的方式重新排序,消除极值带来的不利影响,加权得出指标的权重值。最后考虑指标的灰色性、信息不完整性,构建灰色白化权函数实现指标信息的透明化,完成对PPP融资风险的聚类分析。应用构建的模型对郑州地铁1号线一期工程PPP融资风险进行评价,认为该地铁工程PPP融资风险等级高,应重点关注前期策划、社会资本、政策环境、设计质量、成本超支、建设质量、残值7个主要风险指标的控制,以期为该项目融资风险管理提供建议,并进一步丰富城市轨道交通PPP融资风险的评价方法。     

基于SHEL和三角模糊数理论的地铁钻爆法施工安全评价方法研究

为合理应用决策数据中不同专家的背景知识,找出地铁施工过程中安全测评体系的关注重点,根据地铁施工的特点和SHEL安全模型理论,建立以人-软件(L-S)、人-硬件(L-H)、人-环境(L-E)和人-人(L-L)4个环节为基础的钻爆法地铁施工安全指标体系。请专家给出指标语义评价等级和指标权重语义评价等级,并根据其与三角模糊数的对应关系分别得出三角模糊数评价值。针对不同专家的重要性程度差异,提出基于三角模糊数的群一致性评价方法,得出专家综合权重,集结评价数据,最终得到安全评价结果。最后,对青岛地铁1号线过海段进行实例分析。结果表明,该方法可以解决群评价过程中专家个体权威与群体共识难以兼顾的问题,能够充分利用群决策中的原始评价信息,提高地铁施工安全水平评价的准确性。     

Magneto-rheological suspensions for improving ground vehicle’s ride comfort,stability, and handling

ABSTRACT

A state-of-the-art discussion on the applications of magneto-rheological (MR) suspensions for improving ride comfort, handling, and stability in ground vehicles is discussed for both road and rail applications. A historical perspective on the discovery and engineering development of MR fluids is presented, followed by some of the common methods for modelling their non-Newtonian behaviour. The common modes of the MR fluids are discussed, along with the application of the fluid in valve mode for ground vehicles’ dampers (or shock absorbers). The applications span across nearly all road vehicles, including automobiles, trains, semi-trucks, motorcycles, and even bicycles. For each type of vehicle, the results of some of the past studies is presented briefly, with reference to the originating study. It is discussed that Past experimental and modelling studies have indicated that MR suspensions provide clear advantages for ground vehicles that far surpasses the performance of passive suspension. For rail vehicles, the primary advantage is in terms of increasing the speed at which the onset of hunting occurs, whereas for road vehicles – mainly automobiles – the performance improvements are in terms of a better balance between vehicle ride, handling, and stability. To further elaborate on this point, a single-suspension model is used to develop an index-based approach for studying the compromise that is offered by vehicle suspensions, using the H₂ optimisation approach. Evaluating three indices based on the sprung-mass acceleration, suspension rattlespace, and tyre deflection, it is clearly demonstrated that MR suspensions significantly improve road vehicle’s ride comfort, stability, and handling in comparison with passive suspensions. For rail vehicles, the simulation results indicate that using MR suspensions with an on-off switching control can increase the speed at which the on-set of hunting occurs by as much as 50% to more than 300%.