按弹性支承计算板桥支点的荷载横向分布系数

介绍了配有橡胶支座的板桥．在对其荷载横向分布分析时。考虑支座为弹性体的这一客观因素．提出了计算板桥支点处荷载横向分布影响线的计算方法。由此法计算的荷载横向分布系数小于按直接布载法算得的结果。     

城市高架桥之高独柱墩横向位移探讨

结合城市高架高独柱墩顶部发生横向位移这一现象,对高独柱墩顶部横向荷载产生的原因进行初步分析,并应用三维非线性屈曲分析,阐述组合荷载所引起的大变形及混凝土徐变效应与墩顶横向位移之间的关系,提出了设计、施工类似结构时应注意的事项。     

兰州地铁地温分布特性及其演化规律

以兰州地铁所在地区为研究对象,实测地铁隧道开挖前的地温(简称为初始地温),根据实测数据,提出地铁初始地温预测模型公式。采用非稳态传热的数值模型,分析运营条件下地铁隧道围岩温度的演化规律。结果表明:兰州地铁初始地温随环境气温和埋深的变化而变化;年变温层位于自地表至埋深12m处;年恒温层位于埋深12m及其以下,温度为15℃左右;年变温层中,1年内初始地温变化规律与环境气温变化规律相似,近似呈正弦曲线状分布,但存在相位滞后的现象;1年中初始地温的振幅随埋深的增大呈指数下降趋势。在隧道内空气与围岩之间热交换中,兰州地铁隧道围岩的温度及其梯度、热透厚度(未达到极限时)均与隧道内环境温度、热交换时间成正相关关系,但与距隧道内壁的距离成负相关关系。     

轨道复合不平顺对无缝线路横向变形的影响分析

轨道复合不平顺会对行车的安全及稳定性产生较大影响,也是影响无缝线路横向变形的一个重要因素。为研究轨道复合不平顺对无缝线路的具体影响,通过构建三维轨道框架非线性有限元模型,采用轨道框架单点(或多点)位置发生横向及竖向位移来模拟复合不平顺状态,通过计算获取节点位移变化规律,进而分析轨道复合不平顺对无缝线路横向变形的影响作用。研究结果表明,轨道的复合不平顺会对无缝线路的横向变形产生显著的影响;当线路出现三角坑等类似病害时,其节点位移变化更为显著,在无缝线路的日常养护维修中应尤为注意。     

Privacy-preserving MaaS fleet management

On-demand traffic fleet optimization requires operating Mobility as a Service (MaaS) companies such as Uber, Lyft to locally match the offer of available vehicles with their expected number of requests referred to as demand (as well as to take into account other constraints such as driver’s schedules and preferences). In the present article, we show that this problem can be encoded into a Constrained Integer Quadratic Program (CIQP) with block independent constraints that can then be relaxed in the form of a convex optimization program. We leverage this particular structure to yield a scalable distributed optimization algorithm corresponding to computing a gradient ascent in a dual space. This new framework does not require the drivers to share their availabilities with the operating company (as opposed to standard practice in today’s mobility as a service companies). The resulting parallel algorithm can run on a distributed smartphone based platform.     

横梁端部节点优化

通过有限元计算,对比几种常见的横梁端部连接方式,综合考虑结构整体强度、钢材使用量、舱室空间利用率、施工便利性等方面的影响因素,确定较优的连接过渡方式和过渡结构的参数尺寸,为船舶设计横梁端部节点形式的确定提供参考。     

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.     

Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.     

高墩连续刚构左右幅相连横向抗风分析

高墩大跨径连续刚构桥梁主墩横向抗风设计问题突出,多以增大主墩底部尺寸来解决,但变截面高墩容易造成施工不便和影响美观。对分幅桥梁,笔者认为可将左右幅0#块用外伸横隔板相连,双幅桥梁的主墩共同承担横向风荷载。以龙门大桥为例,用Midas建立单幅桥梁和双幅相连桥梁的两个模型,比较各自的内力和稳定性,再应用ANSYS对受力复杂的0#块及其外伸隔板作三维实体仿真分析。结果表明,双幅桥梁相连后可有效减少墩底内力,提高整体稳定性。     

盾构下坡掘进对推进阻力的影响研究

以神华新街6°煤矿斜井盾构工程为背景,针对盾构下坡掘进工况,通过建立盾构上作用的水土压力载荷与坡度的数学关系模型,推导出推进阻力与坡度的数学公式。结合工程实例,计算水平掘进与6°下坡掘进各推进阻力值,并利用MATLAB软件绘制下坡掘进时推进阻力随坡度变化的曲线。结果表明:1)6°下坡掘进的总推进阻力相对于水平掘进仅减小了约8%,坡度小于13°时,可通过水平掘进时的推进阻力减去重力沿掘进轴线上的分量来近似求得,且偏差小于10%;2)总推进阻力会随坡角的不断增大而减小;3)当坡度大于50.4°后,盾构有自动向下滑移的趋势,刀盘将自动压紧开挖面,导致刀具自动嵌入开挖面,增加了启动扭矩与换刀的难度。