锚贴U形钢板-混凝土组合加固RC梁的抗弯试验

为了研究锚贴U形钢板-混凝土组合加固钢筋混凝土梁的抗弯性能,设计5根加固梁和1根对比梁进行抗弯试验。试件的主要设计参数包括有无加载历史、钢板纵向加固长度、钢板厚度和螺杆间距。加载仪器采用1 000 kN梁柱加载系统,应变采集使用静态应变分析系统,挠度采用机电百分表测量。试验过程中,观测记录试验梁在荷载作用下截面应变、跨中挠度、加固部分与原混凝土之间的相对滑移、裂缝的产生与发展。基于平截面假定,推导试验梁的极限抗弯承载力计算公式,并对比模型试验与理论分析结果。试验结果表明：与未加固的对比梁相比,锚贴U形钢板-混凝土组合加固后的试验梁其开裂弯矩提高近50%,极限抗弯承载力提高约1倍;钢板纵向加固长度对梁的整体刚度有显著的影响,加固范围越大刚度提升越显著;加固范围应充分考虑加固部分截断处截面的抗剪能力,避免使试件从塑性弯曲破坏模式变成脆性剪切破坏模式;对比螺杆间距15 cm与30 cm试验梁的结果发现,只要符合构造要求的螺杆间距对试件的承载能力影响很小,但对裂缝开展有一定的影响,螺杆间距越密其裂缝开展明显变小;随着加固钢板面积增大,抗弯承载力也随之提高。针对加固后适筋破坏的RC梁,推导了极限抗弯承载力计算公式,利用公式计算出的极限抗弯承载力的理论值与试验值相对差值均在10%以内。     

粒料桩复合地基承载力发挥值试验研究

结合实体公路工程,通过现场试验段土压力监测,计算复合地基承载力发挥值;结合破坏模式,对其复合地基承载力发挥值进行分析。研究结果表明:粒料桩复合地基按照土体先破坏计算的复合地基承载力发挥值,接近现场载荷试验结果,复合地基承载力可通过桩墩间土压力进行计算确定;不论是强夯置换砾石墩还是振动砾石桩,其破坏形式是桩（墩）间土先破坏引起的整个复合地基的破坏。     

不同钢内衬加固钢筋混凝土管涵力学特性试验研究

为研究不同钢内衬加固钢筋混凝土管涵的加固效果及其力学特性,对不加固的钢筋混凝土管、10 mm厚平钢管内衬加固钢筋混凝土管、波纹钢管内衬加固钢筋混凝土管3个试件进行两点加载试验,测试P—Δ曲线及截面应变。试验结果表明:采用直接加固方式时,波纹钢管内衬加固钢筋混凝土管、平钢管内衬加固钢筋混凝土管的极限承载力分别比未加固圆管的极限承载力提高240%、22%;加固形成的钢筋混凝土—内填混凝土—内衬波纹钢管截面为部分组合截面;钢筋混凝土—内填混凝土—内衬平钢管截面接近非组合截面,其受力过程与未加固管截面受力过程相似。     

32 m超低高度混凝土T梁动力性能试验与分析

为了研究开行120km/h货车后既有铁路32m超低高度混凝土T梁的运营状况、安全性和加固对策,选取1孔32m超低高度混凝土T梁进行自振频率、挠度、振幅等主要动力性能测试。结果发现跨中横向振幅超过限值,不能满足120km/h货车运行,需要提高该梁的竖向和横向刚度。     

13t单级减速驱动桥总成疲劳试验研究

通过模拟车桥的工况及参考IV ECO16-5220车桥总成疲劳试验的方法,对车桥总成进行疲劳试验。确定施加不同扭矩值时主减速器和差速器等部件的强度极限,由此确定车桥总成的疲劳寿命,从而得出汽车车桥总成的安全系数,为车桥的设计、生产提供可靠的依据。     

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.     

基于GIS的港口防台应急辅助决策与指挥系统研究

台风防御是我国东南沿海各港口＂三防＂工作的重中之重。分析了目前我国港口防台工作的不足之处,提出了利用地理信息技术解决目前存在的问题,设计并开发了港口防台应急辅助决策与指挥系统。在软件整体架构设计、功能模块设计、数据设计等方面进行了研究,并介绍了＂厦门港防台应急指挥系统＂的应用情况。     

A new composite decision support framework for strategic and sustainable transport appraisals

This paper concerns the development of a new decision support framework for the appraisal of transport infrastructure projects. In such appraisals there will often be a need for including both conventional transport impacts as well as criteria of a more strategic and/or sustainable character. The proposed framework is based on the use of cost-benefit analysis featuring feasibility risk assessment in combination with multi-criteria decision analysis and is supported by the concept of decision conferencing. The framework is applied for a transport related case study dealing with the complex decision problem of determining the most attractive alternative for a new fixed link between Denmark and Sweden – the so-called HH-connection. Applying the framework to the case study made it possible to address the decision problem from an economic, a strategic, and a sustainable point of view simultaneously. The outcome of the case study demonstrates the decision making framework as a valuable decision support system (DSS), and it is concluded that appraisals of transport projects can be effectively supported by the use of the DSS. Finally, perspectives of the future modelling work are given.     

Does crowding affect the path choice of metro passengers?

This paper investigates crowding effect on the path choice of metro passengers. We show people reroute not only to avoid the delay from crowding but also to evade crowding itself. More specifically, a logit model fits best when it uses the transit delay from crowding as well as the passenger load of a connection in addition to the conventional explanatory variables. Also, we demonstrate that crowding decreases the overall welfare of metro passengers. The model is tested on the real path choice data acquired by the recent algorithm by Hong et al. (2015) known to detect the real path choice from Smart Card data in more than 90% of the cases.